diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index c6f6fda2..c94225a6 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -67,8 +67,8 @@ members of the project's leadership. ## Attribution -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, -available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.1, +available at https://www.contributor-covenant.org/version/2/1/code_of_conduct [homepage]: https://www.contributor-covenant.org diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 51efab83..535231d7 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -19,6 +19,8 @@ Welcome to Project MONAI Deploy App SDK! We're excited you're here and want to contribute. This documentation is intended for individuals and institutions interested in contributing to MONAI Deploy App SDK. MONAI Deploy App SDK is an open-source project and, as such, its success relies on its community of contributors willing to keep improving it. Your contribution will be a valued addition to the code base; we simply ask that you read this page and understand our contribution process, whether you are a seasoned open-source contributor or whether you are a first-time contributor. +Please also refer to [MONAI Contributing Guide](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md) for general information as well as MONAI Core specifics. + ### Communicate with us We are happy to talk with you about your needs for MONAI Deploy App SDK and your ideas for contributing to the project. One way to do this is to create an issue discussing your thoughts. It might be that a very similar feature is under development or already exists, so an issue is a great starting point. If you are looking for an issue to resolve that will help Project MONAI Deploy App SDK, see the [*good first issue*](https://github.com/Project-MONAI/monai-deploy-app-sdk/labels/good%20first%20issue) and [*Contribution wanted*](https://github.com/Project-MONAI/monai-deploy-app-sdk/labels/Contribution%20wanted) labels. @@ -74,7 +76,7 @@ Before submitting a pull request, we recommend that all linting should pass, by License information: all source code files should start with this paragraph: ```python -# Copyright 2021 MONAI Consortium +# Copyright 2021-2024 MONAI Consortium # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -135,6 +137,10 @@ MONAI Deploy App SDK's code coverage report is available at [CodeCov](https://co #### Building the documentation +:::{note} +Please note that the documentation builds successfully in Python 3.8 environment, but fails with Python 3.10. +::: + MONAI's documentation is located at `docs/`. ```bash @@ -241,21 +247,22 @@ For string definition, [f-string](https://www.python.org/dev/peps/pep-0498/) is ### Submitting pull requests -TBD +Please see this [general guidance](https://github.com/gabrieldemarmiesse/getting_started_open_source) ## The code reviewing process -TBD +Please see this [general guidance](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/about-pull-request-reviews) ### Reviewing pull requests -TBD +At least one contributor of the project needs to approve a pull request. ## Admin tasks -TBD +The contributors with Admin role in the project handle admin tasks. ### Release a new version -[github ci]: https://github.com/Project-MONAI/monai-deploy-app-sdk/actions -[monai-deploy-app-sdk issue list]: https://github.com/Project-MONAI/monai-deploy-app-sdk/issues +[github ci](https://github.com/Project-MONAI/monai-deploy-app-sdk/actions) + +[monai-deploy-app-sdk issue list](https://github.com/Project-MONAI/monai-deploy-app-sdk/issues) diff --git a/README.md b/README.md index 3c11e5d1..63803328 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ MONAI Deploy App SDK offers a framework and associated tools to design, develop ## User Guide -User guide is available at [docs.monai.io](https://docs.monai.io/projects/monai-deploy-app-sdk/en/latest/). +User guide is available at [docs.monai.io](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/). ## Installation @@ -32,9 +32,15 @@ To install [the current release](https://pypi.org/project/monai-deploy-app-sdk/) pip install monai-deploy-app-sdk # '--pre' to install a pre-release version. ``` +Please also note the following system requirements: +- Ubuntu 22.04 on X86-64 is required, as this is the only X86 platform that the underlying Holoscan SDK has been tested to support as of now. +- [CUDA 12](https://developer.nvidia.com/cuda-12-0-0-download-archive) is required along with a supported NVIDIA GPU with at least 8GB of video RAM. If AI inference is not used in the example application and a GPU is not installed, at least [CUDA 12 runtime](https://pypi.org/project/nvidia-cuda-runtime-cu12/) is required, as this is one of the requirements of Holoscan SDK, in addition, the `LIB_LIBRARY_PATH` must be set to include the installed shared library, e.g. in a Python 3.8 env, ```export LD_LIBRARY_PATH=`pwd`/.venv/lib/python3.8/site-packages/nvidia/cuda_runtime/lib:$LD_LIBRARY_PATH``` + + + ## Getting Started -Getting started guide is available at [here](https://docs.monai.io/projects/monai-deploy-app-sdk/en/latest/getting_started/index.html). +Getting started guide is available at [here](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/index.html). ```bash pip install monai-deploy-app-sdk # '--pre' to install a pre-release version. @@ -44,7 +50,7 @@ git clone https://github.com/Project-MONAI/monai-deploy-app-sdk.git cd monai-deploy-app-sdk # Install necessary dependencies for simple_imaging_app -pip install scikit-image +pip install matplotlib Pillow scikit-image # Execute the app locally python examples/apps/simple_imaging_app/app.py -i examples/apps/simple_imaging_app/brain_mr_input.jpg -o output diff --git a/THIRD_PARTY_NOTICES/holoscan_Apache2.0_LICENSE.txt b/THIRD_PARTY_NOTICES/holoscan_Apache2.0_LICENSE.txt new file mode 100644 index 00000000..d6456956 --- /dev/null +++ b/THIRD_PARTY_NOTICES/holoscan_Apache2.0_LICENSE.txt @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/requirements.txt b/docs/requirements.txt index 56a20bb0..d0675536 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -36,4 +36,5 @@ sphinxcontrib-htmlhelp==2.0.0 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==1.0.3 sphinxcontrib-serializinghtml==1.1.5 -sphinxcontrib-mermaid==0.7.1 \ No newline at end of file +sphinxcontrib-mermaid==0.7.1 +lxml_html_clean \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 77191b41..18938a78 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -41,7 +41,7 @@ # -- Project information ----------------------------------------------------- project = "MONAI Deploy App SDK" -copyright = "2021 MONAI Consortium" +copyright = "2021-2024 MONAI Consortium" author = "MONAI Contributors" # The full version, including alpha/beta/rc tags diff --git a/docs/source/getting_started/installing_app_sdk.md b/docs/source/getting_started/installing_app_sdk.md index 3d475d2b..635f2edd 100644 --- a/docs/source/getting_started/installing_app_sdk.md +++ b/docs/source/getting_started/installing_app_sdk.md @@ -15,16 +15,14 @@ pip install --upgrade monai-deploy-app-sdk ``` :::{note} -For packaging your application, [MONAI Application Packager](/developing_with_sdk/packaging_app) and [MONAI Application Runner (MAR)](/developing_with_sdk/executing_packaged_app_locally) requires NVIDIA Docker (NVIDIA Container Toolkit) installed: +For packaging and running your application using [MONAI Application Packager](/developing_with_sdk/packaging_app) and [MONAI Application Runner (MAR)](/developing_with_sdk/executing_packaged_app_locally), [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) needs to be installed. - +For version 1.0.0, `nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu` is used by [MONAI Application Packager](/developing_with_sdk/packaging_app) as base image for X86-64 in Linux system, though this will change with versions. -Currently, `nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu` base Docker image is used by [MONAI Application Packager](/developing_with_sdk/packaging_app) by default for X86-64 in Linux system. - -The base image size is large so please pull the image in advance to save time. Note that the container image tag in the following example, e.g. v0.6.0, corresponds to the SDK version. +The base image size is large, so it is recommended to pull the image in advance to save time. Note that the container image tag in the following example, e.g. `v1.0.3`, corresponds to the underlying Holoscan SDK version. ```bash -docker pull nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu +docker pull nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu ``` ::: diff --git a/docs/source/getting_started/tutorials/mednist_app.md b/docs/source/getting_started/tutorials/mednist_app.md index 6389da82..7ad98c1e 100644 --- a/docs/source/getting_started/tutorials/mednist_app.md +++ b/docs/source/getting_started/tutorials/mednist_app.md @@ -7,7 +7,7 @@ This tutorial demos the process of packaging up a trained model using MONAI Depl ```bash # Create a virtual environment with Python 3.8. # Skip if you are already in a virtual environment. -conda create -n mednist python=3.8 pytorch jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge +conda create -n mednist python=3.8 pytorch jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge conda activate mednist # Launch JupyterLab if you want to work on Jupyter Notebook @@ -41,6 +41,8 @@ jupyter-lab ```{raw} html
+ +

Video may show the use of previous SDK verson.

``` diff --git a/docs/source/getting_started/tutorials/monai_bundle_app.md b/docs/source/getting_started/tutorials/monai_bundle_app.md index 9a05c34e..e93c65e4 100644 --- a/docs/source/getting_started/tutorials/monai_bundle_app.md +++ b/docs/source/getting_started/tutorials/monai_bundle_app.md @@ -7,7 +7,7 @@ This tutorial shows how to create an organ segmentation application for a PyTorc ```bash # Create a virtual environment with Python 3.8. # Skip if you are already in a virtual environment. -conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge +conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge conda activate monai # Launch JupyterLab if you want to work on Jupyter Notebook @@ -31,6 +31,8 @@ jupyter-lab ```{raw} html
+ +

Video may show the use of previous SDK verson.

``` diff --git a/docs/source/getting_started/tutorials/multi_model_app.md b/docs/source/getting_started/tutorials/multi_model_app.md index d91629cc..c197c88e 100644 --- a/docs/source/getting_started/tutorials/multi_model_app.md +++ b/docs/source/getting_started/tutorials/multi_model_app.md @@ -9,7 +9,7 @@ The models used in this example are trained with MONAI, and are packaged in the ```bash # Create a virtual environment with Python 3.8. # Skip if you are already in a virtual environment. -conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge +conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge conda activate monai # Launch JupyterLab if you want to work on Jupyter Notebook diff --git a/docs/source/getting_started/tutorials/segmentation_app.md b/docs/source/getting_started/tutorials/segmentation_app.md index d19147d7..3ef9e55b 100644 --- a/docs/source/getting_started/tutorials/segmentation_app.md +++ b/docs/source/getting_started/tutorials/segmentation_app.md @@ -9,7 +9,7 @@ Please note that the following steps are for demonstration purpose. The code pul ```bash # Create a virtual environment with Python 3.8. # Skip if you are already in a virtual environment. -conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge +conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge conda activate monai # Launch JupyterLab if you want to work on Jupyter Notebook diff --git a/docs/source/getting_started/tutorials/segmentation_clara-viz_app.md b/docs/source/getting_started/tutorials/segmentation_clara-viz_app.md index 3058d0df..1ce87b5a 100644 --- a/docs/source/getting_started/tutorials/segmentation_clara-viz_app.md +++ b/docs/source/getting_started/tutorials/segmentation_clara-viz_app.md @@ -7,7 +7,7 @@ This tutorial shows how to create an organ segmentation application for a PyTorc ```bash # Create a virtual environment with Python 3.8. # Skip if you are already in a virtual environment. -conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge +conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge conda activate monai # Launch JupyterLab if you want to work on Jupyter Notebook diff --git a/docs/source/getting_started/tutorials/simple_app.md b/docs/source/getting_started/tutorials/simple_app.md index 78a62242..3eb0328d 100644 --- a/docs/source/getting_started/tutorials/simple_app.md +++ b/docs/source/getting_started/tutorials/simple_app.md @@ -7,7 +7,7 @@ This tutorial shows how a simple image processing application can be created wit ```bash # Create a virtual environment with Python 3.8. # Skip if you are already in a virtual environment. -conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge +conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge conda activate monai # Launch JupyterLab if you want to work on Jupyter Notebook @@ -48,8 +48,7 @@ cd monai-deploy-app-sdk pip install monai-deploy-app-sdk # Install necessary packages from the app. Can simply run `pip install -r examples/apps/simple_imaging_app/requirements.txt` -pip install scikit-image -pip install setuptools +pip install scikit-image, setuptools, Pillow, matplotlib # See the input file exists in the default `input`` folder in the current working directory ls examples/apps/simple_imaging_app/input/ diff --git a/docs/source/introduction/roadmap.md b/docs/source/introduction/roadmap.md index bde0bf6b..fcba80ca 100644 --- a/docs/source/introduction/roadmap.md +++ b/docs/source/introduction/roadmap.md @@ -2,4 +2,4 @@ The first versions of the MONAI Deploy App SDK offer a core framework to build & package healthcare AI apps so that they can be deployed to a production environment. -We are currently in the process of refining the roadmap for the product based on the community’s input, though it is clear that on the roadmap are more built-in DICOM parsing and DICOM OID generation capabilities to better support [IHE AIR profiles](https://www.ihe.net/uploadedFiles/Documents/Radiology/IHE_RAD_Suppl_AIR_Rev1-2_TI_2022-07-06.pdf), as well as serving model network in a separate process using [Triton](https://developer.nvidia.com/triton-inference-server). +We are currently in the process of refining the roadmap for the product based on the community’s input, though it is clear that on the roadmap are more built-in DICOM parsing and DICOM OID generation capabilities to better support [IHE AIR profiles](https://www.ihe.net/uploadedFiles/Documents/Radiology/IHE_RAD_Suppl_AIR_Rev1-2_TI_2022-07-06.pdf), as well as serving model network in a separate process using [Triton](https://developer.nvidia.com/triton-inference-server) and potentially utilizing [Nvidia Inference Microservices](https://www.nvidia.com/en-us/ai/). diff --git a/docs/source/release_notes/index.md b/docs/source/release_notes/index.md index 0b81d4d3..16b62fed 100644 --- a/docs/source/release_notes/index.md +++ b/docs/source/release_notes/index.md @@ -4,6 +4,13 @@ :hidden: :maxdepth: 2 +``` +## Version 1.0 + +```{toctree} +:maxdepth: 1 + +v1.0.0 ``` ## Version 0.6 diff --git a/docs/source/release_notes/v1.0.0.md b/docs/source/release_notes/v1.0.0.md new file mode 100644 index 00000000..d38fddf8 --- /dev/null +++ b/docs/source/release_notes/v1.0.0.md @@ -0,0 +1,35 @@ +# Version 1.0.0 (April 2024) + +## What's new in 1.0.0 +App SDK has been migrated to be dependent on [NVIDIA Holoscan SDK](https://developer.nvidia.com/holoscan-sdk) since Version [v0.6](https://github.com/nvidia-holoscan/holoscan-sdk/releases) when breaking changes were introduced in some core class APIs. This version is a simple update of the App SDK to make use of the newly released Holoscan SDK v1.0.3, as well as bug fixes of a few known issues. + +### Key changes and migration guide + +- [CUDA 12](https://developer.nvidia.com/cuda-12-0-0-download-archive) is required along with a supported NVIDIA GPU with at least 8GB of video RAM. If AI inference is not used in the example application and a GPU is not installed, at least [CUDA 12 runtime](https://pypi.org/project/nvidia-cuda-runtime-cu12/) is required, as this is one of the requirements of Holoscan SDK, in addition, the `LIB_LIBRARY_PATH` must be set to include the installed shared library, e.g. in a Python 3.8 env, ```export LD_LIBRARY_PATH=`pwd`/.venv/lib/python3.8/site-packages/nvidia/cuda_runtime/lib:$LD_LIBRARY_PATH``` +- Ubuntu 22.04 on X86-64 is required, similarly required by Holoscan SDK +- The following is repeated from the V0.6 release note, for readers' convenience. + - In App SDK core module, `monai.deploy.core`, instead of through a wrapper layer, Holoscan SDK core sub modules are all directly imported and exposed under`monai.deploy.core`, mixed in with the ones original to the App SDK. The same also applies to those modules, e.g., `conditions`, `executors`, `graphs`, `logger`, and `resources`. As such, the [Modudle API documentation](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/modules/index.html) may show a mixture of `monai.deploy` and `holoscan`. + + - For `monai.deploy.operators`, Holoscan SDK built-in operators are selectively imported and exposed, with the main reason being to avoid introducing extra dependencies on system packages. All of the original and existing App SDK built-in operators are still present and migrated to be based on Holoscan SDK base `operator` class. + + - Python decorator support for `Application` and `Operator` class is absent in this release, so alternative approaches must be used + - `Operator` input(s) and output(s) now must be defined in the `setup()` method of this class + - `Application` and `Operator` cannot decorate or serve out the resource and package dependencies, which are required when packaging the application into MONAI Application Package. So the application developer must now provide the Python package requirement file and application configuration file when creating a MAP + + - Derived `Operator` class must first assign its attributes before calling the constructor of the base `Operator`. + + - `Application`'s `run()` method can no longer pass the file I/O paths, e.g. `input`, `output`, and `models`, to the execution context of each operator when its `compute()` method is called. For operators depending on them, the I/O paths need to be passed in as arguments in the constructor. + + - App SDK CLI, `monai-deploy`, no longer support `exec` sub-command. However, when directly running the application with Python, command line options for `--input`, `--output`, and `--model`, are supported if the application make use of the `Application`'s class method, `init_app_context(argv)`. + + - App SDK CLI packaging command, `monai-deploy package`, requires additional command line parameters, namely, application configuration yaml file, Python requirements file, and the platform configuration (as it supports both x86-64 and ARMv8 AArch64 targets). Details can be found in the tutorials and Users Guide. + + +Please also see the closed issues on Github and the closed pull requests on Github. + +## Additional information +Please visit [GETTING STARTED](/getting_started/index) guide and follow the tutorials. + +You can learn more about SDK usage through [DEVELOPING WITH SDK](/developing_with_sdk/index). + +Please let us know how you like it and what could be improved by [submitting an issue](https://github.com/Project-MONAI/monai-deploy-app-sdk/issues/new/choose) or [asking questions](https://github.com/Project-MONAI/monai-deploy-app-sdk/discussions) \ No newline at end of file diff --git a/monai/deploy/conditions/__init__.py b/monai/deploy/conditions/__init__.py index cc6d84ba..8dcad829 100644 --- a/monai/deploy/conditions/__init__.py +++ b/monai/deploy/conditions/__init__.py @@ -8,6 +8,7 @@ MessageAvailableCondition PeriodicCondition """ + # Need to import explicit ones to quiet mypy complaints from holoscan.conditions import * from holoscan.conditions import CountCondition diff --git a/notebooks/tutorials/01_simple_app.ipynb b/notebooks/tutorials/01_simple_app.ipynb index b343563a..a1f10f59 100644 --- a/notebooks/tutorials/01_simple_app.ipynb +++ b/notebooks/tutorials/01_simple_app.ipynb @@ -95,7 +95,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -104,7 +104,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -514,8 +514,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-08-30 00:09:09,515] [INFO] (root) - Parsed args: Namespace(argv=[], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-08-30 00:09:09,523] [INFO] (root) - AppContext object: AppContext(input_path=/tmp/simple_app/normal-brain-mri-4.png, output_path=output, model_path=models, workdir=)\n" + "[2024-04-10 16:18:44,711] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])\n", + "[2024-04-10 16:18:44,717] [INFO] (root) - AppContext object: AppContext(input_path=/tmp/simple_app/normal-brain-mri-4.png, output_path=output, model_path=models, workdir=)\n" ] }, { @@ -531,12 +531,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[info] [gxf_executor.cpp:210] Creating context\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[info] [gxf_executor.cpp:211] Creating context\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[info] [greedy_scheduler.cpp:190] Scheduling 3 entities\n" ] }, @@ -556,10 +556,10 @@ "text": [ "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[info] [gxf_executor.cpp:229] Destroying context\n" + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[info] [gxf_executor.cpp:230] Destroying context\n" ] } ], @@ -593,7 +593,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -602,7 +602,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1059,87 +1059,87 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 00:09:17,221] [INFO] (root) - Parsed args: Namespace(argv=['simple_imaging_app', '-i', '/tmp/simple_app', '-o', 'output', '-l', 'DEBUG'], input=PosixPath('/tmp/simple_app'), log_level='DEBUG', model=None, output=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output'), workdir=None)\n", - "[2023-08-30 00:09:17,223] [INFO] (root) - AppContext object: AppContext(input_path=/tmp/simple_app, output_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output, model_path=models, workdir=)\n", - "[2023-08-30 00:09:17,223] [INFO] (root) - sample_data_path: /tmp/simple_app\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:210] Creating context\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1741] Activating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1771] Running Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 16:18:49,954] [INFO] (root) - Parsed args: Namespace(log_level='DEBUG', input=PosixPath('/tmp/simple_app'), output=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output'), model=None, workdir=None, argv=['simple_imaging_app', '-i', '/tmp/simple_app', '-o', 'output', '-l', 'DEBUG'])\n", + "[2024-04-10 16:18:50,126] [INFO] (root) - AppContext object: AppContext(input_path=/tmp/simple_app, output_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output, model_path=models, workdir=)\n", + "[2024-04-10 16:18:50,127] [INFO] (root) - sample_data_path: /tmp/simple_app\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:211] Creating context\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1864] Activating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1894] Running Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:190] Scheduling 3 entities\n", "Number of times operator sobel_op whose class is defined in sobel_operator called: 1\n", "Input from: /tmp/simple_app, whose absolute path: /tmp/simple_app\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IHDR' 16 13\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'sRGB' 41 1\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'gAMA' 54 4\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'pHYs' 70 9\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IDAT' 91 65445\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IHDR' 16 13\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'sRGB' 41 1\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'gAMA' 54 4\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'pHYs' 70 9\n", - "[2023-08-30 00:09:17,302] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IDAT' 91 65445\n", - "[2023-08-30 00:09:17,308] [DEBUG] (PIL.Image) - Error closing: Operation on closed image\n", + "[2024-04-10 16:18:50,179] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IHDR' 16 13\n", + "[2024-04-10 16:18:50,179] [DEBUG] (PIL.PngImagePlugin) - STREAM b'sRGB' 41 1\n", + "[2024-04-10 16:18:50,179] [DEBUG] (PIL.PngImagePlugin) - STREAM b'gAMA' 54 4\n", + "[2024-04-10 16:18:50,179] [DEBUG] (PIL.PngImagePlugin) - STREAM b'pHYs' 70 9\n", + "[2024-04-10 16:18:50,179] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IDAT' 91 65445\n", + "[2024-04-10 16:18:50,179] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IHDR' 16 13\n", + "[2024-04-10 16:18:50,179] [DEBUG] (PIL.PngImagePlugin) - STREAM b'sRGB' 41 1\n", + "[2024-04-10 16:18:50,179] [DEBUG] (PIL.PngImagePlugin) - STREAM b'gAMA' 54 4\n", + "[2024-04-10 16:18:50,180] [DEBUG] (PIL.PngImagePlugin) - STREAM b'pHYs' 70 9\n", + "[2024-04-10 16:18:50,180] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IDAT' 91 65445\n", + "[2024-04-10 16:18:50,185] [DEBUG] (PIL.Image) - Error closing: Operation on closed image\n", "Number of times operator median_op whose class is defined in median_operator called: 1\n", "Number of times operator gaussian_op whose class is defined in gaussian_operator called: 1\n", "Data type of output: , max = 0.35821119421406195\n", "Data type of output post conversion: , max = 91\n", - "[2023-08-30 00:09:17,513] [DEBUG] (PIL.Image) - Importing BlpImagePlugin\n", - "[2023-08-30 00:09:17,514] [DEBUG] (PIL.Image) - Importing BmpImagePlugin\n", - "[2023-08-30 00:09:17,514] [DEBUG] (PIL.Image) - Importing BufrStubImagePlugin\n", - "[2023-08-30 00:09:17,514] [DEBUG] (PIL.Image) - Importing CurImagePlugin\n", - "[2023-08-30 00:09:17,515] [DEBUG] (PIL.Image) - Importing DcxImagePlugin\n", - "[2023-08-30 00:09:17,515] [DEBUG] (PIL.Image) - Importing DdsImagePlugin\n", - "[2023-08-30 00:09:17,515] [DEBUG] (PIL.Image) - Importing EpsImagePlugin\n", - "[2023-08-30 00:09:17,516] [DEBUG] (PIL.Image) - Importing FitsImagePlugin\n", - "[2023-08-30 00:09:17,516] [DEBUG] (PIL.Image) - Importing FliImagePlugin\n", - "[2023-08-30 00:09:17,516] [DEBUG] (PIL.Image) - Importing FpxImagePlugin\n", - "[2023-08-30 00:09:17,516] [DEBUG] (PIL.Image) - Image: failed to import FpxImagePlugin: No module named 'olefile'\n", - "[2023-08-30 00:09:17,516] [DEBUG] (PIL.Image) - Importing FtexImagePlugin\n", - "[2023-08-30 00:09:17,517] [DEBUG] (PIL.Image) - Importing GbrImagePlugin\n", - "[2023-08-30 00:09:17,517] [DEBUG] (PIL.Image) - Importing GifImagePlugin\n", - "[2023-08-30 00:09:17,517] [DEBUG] (PIL.Image) - Importing GribStubImagePlugin\n", - "[2023-08-30 00:09:17,517] [DEBUG] (PIL.Image) - Importing Hdf5StubImagePlugin\n", - "[2023-08-30 00:09:17,517] [DEBUG] (PIL.Image) - Importing IcnsImagePlugin\n", - "[2023-08-30 00:09:17,518] [DEBUG] (PIL.Image) - Importing IcoImagePlugin\n", - "[2023-08-30 00:09:17,518] [DEBUG] (PIL.Image) - Importing ImImagePlugin\n", - "[2023-08-30 00:09:17,519] [DEBUG] (PIL.Image) - Importing ImtImagePlugin\n", - "[2023-08-30 00:09:17,519] [DEBUG] (PIL.Image) - Importing IptcImagePlugin\n", - "[2023-08-30 00:09:17,519] [DEBUG] (PIL.Image) - Importing JpegImagePlugin\n", - "[2023-08-30 00:09:17,519] [DEBUG] (PIL.Image) - Importing Jpeg2KImagePlugin\n", - "[2023-08-30 00:09:17,519] [DEBUG] (PIL.Image) - Importing McIdasImagePlugin\n", - "[2023-08-30 00:09:17,520] [DEBUG] (PIL.Image) - Importing MicImagePlugin\n", - "[2023-08-30 00:09:17,520] [DEBUG] (PIL.Image) - Image: failed to import MicImagePlugin: No module named 'olefile'\n", - "[2023-08-30 00:09:17,520] [DEBUG] (PIL.Image) - Importing MpegImagePlugin\n", - "[2023-08-30 00:09:17,520] [DEBUG] (PIL.Image) - Importing MpoImagePlugin\n", - "[2023-08-30 00:09:17,522] [DEBUG] (PIL.Image) - Importing MspImagePlugin\n", - "[2023-08-30 00:09:17,522] [DEBUG] (PIL.Image) - Importing PalmImagePlugin\n", - "[2023-08-30 00:09:17,523] [DEBUG] (PIL.Image) - Importing PcdImagePlugin\n", - "[2023-08-30 00:09:17,523] [DEBUG] (PIL.Image) - Importing PcxImagePlugin\n", - "[2023-08-30 00:09:17,523] [DEBUG] (PIL.Image) - Importing PdfImagePlugin\n", - "[2023-08-30 00:09:17,528] [DEBUG] (PIL.Image) - Importing PixarImagePlugin\n", - "[2023-08-30 00:09:17,528] [DEBUG] (PIL.Image) - Importing PngImagePlugin\n", - "[2023-08-30 00:09:17,528] [DEBUG] (PIL.Image) - Importing PpmImagePlugin\n", - "[2023-08-30 00:09:17,528] [DEBUG] (PIL.Image) - Importing PsdImagePlugin\n", - "[2023-08-30 00:09:17,528] [DEBUG] (PIL.Image) - Importing QoiImagePlugin\n", - "[2023-08-30 00:09:17,528] [DEBUG] (PIL.Image) - Importing SgiImagePlugin\n", - "[2023-08-30 00:09:17,528] [DEBUG] (PIL.Image) - Importing SpiderImagePlugin\n", - "[2023-08-30 00:09:17,529] [DEBUG] (PIL.Image) - Importing SunImagePlugin\n", - "[2023-08-30 00:09:17,529] [DEBUG] (PIL.Image) - Importing TgaImagePlugin\n", - "[2023-08-30 00:09:17,529] [DEBUG] (PIL.Image) - Importing TiffImagePlugin\n", - "[2023-08-30 00:09:17,529] [DEBUG] (PIL.Image) - Importing WebPImagePlugin\n", - "[2023-08-30 00:09:17,530] [DEBUG] (PIL.Image) - Importing WmfImagePlugin\n", - "[2023-08-30 00:09:17,530] [DEBUG] (PIL.Image) - Importing XbmImagePlugin\n", - "[2023-08-30 00:09:17,531] [DEBUG] (PIL.Image) - Importing XpmImagePlugin\n", - "[2023-08-30 00:09:17,531] [DEBUG] (PIL.Image) - Importing XVThumbImagePlugin\n", + "[2024-04-10 16:18:50,402] [DEBUG] (PIL.Image) - Importing BlpImagePlugin\n", + "[2024-04-10 16:18:50,403] [DEBUG] (PIL.Image) - Importing BmpImagePlugin\n", + "[2024-04-10 16:18:50,404] [DEBUG] (PIL.Image) - Importing BufrStubImagePlugin\n", + "[2024-04-10 16:18:50,404] [DEBUG] (PIL.Image) - Importing CurImagePlugin\n", + "[2024-04-10 16:18:50,404] [DEBUG] (PIL.Image) - Importing DcxImagePlugin\n", + "[2024-04-10 16:18:50,404] [DEBUG] (PIL.Image) - Importing DdsImagePlugin\n", + "[2024-04-10 16:18:50,407] [DEBUG] (PIL.Image) - Importing EpsImagePlugin\n", + "[2024-04-10 16:18:50,408] [DEBUG] (PIL.Image) - Importing FitsImagePlugin\n", + "[2024-04-10 16:18:50,408] [DEBUG] (PIL.Image) - Importing FliImagePlugin\n", + "[2024-04-10 16:18:50,408] [DEBUG] (PIL.Image) - Importing FpxImagePlugin\n", + "[2024-04-10 16:18:50,408] [DEBUG] (PIL.Image) - Image: failed to import FpxImagePlugin: No module named 'olefile'\n", + "[2024-04-10 16:18:50,408] [DEBUG] (PIL.Image) - Importing FtexImagePlugin\n", + "[2024-04-10 16:18:50,409] [DEBUG] (PIL.Image) - Importing GbrImagePlugin\n", + "[2024-04-10 16:18:50,409] [DEBUG] (PIL.Image) - Importing GifImagePlugin\n", + "[2024-04-10 16:18:50,409] [DEBUG] (PIL.Image) - Importing GribStubImagePlugin\n", + "[2024-04-10 16:18:50,409] [DEBUG] (PIL.Image) - Importing Hdf5StubImagePlugin\n", + "[2024-04-10 16:18:50,409] [DEBUG] (PIL.Image) - Importing IcnsImagePlugin\n", + "[2024-04-10 16:18:50,411] [DEBUG] (PIL.Image) - Importing IcoImagePlugin\n", + "[2024-04-10 16:18:50,411] [DEBUG] (PIL.Image) - Importing ImImagePlugin\n", + "[2024-04-10 16:18:50,411] [DEBUG] (PIL.Image) - Importing ImtImagePlugin\n", + "[2024-04-10 16:18:50,412] [DEBUG] (PIL.Image) - Importing IptcImagePlugin\n", + "[2024-04-10 16:18:50,412] [DEBUG] (PIL.Image) - Importing JpegImagePlugin\n", + "[2024-04-10 16:18:50,412] [DEBUG] (PIL.Image) - Importing Jpeg2KImagePlugin\n", + "[2024-04-10 16:18:50,412] [DEBUG] (PIL.Image) - Importing McIdasImagePlugin\n", + "[2024-04-10 16:18:50,412] [DEBUG] (PIL.Image) - Importing MicImagePlugin\n", + "[2024-04-10 16:18:50,412] [DEBUG] (PIL.Image) - Image: failed to import MicImagePlugin: No module named 'olefile'\n", + "[2024-04-10 16:18:50,413] [DEBUG] (PIL.Image) - Importing MpegImagePlugin\n", + "[2024-04-10 16:18:50,413] [DEBUG] (PIL.Image) - Importing MpoImagePlugin\n", + "[2024-04-10 16:18:50,414] [DEBUG] (PIL.Image) - Importing MspImagePlugin\n", + "[2024-04-10 16:18:50,414] [DEBUG] (PIL.Image) - Importing PalmImagePlugin\n", + "[2024-04-10 16:18:50,415] [DEBUG] (PIL.Image) - Importing PcdImagePlugin\n", + "[2024-04-10 16:18:50,415] [DEBUG] (PIL.Image) - Importing PcxImagePlugin\n", + "[2024-04-10 16:18:50,415] [DEBUG] (PIL.Image) - Importing PdfImagePlugin\n", + "[2024-04-10 16:18:50,419] [DEBUG] (PIL.Image) - Importing PixarImagePlugin\n", + "[2024-04-10 16:18:50,420] [DEBUG] (PIL.Image) - Importing PngImagePlugin\n", + "[2024-04-10 16:18:50,420] [DEBUG] (PIL.Image) - Importing PpmImagePlugin\n", + "[2024-04-10 16:18:50,420] [DEBUG] (PIL.Image) - Importing PsdImagePlugin\n", + "[2024-04-10 16:18:50,420] [DEBUG] (PIL.Image) - Importing QoiImagePlugin\n", + "[2024-04-10 16:18:50,420] [DEBUG] (PIL.Image) - Importing SgiImagePlugin\n", + "[2024-04-10 16:18:50,420] [DEBUG] (PIL.Image) - Importing SpiderImagePlugin\n", + "[2024-04-10 16:18:50,421] [DEBUG] (PIL.Image) - Importing SunImagePlugin\n", + "[2024-04-10 16:18:50,421] [DEBUG] (PIL.Image) - Importing TgaImagePlugin\n", + "[2024-04-10 16:18:50,421] [DEBUG] (PIL.Image) - Importing TiffImagePlugin\n", + "[2024-04-10 16:18:50,421] [DEBUG] (PIL.Image) - Importing WebPImagePlugin\n", + "[2024-04-10 16:18:50,422] [DEBUG] (PIL.Image) - Importing WmfImagePlugin\n", + "[2024-04-10 16:18:50,422] [DEBUG] (PIL.Image) - Importing XbmImagePlugin\n", + "[2024-04-10 16:18:50,423] [DEBUG] (PIL.Image) - Importing XpmImagePlugin\n", + "[2024-04-10 16:18:50,423] [DEBUG] (PIL.Image) - Importing XVThumbImagePlugin\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:229] Destroying context\n" + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:230] Destroying context\n" ] } ], @@ -1156,7 +1156,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 20, @@ -1165,7 +1165,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1263,12 +1263,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 00:09:20,073] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app\n", - "[2023-08-30 00:09:20,073] [INFO] (packager.parameters) - Detected application type: Python Module\n", - "[2023-08-30 00:09:20,074] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app/app.yaml...\n", - "[2023-08-30 00:09:20,075] [INFO] (packager) - Generating app.json...\n", - "[2023-08-30 00:09:20,075] [INFO] (packager) - Generating pkg.json...\n", - "[2023-08-30 00:09:20,076] [DEBUG] (common) - \n", + "[2024-04-10 16:18:52,341] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app\n", + "[2024-04-10 16:18:52,341] [INFO] (packager.parameters) - Detected application type: Python Module\n", + "[2024-04-10 16:18:52,341] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app/app.yaml...\n", + "[2024-04-10 16:18:52,343] [INFO] (packager) - Generating app.json...\n", + "[2024-04-10 16:18:52,343] [INFO] (packager) - Generating pkg.json...\n", + "[2024-04-10 16:18:52,348] [DEBUG] (common) - \n", "=============== Begin app.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", @@ -1296,14 +1296,14 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1.0,\n", " \"workingDirectory\": \"/var/holoscan\"\n", "}\n", "================ End app.json ================\n", " \n", - "[2023-08-30 00:09:20,076] [DEBUG] (common) - \n", + "[2024-04-10 16:18:52,348] [DEBUG] (common) - \n", "=============== Begin pkg.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", @@ -1316,15 +1316,16 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"1Gi\"\n", " },\n", - " \"version\": 1.0\n", + " \"version\": 1.0,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "================ End pkg.json ================\n", " \n", - "[2023-08-30 00:09:20,088] [DEBUG] (packager.builder) - \n", + "[2024-04-10 16:18:52,364] [DEBUG] (packager.builder) - \n", "========== Begin Dockerfile ==========\n", "\n", "\n", - "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", + "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", "\n", "ENV DEBIAN_FRONTEND=noninteractive\n", "ENV TERM=xterm-256color\n", @@ -1340,11 +1341,13 @@ " && mkdir -p /var/holoscan/input \\\n", " && mkdir -p /var/holoscan/output\n", "\n", - "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\"\n", + "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\"\n", "LABEL tag=\"simple_imaging_app:1.0\"\n", "LABEL org.opencontainers.image.title=\"MONAI Deploy App Package - Simple Imaging App\"\n", "LABEL org.opencontainers.image.version=\"1.0\"\n", - "LABEL org.nvidia.holoscan=\"0.6.0\"\n", + "LABEL org.nvidia.holoscan=\"1.0.3\"\n", + "LABEL org.monai.deploy.app-sdk=\"0.5.1\"\n", + "\n", "\n", "ENV HOLOSCAN_ENABLE_HEALTH_CHECK=true\n", "ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input\n", @@ -1369,7 +1372,7 @@ "\n", "\n", "\n", - "RUN groupadd -g $GID $UNAME\n", + "RUN groupadd -f -g $GID $UNAME\n", "RUN useradd -rm -d /home/$UNAME -s /bin/bash -g $GID -G sudo -u $UID $UNAME\n", "RUN chown -R holoscan /var/holoscan \n", "RUN chown -R holoscan /var/holoscan/input \n", @@ -1394,13 +1397,12 @@ "RUN pip install --upgrade pip\n", "RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "\n", - "# Install Holoscan from PyPI org\n", - "RUN pip install holoscan==0.6.0\n", - "\n", + "# Install Holoscan from PyPI only when sdk_type is Holoscan. \n", + "# For MONAI Deploy, the APP SDK will install it unless user specifies the Holoscan SDK file.\n", "\n", "# Copy user-specified MONAI Deploy SDK file\n", - "COPY ./monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", - "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + "COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", "\n", "\n", "\n", @@ -1415,202 +1417,270 @@ "ENTRYPOINT [\"/var/holoscan/tools\"]\n", "=========== End Dockerfile ===========\n", "\n", - "[2023-08-30 00:09:20,088] [INFO] (packager.builder) - \n", + "[2024-04-10 16:18:52,365] [INFO] (packager.builder) - \n", "===============================================================================\n", "Building image for: x64-workstation\n", " Architecture: linux/amd64\n", - " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - " Build Image: N/A \n", + " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + " Build Image: N/A\n", " Cache: Enabled\n", " Configuration: dgpu\n", - " Holoiscan SDK Package: pypi.org\n", - " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + " Holoscan SDK Package: pypi.org\n", + " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", " gRPC Health Probe: N/A\n", - " SDK Version: 0.6.0\n", + " SDK Version: 1.0.3\n", " SDK: monai-deploy\n", " Tag: simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0\n", " \n", - "[2023-08-30 00:09:20,333] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", - "[2023-08-30 00:09:20,333] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0\n", - "#1 [internal] load .dockerignore\n", - "#1 transferring context: 33B\n", - "#1 transferring context: 1.79kB done\n", - "#1 DONE 0.1s\n", + "[2024-04-10 16:18:52,650] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", + "[2024-04-10 16:18:52,650] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0\n", + "#0 building with \"holoscan_app_builder\" instance using docker-container driver\n", + "\n", + "#1 [internal] load build definition from Dockerfile\n", + "#1 transferring dockerfile: 2.77kB done\n", + "#1 DONE 0.0s\n", "\n", - "#2 [internal] load build definition from Dockerfile\n", - "#2 transferring dockerfile: 2.64kB done\n", - "#2 DONE 0.1s\n", + "#2 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#2 DONE 0.4s\n", "\n", - "#3 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#3 DONE 0.4s\n", + "#3 [internal] load .dockerignore\n", + "#3 transferring context: 1.79kB done\n", + "#3 DONE 0.0s\n", "\n", "#4 [internal] load build context\n", "#4 DONE 0.0s\n", "\n", - "#5 importing cache manifest from local:10108727038215150215\n", + "#5 importing cache manifest from local:14270000836931083776\n", + "#5 inferred cache manifest type: application/vnd.oci.image.index.v1+json done\n", "#5 DONE 0.0s\n", "\n", - "#6 [ 1/21] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc\n", - "#6 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc 0.0s done\n", + "#6 [ 1/20] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155\n", + "#6 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155 0.0s done\n", "#6 DONE 0.0s\n", "\n", - "#7 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#7 DONE 0.9s\n", + "#7 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#7 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done\n", + "#7 DONE 0.4s\n", "\n", "#4 [internal] load build context\n", - "#4 transferring context: 163.39kB 0.0s done\n", - "#4 DONE 0.1s\n", + "#4 transferring context: 157.50kB 0.0s done\n", + "#4 DONE 0.0s\n", "\n", - "#8 [ 9/21] WORKDIR /var/holoscan\n", + "#8 [ 5/20] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", "#8 CACHED\n", "\n", - "#9 [13/21] RUN pip install --upgrade pip\n", + "#9 [11/20] RUN chmod +x /var/holoscan/tools\n", "#9 CACHED\n", "\n", - "#10 [ 4/21] RUN groupadd -g 1000 holoscan\n", + "#10 [ 2/20] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", "#10 CACHED\n", "\n", - "#11 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt\n", + "#11 [ 4/20] RUN groupadd -f -g 1000 holoscan\n", "#11 CACHED\n", "\n", - "#12 [ 6/21] RUN chown -R holoscan /var/holoscan\n", + "#12 [ 7/20] RUN chown -R holoscan /var/holoscan/input\n", "#12 CACHED\n", "\n", - "#13 [15/21] RUN pip install holoscan==0.6.0\n", + "#13 [ 8/20] RUN chown -R holoscan /var/holoscan/output\n", "#13 CACHED\n", "\n", - "#14 [16/21] COPY ./monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + "#14 [ 6/20] RUN chown -R holoscan /var/holoscan\n", "#14 CACHED\n", "\n", - "#15 [ 8/21] RUN chown -R holoscan /var/holoscan/output\n", + "#15 [ 9/20] WORKDIR /var/holoscan\n", "#15 CACHED\n", "\n", - "#16 [ 3/21] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", + "#16 [ 3/20] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", "#16 CACHED\n", "\n", - "#17 [11/21] RUN chmod +x /var/holoscan/tools\n", + "#17 [12/20] COPY ./pip/requirements.txt /tmp/requirements.txt\n", "#17 CACHED\n", "\n", - "#18 [ 7/21] RUN chown -R holoscan /var/holoscan/input\n", + "#18 [10/20] COPY ./tools /var/holoscan/tools\n", "#18 CACHED\n", "\n", - "#19 [14/21] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", + "#19 [13/20] RUN pip install --upgrade pip\n", "#19 CACHED\n", "\n", - "#20 [ 2/21] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", + "#20 [14/20] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "#20 CACHED\n", "\n", - "#21 [20/21] COPY ./map/pkg.json /etc/holoscan/pkg.json\n", - "#21 CACHED\n", - "\n", - "#22 [17/21] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", - "#22 CACHED\n", - "\n", - "#23 [19/21] COPY ./app.config /var/holoscan/app.yaml\n", - "#23 CACHED\n", - "\n", - "#24 [10/21] COPY ./tools /var/holoscan/tools\n", - "#24 CACHED\n", - "\n", - "#25 [18/21] COPY ./map/app.json /etc/holoscan/app.json\n", - "#25 CACHED\n", - "\n", - "#26 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", - "#26 CACHED\n", - "\n", - "#27 [21/21] COPY ./app /opt/holoscan/app\n", - "#27 CACHED\n", - "\n", - "#28 exporting to docker image format\n", - "#28 exporting layers done\n", - "#28 exporting manifest sha256:7a414ac96708c13dd2e15e2134807075b96eb40f1e16a0c9caf433f415792140 done\n", - "#28 exporting config sha256:1e2e576a3d23da01c03efa3c866cf22ecbcb75fa628e7edb6f2e0ffa7f6900b3 done\n", - "#28 sending tarball\n", - "#28 ...\n", - "\n", - "#29 importing to docker\n", - "#29 DONE 0.5s\n", - "\n", - "#28 exporting to docker image format\n", - "#28 sending tarball 40.7s done\n", - "#28 DONE 40.7s\n", - "\n", - "#30 exporting content cache\n", - 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already satisfied: cupy-cuda12x==12.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (12.2.0)\n", + "#22 1.309 Requirement already satisfied: cloudpickle==2.2.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (2.2.1)\n", + "#22 1.310 Requirement already satisfied: python-on-whales==0.60.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (0.60.1)\n", + "#22 1.311 Requirement already satisfied: Jinja2==3.1.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (3.1.2)\n", + "#22 1.311 Requirement already satisfied: packaging==23.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (23.1)\n", + "#22 1.312 Requirement already satisfied: pyyaml==6.0 in 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Collecting filelock (from wheel-axle-runtime<1.0->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.573 Downloading filelock-3.13.4-py3-none-any.whl.metadata (2.8 kB)\n", + "#22 1.639 Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer>=0.4.1->python-on-whales==0.60.1->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (8.1.7)\n", + "#22 1.683 Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n", + "#22 1.780 Downloading holoscan-1.0.3-cp310-cp310-manylinux_2_35_x86_64.whl (33.6 MB)\n", + "#22 6.199 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 33.6/33.6 MB 10.0 MB/s eta 0:00:00\n", + "#22 6.209 Downloading pip-23.3.2-py3-none-any.whl (2.1 MB)\n", + "#22 6.254 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 53.3 MB/s eta 0:00:00\n", + "#22 6.263 Downloading typeguard-4.2.1-py3-none-any.whl (34 kB)\n", + "#22 6.280 Downloading typing_extensions-4.11.0-py3-none-any.whl (34 kB)\n", + "#22 6.296 Downloading wheel_axle_runtime-0.0.5-py3-none-any.whl (12 kB)\n", + "#22 6.314 Downloading filelock-3.13.4-py3-none-any.whl (11 kB)\n", + "#22 6.622 Installing collected packages: typing-extensions, pip, filelock, colorama, wheel-axle-runtime, typeguard, holoscan, monai-deploy-app-sdk\n", + "#22 6.643 Attempting uninstall: pip\n", + "#22 6.644 Found existing installation: pip 24.0\n", + "#22 6.709 Uninstalling pip-24.0:\n", + "#22 7.165 Successfully uninstalled pip-24.0\n", + "#22 8.769 Successfully installed colorama-0.4.6 filelock-3.13.4 holoscan-1.0.3 monai-deploy-app-sdk-0.5.1+25.g31e4165.dirty pip-23.3.2 typeguard-4.2.1 typing-extensions-4.11.0 wheel-axle-runtime-0.0.5\n", + "#22 DONE 9.3s\n", + "\n", + "#23 [17/20] COPY ./map/app.json /etc/holoscan/app.json\n", + "#23 DONE 0.1s\n", + "\n", + "#24 [18/20] COPY ./app.config /var/holoscan/app.yaml\n", + "#24 DONE 0.0s\n", + "\n", + "#25 [19/20] COPY ./map/pkg.json /etc/holoscan/pkg.json\n", + "#25 DONE 0.0s\n", + "\n", + "#26 [20/20] COPY ./app /opt/holoscan/app\n", + "#26 DONE 0.0s\n", + "\n", + "#27 exporting to docker image format\n", + "#27 exporting layers\n", + "#27 exporting layers 5.7s done\n", + "#27 exporting manifest sha256:a5d5363b67b4d546c663819215cd3479b9fc8038916c32b1f39a3bd380aa1d27 0.0s done\n", + "#27 exporting config sha256:00503bdec78188be5602490be8caaa715487986151fd9430d0433b996ce4d386 0.0s done\n", + "#27 sending tarball\n", + "#27 ...\n", + "\n", + "#28 importing to docker\n", + "#28 loading layer 1a101d9210ae 32.77kB / 125.57kB\n", + "#28 loading layer c4b083b26ab0 557.06kB / 74.13MB\n", + "#28 loading layer c4b083b26ab0 71.30MB / 74.13MB 2.0s\n", + "#28 loading layer 77455b6ee5d5 492B / 492B\n", + "#28 loading layer 4583ff017de7 313B / 313B\n", + "#28 loading layer 90825b467166 293B / 293B\n", + "#28 loading layer e972a134a523 3.18kB / 3.18kB\n", + "#28 loading layer c4b083b26ab0 71.30MB / 74.13MB 3.1s done\n", + "#28 loading layer 1a101d9210ae 32.77kB / 125.57kB 3.2s done\n", + "#28 loading layer 77455b6ee5d5 492B / 492B 0.6s done\n", + "#28 loading layer 4583ff017de7 313B / 313B 0.6s done\n", + "#28 loading layer 90825b467166 293B / 293B 0.5s done\n", + "#28 loading layer e972a134a523 3.18kB / 3.18kB 0.5s done\n", + "#28 DONE 3.2s\n", + "\n", + "#27 exporting to docker image format\n", + "#27 sending tarball 42.0s done\n", + "#27 DONE 47.7s\n", + "\n", + "#29 exporting cache to client directory\n", + "#29 preparing build cache for export\n", + "#29 writing layer sha256:00bb4c1319ba1a33ac3edcb3aa1240d8abcb8d0383c6267ed8028d3b6228a8a4\n", + "#29 writing layer sha256:00bb4c1319ba1a33ac3edcb3aa1240d8abcb8d0383c6267ed8028d3b6228a8a4 done\n", + "#29 writing layer sha256:014cff740c9ec6e9a30d0b859219a700ae880eb385d62095d348f5ea136d6015 done\n", + "#29 writing layer sha256:085504367cb16317416ecf8e22015fdf0c1b46551c023bbf3b8742e23f5aef3c 0.0s done\n", + "#29 writing layer sha256:0a1756432df4a4350712d8ae5c003f1526bd2180800b3ae6301cfc9ccf370254 done\n", + "#29 writing 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done\n", + "#29 writing layer sha256:afde1c269453ce68a0f2b54c1ba8c5ecddeb18a19e5618a4acdef1f0fe3921af done\n", + "#29 writing layer sha256:b48a5fafcaba74eb5d7e7665601509e2889285b50a04b5b639a23f8adc818157 done\n", + "#29 writing layer sha256:ba9f7c75e4dd7942b944679995365aab766d3677da2e69e1d74472f471a484dd done\n", + "#29 writing layer sha256:bdfc73b2a0fa11b4086677e117a2f9feb6b4ffeccb23a3d58a30543339607e31 done\n", + "#29 writing layer sha256:c175bb235295e50de2961fa1e1a2235c57e6eba723a914287dfc26d3be0eac11 done\n", + "#29 writing layer sha256:c98533d2908f36a5e9b52faae83809b3b6865b50e90e2817308acfc64cd3655f done\n", + "#29 writing layer sha256:cb6c95b33bc30dd285c5b3cf99a05281b8f12decae1c932ab64bd58f56354021 done\n", + "#29 writing layer sha256:d7da5c5e9a40c476c4b3188a845e3276dedfd752e015ea5113df5af64d4d43f7 done\n", + "#29 writing layer sha256:de5a53c62648303a3a81f6443a00acfdaceea5d81a55b26a9dfcff656363ec2c 0.0s done\n", + "#29 writing layer sha256:e32a133f2d3ec74bbd17b59c6276eb0bffaff9e0737558a385e2f2280b4ca568 0.0s done\n", + "#29 writing layer sha256:e4aedc686433c0ec5e676e6cc54a164345f7016aa0eb714f00c07e11664a1168 done\n", + "#29 writing layer sha256:e8acb678f16bc0c369d5cf9c184f2d3a1c773986816526e5e3e9c0354f7e757f done\n", + "#29 writing layer sha256:e9225f7ab6606813ec9acba98a064826ebfd6713a9645a58cd068538af1ecddb done\n", + "#29 writing layer sha256:f06b2d19cffd90387b7ccd4e80ace09ab21995cf85c940091e6c80907532bda8\n", + "#29 preparing build cache for export 1.9s done\n", + "#29 writing layer sha256:f06b2d19cffd90387b7ccd4e80ace09ab21995cf85c940091e6c80907532bda8 0.0s done\n", + "#29 writing layer sha256:f33546e75bf1a7d9dc9e21b9a2c54c9d09b24790ad7a4192a8509002ceb14688 done\n", + "#29 writing layer sha256:f608e2fbff86e98627b7e462057e7d2416522096d73fe4664b82fe6ce8a4047d done\n", + "#29 writing layer sha256:f7702077ced42a1ee35e7f5e45f72634328ff3bcfe3f57735ba80baa5ec45daf done\n", + "#29 writing layer sha256:fa66a49172c6e821a1bace57c007c01da10cbc61507c44f8cdfeed8c4e5febab done\n", + "#29 writing config sha256:488336ab32415713da34c305ad87c5f983293c64534687f5fb685eb5b957c49c 0.0s done\n", + "#29 writing cache manifest sha256:69fe5811c303d48c634f794997602fd274d7922354e7aedf0dcd70b373a7a6a8 0.0s done\n", + "#29 DONE 1.9s\n", + "[2024-04-10 16:19:53,891] [INFO] (packager) - Build Summary:\n", "\n", "Platform: x64-workstation/dgpu\n", " Status: Succeeded\n", @@ -1647,7 +1717,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "simple_imaging_app-x64-workstation-dgpu-linux-amd64 1.0 1e2e576a3d23 45 minutes ago 10.8GB\n" + "simple_imaging_app-x64-workstation-dgpu-linux-amd64 1.0 00503bdec781 51 seconds ago 12.3GB\n" ] } ], @@ -1705,7 +1775,7 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1,\n", " \"workingDirectory\": \"/var/holoscan\"\n", @@ -1723,20 +1793,21 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"1Gi\"\n", " },\n", - " \"version\": 1\n", + " \"version\": 1,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "\n", - "2023-08-30 07:10:10 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app\n", + "2024-04-10 23:19:57 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app\n", "\n", - "2023-08-30 07:10:10 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json\n", - "2023-08-30 07:10:10 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json\n", - "2023-08-30 07:10:10 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml\n", + "2024-04-10 23:19:57 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json\n", + "2024-04-10 23:19:57 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json\n", + "2024-04-10 23:19:57 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml\n", "\n", - "2023-08-30 07:10:10 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models\n", - "2023-08-30 07:10:10 [INFO] '/opt/holoscan/models' cannot be found.\n", + "2024-04-10 23:19:57 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models\n", + "2024-04-10 23:19:57 [INFO] '/opt/holoscan/models' cannot be found.\n", "\n", - "2023-08-30 07:10:10 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs\n", - "2023-08-30 07:10:10 [INFO] '/opt/holoscan/docs/' cannot be found.\n", + "2024-04-10 23:19:57 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs\n", + "2024-04-10 23:19:57 [INFO] '/opt/holoscan/docs/' cannot be found.\n", "\n", "app config\n" ] @@ -1770,20 +1841,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 00:10:13,473] [INFO] (runner) - Checking dependencies...\n", - "[2023-08-30 00:10:13,473] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "[2024-04-10 16:19:58,260] [INFO] (runner) - Checking dependencies...\n", + "[2024-04-10 16:19:58,260] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "\n", + "[2024-04-10 16:19:58,260] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", "\n", - "[2023-08-30 00:10:13,473] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", + "[2024-04-10 16:19:58,260] [INFO] (runner) - --> Verifying if \"simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", "\n", - "[2023-08-30 00:10:13,473] [INFO] (runner) - --> Verifying if \"simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", + "[2024-04-10 16:19:58,323] [INFO] (runner) - Reading HAP/MAP manifest...\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmpkogym2cj/app.json\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmpkogym2cj/pkg.json\n", + "[2024-04-10 16:19:58,545] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", "\n", - "[2023-08-30 00:10:13,539] [INFO] (runner) - Reading HAP/MAP manifest...\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmpzdvb7nut/app.json\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmpzdvb7nut/pkg.json\n", - "[2023-08-30 00:10:13,748] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", + "[2024-04-10 16:19:58,545] [INFO] (runner) - --> Verifying \"nvidia-ctk\" version...\n", "\n", - "[2023-08-30 00:10:13,942] [INFO] (common) - Launching container (0fbb33cb469e) using image 'simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", - " container name: determined_leavitt\n", + "[2024-04-10 16:19:58,814] [INFO] (common) - Launching container (8e18e862585f) using image 'simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", + " container name: unruffled_kalam\n", " host name: mingq-dt\n", " network: host\n", " user: 1000:1000\n", @@ -1792,29 +1865,30 @@ " ipc mode: host\n", " shared memory size: 67108864\n", " devices: \n", - "2023-08-30 07:10:14 [INFO] Launching application python3 /opt/holoscan/app ...\n", + " group_add: 44\n", + "2024-04-10 23:19:59 [INFO] Launching application python3 /opt/holoscan/app ...\n", "\n", - "[2023-08-30 07:10:15,097] [INFO] (root) - Parsed args: Namespace(argv=['/opt/holoscan/app'], input=None, log_level=None, model=None, output=None, workdir=None)\n", + "[2024-04-10 23:20:00,177] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['/opt/holoscan/app'])\n", "\n", - "[2023-08-30 07:10:15,097] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", + "[2024-04-10 23:20:00,178] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", "\n", - "[2023-08-30 07:10:15,098] [INFO] (root) - sample_data_path: /var/holoscan/input\n", + "[2024-04-10 23:20:00,178] [INFO] (root) - sample_data_path: /var/holoscan/input\n", "\n", - "[info] [app_driver.cpp:1025] Launching the driver/health checking service\n", + "[info] [app_driver.cpp:1161] Launching the driver/health checking service\n", "\n", - "[info] [gxf_executor.cpp:210] Creating context\n", + "[info] [gxf_executor.cpp:211] Creating context\n", "\n", - "[info] [server.cpp:73] Health checking server listening on 0.0.0.0:8777\n", + "[info] [server.cpp:87] Health checking server listening on 0.0.0.0:8777\n", "\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", "\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", "\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", "\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "\n", "[info] [greedy_scheduler.cpp:190] Scheduling 3 entities\n", "\n", @@ -1822,13 +1896,13 @@ "\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", "\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", "\n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", "\n", - "[info] [gxf_executor.cpp:229] Destroying context\n", + "[info] [gxf_executor.cpp:230] Destroying context\n", "\n", "Number of times operator sobel_op whose class is defined in sobel_operator called: 1\n", "\n", @@ -1842,7 +1916,7 @@ "\n", "Data type of output post conversion: , max = 91\n", "\n", - "[2023-08-30 00:10:16,538] [INFO] (common) - Container 'determined_leavitt'(0fbb33cb469e) exited.\n" + "[2024-04-10 16:20:01,327] [INFO] (common) - Container 'unruffled_kalam'(8e18e862585f) exited.\n" ] } ], @@ -1860,7 +1934,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 27, @@ -1869,7 +1943,7 @@ }, { "data": { - "image/png": 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", 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", 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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], @@ -129,31 +136,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: monai in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (1.3.0)\n", - "Requirement already satisfied: Pillow in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (10.0.1)\n", - "Requirement already satisfied: numpy>=1.20 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from monai) (1.24.4)\n", - "Requirement already satisfied: torch>=1.9 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from monai) 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"Requirement already satisfied: triton==2.0.0 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from torch>=1.9->monai) (2.0.0)\n", + "Requirement already satisfied: setuptools in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.9->monai) (69.2.0)\n", + "Requirement already satisfied: wheel in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.9->monai) (0.43.0)\n", + "Requirement already satisfied: cmake in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from triton==2.0.0->torch>=1.9->monai) (3.29.0.1)\n", + "Requirement already satisfied: lit in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from triton==2.0.0->torch>=1.9->monai) (18.1.2)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from jinja2->torch>=1.9->monai) (2.1.5)\n", + "Requirement already satisfied: mpmath>=0.19 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from sympy->torch>=1.9->monai) (1.3.0)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], @@ -180,23 +192,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: gdown in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (4.7.1)\n", - "Requirement already satisfied: filelock in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (3.13.1)\n", - "Requirement already satisfied: requests[socks] in 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requests[socks] in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (2.28.2)\n", + "Requirement already satisfied: tqdm in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (4.66.2)\n", + "Requirement already satisfied: soupsieve>1.2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from beautifulsoup4->gdown) (2.5)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (3.6)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (1.26.18)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (2024.2.2)\n", + "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (1.7.1)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Downloading...\n", - "From (uriginal): https://drive.google.com/uc?id=1yJ4P-xMNEfN6lIOq_u6x1eMAq1_MJu-E\n", - "From (redirected): https://drive.google.com/uc?id=1yJ4P-xMNEfN6lIOq_u6x1eMAq1_MJu-E&confirm=t&uuid=d9974e09-6ccd-4416-9f41-2c3702a3bea7\n", + "From (original): https://drive.google.com/uc?id=1yJ4P-xMNEfN6lIOq_u6x1eMAq1_MJu-E\n", + "From (redirected): https://drive.google.com/uc?id=1yJ4P-xMNEfN6lIOq_u6x1eMAq1_MJu-E&confirm=t&uuid=8dfa9939-2267-4d12-ba17-3e35b1626357\n", "To: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/mednist_classifier_data.zip\n", - "100%|██████████████████████████████████████| 28.6M/28.6M [00:00<00:00, 64.3MB/s]\n" + "100%|██████████████████████████████████████| 28.6M/28.6M [00:00<00:00, 62.3MB/s]\n" ] } ], @@ -291,14 +305,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-11-15 18:37:06,027] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/source/examples/apps/mednist_classifier_monaideploy/mednist_classifier_monaideploy.py\n", - "[2023-11-15 18:37:06,027] [INFO] (packager.parameters) - Detected application type: Python File\n", - "[2023-11-15 18:37:06,027] [INFO] (packager) - Scanning for models in {models_path}...\n", - "[2023-11-15 18:37:06,027] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.\n", - "[2023-11-15 18:37:06,027] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/source/examples/apps/mednist_classifier_monaideploy/app.yaml...\n", - "[2023-11-15 18:37:06,030] [INFO] (packager) - Generating app.json...\n", - "[2023-11-15 18:37:06,030] [INFO] (packager) - Generating pkg.json...\n", - "[2023-11-15 18:37:06,033] [DEBUG] (common) - \n", + "[2024-04-10 16:23:51,962] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/source/examples/apps/mednist_classifier_monaideploy/mednist_classifier_monaideploy.py\n", + "[2024-04-10 16:23:51,962] [INFO] (packager.parameters) - Detected application type: Python File\n", + "[2024-04-10 16:23:51,962] [INFO] (packager) - Scanning for models in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models...\n", + "[2024-04-10 16:23:51,962] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.\n", + "[2024-04-10 16:23:51,962] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/source/examples/apps/mednist_classifier_monaideploy/app.yaml...\n", + "[2024-04-10 16:23:51,964] [INFO] (packager) - Generating app.json...\n", + "[2024-04-10 16:23:51,964] [INFO] (packager) - Generating pkg.json...\n", + "[2024-04-10 16:23:51,969] [DEBUG] (common) - \n", "=============== Begin app.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", @@ -326,21 +340,21 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1.0,\n", " \"workingDirectory\": \"/var/holoscan\"\n", "}\n", "================ End app.json ================\n", " \n", - "[2023-11-15 18:37:06,033] [DEBUG] (common) - \n", + "[2024-04-10 16:23:51,969] [DEBUG] (common) - \n", "=============== Begin pkg.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", " \"applicationRoot\": \"/opt/holoscan/app\",\n", " \"modelRoot\": \"/opt/holoscan/models\",\n", " \"models\": {\n", - " \"model\": \"/opt/holoscan/models\"\n", + " \"model\": \"/opt/holoscan/models/model\"\n", " },\n", " \"resources\": {\n", " \"cpu\": 1,\n", @@ -348,15 +362,16 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"1Gi\"\n", " },\n", - " \"version\": 1.0\n", + " \"version\": 1.0,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "================ End pkg.json ================\n", " \n", - "[2023-11-15 18:37:06,061] [DEBUG] (packager.builder) - \n", + "[2024-04-10 16:23:52,003] [DEBUG] (packager.builder) - \n", "========== Begin Dockerfile ==========\n", "\n", "\n", - "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", + "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", "\n", "ENV DEBIAN_FRONTEND=noninteractive\n", "ENV TERM=xterm-256color\n", @@ -372,11 +387,13 @@ " && mkdir -p /var/holoscan/input \\\n", " && mkdir -p /var/holoscan/output\n", "\n", - "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\"\n", + "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\"\n", "LABEL tag=\"mednist_app:1.0\"\n", "LABEL org.opencontainers.image.title=\"MONAI Deploy App Package - MedNIST Classifier App\"\n", "LABEL org.opencontainers.image.version=\"1.0\"\n", - "LABEL org.nvidia.holoscan=\"0.6.0\"\n", + "LABEL org.nvidia.holoscan=\"1.0.3\"\n", + "LABEL org.monai.deploy.app-sdk=\"0.5.1\"\n", + "\n", "\n", "ENV HOLOSCAN_ENABLE_HEALTH_CHECK=true\n", "ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input\n", @@ -401,7 +418,7 @@ "\n", "\n", "\n", - "RUN groupadd -g $GID $UNAME\n", + "RUN groupadd -f -g $GID $UNAME\n", "RUN useradd -rm -d /home/$UNAME -s /bin/bash -g $GID -G sudo -u $UID $UNAME\n", "RUN chown -R holoscan /var/holoscan \n", "RUN chown -R holoscan /var/holoscan/input \n", @@ -426,12 +443,12 @@ "RUN pip install --upgrade pip\n", "RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "\n", - "# Install Holoscan from PyPI org\n", - "RUN pip install holoscan==0.6.0\n", - "\n", + "# Install Holoscan from PyPI only when sdk_type is Holoscan. \n", + "# For MONAI Deploy, the APP SDK will install it unless user specifies the Holoscan SDK file.\n", "\n", - "# Install MONAI Deploy from PyPI org\n", - "RUN pip install monai-deploy-app-sdk==0.6.0\n", + "# Copy user-specified MONAI Deploy SDK file\n", + "COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", "\n", "\n", "\n", @@ -447,203 +464,276 @@ "ENTRYPOINT [\"/var/holoscan/tools\"]\n", "=========== End Dockerfile ===========\n", "\n", - "[2023-11-15 18:37:06,061] [INFO] (packager.builder) - \n", + "[2024-04-10 16:23:52,003] [INFO] (packager.builder) - \n", "===============================================================================\n", "Building image for: x64-workstation\n", " Architecture: linux/amd64\n", - " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - " Build Image: N/A \n", + " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + " Build Image: N/A\n", " Cache: Enabled\n", " Configuration: dgpu\n", - " Holoiscan SDK Package: pypi.org\n", - " MONAI Deploy App SDK Package: pypi.org\n", + " Holoscan SDK Package: pypi.org\n", + " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", " gRPC Health Probe: N/A\n", - " SDK Version: 0.6.0\n", + " SDK Version: 1.0.3\n", " SDK: monai-deploy\n", " Tag: mednist_app-x64-workstation-dgpu-linux-amd64:1.0\n", " \n", - "[2023-11-15 18:37:06,311] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", - "[2023-11-15 18:37:06,312] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=mednist_app-x64-workstation-dgpu-linux-amd64:1.0\n", + "[2024-04-10 16:23:52,265] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", + "[2024-04-10 16:23:52,265] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=mednist_app-x64-workstation-dgpu-linux-amd64:1.0\n", "#0 building with \"holoscan_app_builder\" instance using docker-container driver\n", "\n", "#1 [internal] load build definition from Dockerfile\n", - "#1 transferring dockerfile: 2.49kB done\n", + "#1 transferring dockerfile: 2.81kB done\n", "#1 DONE 0.1s\n", "\n", - "#2 [internal] load .dockerignore\n", - "#2 transferring context: 1.79kB done\n", + "#2 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", "#2 DONE 0.1s\n", "\n", - "#3 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#3 DONE 0.4s\n", + "#3 [internal] load .dockerignore\n", + "#3 transferring context: 1.79kB done\n", + "#3 DONE 0.1s\n", "\n", - "#4 [internal] load build context\n", - "#4 DONE 0.0s\n", + "#4 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#4 ...\n", "\n", - "#5 importing cache manifest from local:12435489437730595250\n", + "#5 [internal] load build context\n", "#5 DONE 0.0s\n", "\n", - "#6 [ 1/21] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc\n", - "#6 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc 0.1s done\n", - "#6 DONE 0.1s\n", + "#6 importing cache manifest from local:3023656059275295125\n", + "#6 inferred cache manifest type: application/vnd.oci.image.index.v1+json done\n", + "#6 DONE 0.0s\n", "\n", - "#7 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#7 DONE 0.7s\n", + "#7 [ 1/21] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155\n", + "#7 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155 0.1s done\n", + "#7 DONE 0.1s\n", "\n", - "#4 [internal] load build context\n", - "#4 transferring context: 28.60MB 0.3s done\n", - "#4 DONE 0.3s\n", + "#4 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#4 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done\n", + "#4 DONE 0.7s\n", "\n", - "#8 [11/21] RUN chmod +x /var/holoscan/tools\n", + "#5 [internal] load build context\n", + "#5 transferring context: 28.73MB 0.2s done\n", + "#5 DONE 0.2s\n", + "\n", + "#8 [ 9/21] WORKDIR /var/holoscan\n", "#8 CACHED\n", "\n", - "#9 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt\n", + "#9 [ 2/21] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", "#9 CACHED\n", "\n", - "#10 [ 7/21] RUN chown -R holoscan /var/holoscan/input\n", + "#10 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", "#10 CACHED\n", "\n", - "#11 [20/21] COPY ./map/pkg.json /etc/holoscan/pkg.json\n", + "#11 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt\n", "#11 CACHED\n", "\n", - "#12 [ 2/21] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", + "#12 [ 6/21] RUN chown -R holoscan /var/holoscan\n", "#12 CACHED\n", "\n", - "#13 [ 6/21] RUN chown -R holoscan /var/holoscan\n", + "#13 [11/21] RUN chmod +x /var/holoscan/tools\n", "#13 CACHED\n", "\n", - "#14 [ 9/21] WORKDIR /var/holoscan\n", + "#14 [13/21] RUN pip install --upgrade pip\n", "#14 CACHED\n", "\n", - "#15 [ 8/21] RUN chown -R holoscan /var/holoscan/output\n", + "#15 [10/21] COPY ./tools /var/holoscan/tools\n", "#15 CACHED\n", "\n", - "#16 [10/21] COPY ./tools /var/holoscan/tools\n", + "#16 [ 8/21] RUN chown -R holoscan /var/holoscan/output\n", "#16 CACHED\n", "\n", - "#17 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", + "#17 [ 4/21] RUN groupadd -f -g 1000 holoscan\n", "#17 CACHED\n", "\n", - "#18 [17/21] COPY ./models /opt/holoscan/models\n", + "#18 [ 3/21] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", "#18 CACHED\n", "\n", - "#19 [16/21] RUN pip install monai-deploy-app-sdk==0.6.0\n", + "#19 [ 7/21] RUN chown -R holoscan /var/holoscan/input\n", "#19 CACHED\n", "\n", - "#20 [ 3/21] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", + "#20 [14/21] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "#20 CACHED\n", "\n", - "#21 [13/21] RUN pip install --upgrade pip\n", - "#21 CACHED\n", - "\n", - "#22 [14/21] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", - "#22 CACHED\n", - "\n", - "#23 [18/21] COPY ./map/app.json /etc/holoscan/app.json\n", - "#23 CACHED\n", - "\n", - "#24 [ 4/21] RUN groupadd -g 1000 holoscan\n", - "#24 CACHED\n", - "\n", - "#25 [15/21] RUN pip install holoscan==0.6.0\n", - "#25 CACHED\n", - "\n", - "#26 [19/21] COPY ./app.config /var/holoscan/app.yaml\n", - "#26 CACHED\n", + "#21 [15/21] COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#21 DONE 0.3s\n", + "\n", + "#22 [16/21] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#22 0.682 Defaulting to user installation because normal site-packages is not writeable\n", + "#22 0.793 Processing /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#22 0.803 Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (1.23.5)\n", + "#22 0.906 Collecting holoscan~=1.0 (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 0.971 Downloading holoscan-1.0.3-cp310-cp310-manylinux_2_35_x86_64.whl.metadata (4.1 kB)\n", + "#22 1.044 Collecting colorama>=0.4.1 (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.050 Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n", + "#22 1.135 Collecting typeguard>=3.0.0 (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.139 Downloading typeguard-4.2.1-py3-none-any.whl.metadata (3.7 kB)\n", + "#22 1.248 Collecting pip==23.3.2 (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.254 Downloading pip-23.3.2-py3-none-any.whl.metadata (3.5 kB)\n", + "#22 1.269 Requirement already satisfied: cupy-cuda12x==12.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (12.2.0)\n", + "#22 1.269 Requirement already satisfied: cloudpickle==2.2.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (2.2.1)\n", + "#22 1.271 Requirement already satisfied: python-on-whales==0.60.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (0.60.1)\n", + "#22 1.272 Requirement already satisfied: Jinja2==3.1.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (3.1.2)\n", + "#22 1.272 Requirement already satisfied: packaging==23.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (23.1)\n", + "#22 1.273 Requirement already satisfied: pyyaml==6.0 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (6.0)\n", + "#22 1.274 Requirement already satisfied: requests==2.28.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (2.28.2)\n", + "#22 1.275 Requirement already satisfied: psutil==5.9.6 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (5.9.6)\n", + "#22 1.308 Collecting wheel-axle-runtime<1.0 (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.313 Downloading wheel_axle_runtime-0.0.5-py3-none-any.whl.metadata (7.7 kB)\n", + "#22 1.347 Requirement already satisfied: fastrlock>=0.5 in /usr/local/lib/python3.10/dist-packages (from cupy-cuda12x==12.2->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (0.8.2)\n", + "#22 1.351 Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2==3.1.2->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (2.1.3)\n", + "#22 1.366 Requirement already satisfied: pydantic<2,>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (1.10.14)\n", + "#22 1.367 Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (4.66.1)\n", + "#22 1.367 Requirement already satisfied: typer>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (0.9.0)\n", + "#22 1.369 Requirement already satisfied: typing-extensions in /home/holoscan/.local/lib/python3.10/site-packages (from python-on-whales==0.60.1->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (4.10.0)\n", + "#22 1.380 Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests==2.28.2->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (3.3.2)\n", + "#22 1.381 Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests==2.28.2->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (3.6)\n", + "#22 1.382 Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests==2.28.2->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (1.26.18)\n", + "#22 1.383 Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests==2.28.2->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (2023.11.17)\n", + "#22 1.401 Requirement already satisfied: filelock in /home/holoscan/.local/lib/python3.10/site-packages (from wheel-axle-runtime<1.0->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (3.13.3)\n", + "#22 1.443 Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer>=0.4.1->python-on-whales==0.60.1->holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (8.1.7)\n", + "#22 1.490 Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n", + "#22 1.516 Downloading holoscan-1.0.3-cp310-cp310-manylinux_2_35_x86_64.whl (33.6 MB)\n", + "#22 1.982 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 33.6/33.6 MB 43.1 MB/s eta 0:00:00\n", + "#22 1.989 Downloading pip-23.3.2-py3-none-any.whl (2.1 MB)\n", + "#22 2.032 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 53.0 MB/s eta 0:00:00\n", + "#22 2.039 Downloading typeguard-4.2.1-py3-none-any.whl (34 kB)\n", + "#22 2.064 Downloading wheel_axle_runtime-0.0.5-py3-none-any.whl (12 kB)\n", + "#22 2.417 Installing collected packages: wheel-axle-runtime, typeguard, pip, colorama, holoscan, monai-deploy-app-sdk\n", + "#22 2.492 Attempting uninstall: pip\n", + "#22 2.493 Found existing installation: pip 24.0\n", + "#22 2.547 Uninstalling pip-24.0:\n", + "#22 2.977 Successfully uninstalled pip-24.0\n", + "#22 4.656 Successfully installed colorama-0.4.6 holoscan-1.0.3 monai-deploy-app-sdk-0.5.1+25.g31e4165.dirty pip-23.3.2 typeguard-4.2.1 wheel-axle-runtime-0.0.5\n", + "#22 DONE 5.2s\n", + "\n", + "#23 [17/21] COPY ./models /opt/holoscan/models\n", + "#23 DONE 0.2s\n", + "\n", + "#24 [18/21] COPY ./map/app.json /etc/holoscan/app.json\n", + "#24 DONE 0.1s\n", + "\n", + "#25 [19/21] COPY ./app.config /var/holoscan/app.yaml\n", + "#25 DONE 0.1s\n", + "\n", + "#26 [20/21] COPY ./map/pkg.json /etc/holoscan/pkg.json\n", + "#26 DONE 0.1s\n", "\n", "#27 [21/21] COPY ./app /opt/holoscan/app\n", - "#27 CACHED\n", + "#27 DONE 0.1s\n", "\n", "#28 exporting to docker image format\n", - "#28 exporting layers done\n", - "#28 exporting manifest sha256:19203b8c5bb9a13da3c03963a62723b9f330ff4b2d37776a26d1316d03da1b0b done\n", - "#28 exporting config sha256:f980243cd5d80a64490eda352560b92681a08191a30d0a585cdbbf0c409e8abc done\n", + "#28 exporting layers\n", + "#28 exporting layers 6.0s done\n", + "#28 exporting manifest sha256:02c9015ef90dc072d10044946ce69d29e7dbd7e748cd98b56713e2a32f1823bc 0.0s done\n", + "#28 exporting config sha256:26b7cd41adaba5d7700f104387d1c2d4b66829292de9fe656dff6f30ba20e56d 0.0s done\n", "#28 sending tarball\n", "#28 ...\n", "\n", "#29 importing to docker\n", - "#29 DONE 0.9s\n", + "#29 loading layer 3c784a11874c 32.77kB / 125.57kB\n", + "#29 loading layer 71abc17edeb9 557.06kB / 73.96MB\n", + "#29 loading layer 71abc17edeb9 73.53MB / 73.96MB 2.0s\n", + "#29 loading layer 8eee96e0be35 262.14kB / 25.59MB\n", + "#29 loading layer 81dfa72eaf50 512B / 512B\n", + "#29 loading layer c726a53666d2 697B / 697B\n", + "#29 loading layer 0266dfb048c0 297B / 297B\n", + "#29 loading layer d4a6edcf43fc 4.17kB / 4.17kB\n", + "#29 loading layer 3c784a11874c 32.77kB / 125.57kB 5.0s done\n", + "#29 loading layer 71abc17edeb9 73.53MB / 73.96MB 4.9s done\n", + "#29 loading layer 8eee96e0be35 262.14kB / 25.59MB 2.8s done\n", + "#29 loading layer 81dfa72eaf50 512B / 512B 2.2s done\n", + "#29 loading layer c726a53666d2 697B / 697B 1.9s done\n", + "#29 loading layer 0266dfb048c0 297B / 297B 1.5s done\n", + "#29 loading layer d4a6edcf43fc 4.17kB / 4.17kB 1.1s done\n", + "#29 DONE 5.0s\n", "\n", "#28 exporting to docker image format\n", - "#28 sending tarball 39.8s done\n", - "#28 DONE 39.8s\n", + "#28 sending tarball 65.8s done\n", + "#28 DONE 72.0s\n", "\n", - "#30 exporting content cache\n", + "#30 exporting cache to client directory\n", "#30 preparing build cache for export\n", - "#30 writing layer sha256:0709800848b4584780b40e7e81200689870e890c38b54e96b65cd0a3b1942f2d done\n", - "#30 writing layer sha256:0ce020987cfa5cd1654085af3bb40779634eb3d792c4a4d6059036463ae0040d done\n", - "#30 writing layer sha256:0f65089b284381bf795d15b1a186e2a8739ea957106fa526edef0d738e7cda70 done\n", - "#30 writing layer sha256:12a47450a9f9cc5d4edab65d0f600dbbe8b23a1663b0b3bb2c481d40e074b580 done\n", - "#30 writing layer sha256:19c20b65326c1511f8ab02f4a41453f8c0b6d9f2bdea8bb25038b628cef67ab9 done\n", - "#30 writing layer sha256:1de965777e2e37c7fabe00bdbf3d0203ca83ed30a71a5479c3113fe4fc48c4bb done\n", - "#30 writing layer sha256:22b384cd1e678fc56dc95c82f42e7a540d055418d0f1eef8d908c88305e23a88 done\n", - "#30 writing layer sha256:2369548ddf79fa9fd07c5f1c4b226da97c9cc7991c4b3bd57a97796c31ff0648 done\n", - "#30 writing layer sha256:24b5aa2448e920814dd67d7d3c0169b2cdacb13c4048d74ded3b4317843b13ff done\n", - "#30 writing layer sha256:2d42104dbf0a7cc962b791f6ab4f45a803f8a36d296f996aca180cfb2f3e30d0 done\n", - "#30 writing layer sha256:2fa1ce4fa3fec6f9723380dc0536b7c361d874add0baaddc4bbf2accac82d2ff done\n", - "#30 writing layer sha256:361eb07c24550c859aa0f62bebfcaabde6373eeee0c16ae66e7c7053bf1f1e42 done\n", - "#30 writing layer sha256:38794be1b5dc99645feabf89b22cd34fb5bdffb5164ad920e7df94f353efe9c0 done\n", - 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layer sha256:bafd0706f1969063f2baea22f5df92629ba228c67819e957f88b20582aaa4801 1.5s done\n", + "#30 writing layer sha256:bdfc73b2a0fa11b4086677e117a2f9feb6b4ffeccb23a3d58a30543339607e31\n", + "#30 writing layer sha256:bdfc73b2a0fa11b4086677e117a2f9feb6b4ffeccb23a3d58a30543339607e31 done\n", + "#30 writing layer sha256:c175bb235295e50de2961fa1e1a2235c57e6eba723a914287dfc26d3be0eac11 done\n", + "#30 writing layer sha256:c2a80b194dd0ff43e8e6ea838efe3b6f24371797b498b6d2d7ac53fb9d4aee8b done\n", + "#30 writing layer sha256:c6e0f549352b7817454c6c4540b863766f732e9216158288017cfcb19cd91bef 0.0s done\n", + "#30 writing layer sha256:c98533d2908f36a5e9b52faae83809b3b6865b50e90e2817308acfc64cd3655f done\n", + "#30 writing layer sha256:cb6c95b33bc30dd285c5b3cf99a05281b8f12decae1c932ab64bd58f56354021 done\n", + "#30 writing layer sha256:d7da5c5e9a40c476c4b3188a845e3276dedfd752e015ea5113df5af64d4d43f7 done\n", + "#30 writing layer sha256:e4aedc686433c0ec5e676e6cc54a164345f7016aa0eb714f00c07e11664a1168 done\n", + "#30 writing layer sha256:e4e14fa6c90d19eb19aad3f52f9cd59a25c44007ba201741ac7cbff722837883 done\n", + "#30 writing layer sha256:e8acb678f16bc0c369d5cf9c184f2d3a1c773986816526e5e3e9c0354f7e757f done\n", + "#30 writing layer sha256:e9225f7ab6606813ec9acba98a064826ebfd6713a9645a58cd068538af1ecddb done\n", + "#30 writing layer sha256:f33546e75bf1a7d9dc9e21b9a2c54c9d09b24790ad7a4192a8509002ceb14688 done\n", + "#30 writing layer sha256:f608e2fbff86e98627b7e462057e7d2416522096d73fe4664b82fe6ce8a4047d done\n", + "#30 writing layer sha256:f7702077ced42a1ee35e7f5e45f72634328ff3bcfe3f57735ba80baa5ec45daf done\n", + "#30 writing layer sha256:fa66a49172c6e821a1bace57c007c01da10cbc61507c44f8cdfeed8c4e5febab done\n", + "#30 writing layer sha256:fc1f60b32aa696c9cfeacbee0e2c0aeefd8331e0a38fcd082b60ae33b67e34e4 0.0s done\n", + "#30 writing config sha256:b2150835373b386791df4e482a3f33750ce9ec1d81393ee061a6d9c06dc8d52a 0.0s done\n", + "#30 preparing build cache for export 2.8s done\n", + "#30 writing cache manifest sha256:ed8fc8e6d14dafb96aed8205ce985667c75f419d2bf7ce449e533b415b01b699 0.0s done\n", + "#30 DONE 2.8s\n", + "[2024-04-10 16:25:16,137] [INFO] (packager) - Build Summary:\n", "\n", "Platform: x64-workstation/dgpu\n", " Status: Succeeded\n", @@ -674,7 +764,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "mednist_app-x64-workstation-dgpu-linux-amd64 1.0 f980243cd5d8 About an hour ago 15.4GB\n" + "mednist_app-x64-workstation-dgpu-linux-amd64 1.0 26b7cd41adab About a minute ago 17.5GB\n" ] } ], @@ -732,7 +822,7 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1,\n", " \"workingDirectory\": \"/var/holoscan\"\n", @@ -744,7 +834,7 @@ " \"applicationRoot\": \"/opt/holoscan/app\",\n", " \"modelRoot\": \"/opt/holoscan/models\",\n", " \"models\": {\n", - " \"model\": \"/opt/holoscan/models\"\n", + " \"model\": \"/opt/holoscan/models/model\"\n", " },\n", " \"resources\": {\n", " \"cpu\": 1,\n", @@ -752,19 +842,20 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"1Gi\"\n", " },\n", - " \"version\": 1\n", + " \"version\": 1,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "\n", - "2023-11-16 02:37:55 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app\n", + "2024-04-10 23:25:19 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app\n", "\n", - "2023-11-16 02:37:55 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json\n", - "2023-11-16 02:37:55 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json\n", - "2023-11-16 02:37:55 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml\n", + "2024-04-10 23:25:19 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json\n", + "2024-04-10 23:25:19 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json\n", + "2024-04-10 23:25:19 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml\n", "\n", - "2023-11-16 02:37:55 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models\n", + "2024-04-10 23:25:19 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models\n", "\n", - "2023-11-16 02:37:55 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs\n", - "2023-11-16 02:37:55 [INFO] '/opt/holoscan/docs/' cannot be found.\n", + "2024-04-10 23:25:19 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs\n", + "2024-04-10 23:25:19 [INFO] '/opt/holoscan/docs/' cannot be found.\n", "\n", "app config models\n" ] @@ -796,20 +887,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-11-15 18:37:58,606] [INFO] (runner) - Checking dependencies...\n", - "[2023-11-15 18:37:58,606] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "[2024-04-10 16:25:20,790] [INFO] (runner) - Checking dependencies...\n", + "[2024-04-10 16:25:20,790] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "\n", + "[2024-04-10 16:25:20,791] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", "\n", - "[2023-11-15 18:37:58,606] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", + "[2024-04-10 16:25:20,791] [INFO] (runner) - --> Verifying if \"mednist_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", "\n", - "[2023-11-15 18:37:58,606] [INFO] (runner) - --> Verifying if \"mednist_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", + "[2024-04-10 16:25:20,871] [INFO] (runner) - Reading HAP/MAP manifest...\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmpryp430i1/app.json\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmpryp430i1/pkg.json\n", + "[2024-04-10 16:25:21,124] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", "\n", - "[2023-11-15 18:37:58,679] [INFO] (runner) - Reading HAP/MAP manifest...\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmp89s5qkz8/app.json\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmp89s5qkz8/pkg.json\n", - "[2023-11-15 18:37:58,873] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", + "[2024-04-10 16:25:21,124] [INFO] (runner) - --> Verifying \"nvidia-ctk\" version...\n", "\n", - "[2023-11-15 18:37:59,068] [INFO] (common) - Launching container (e8950f0a463a) using image 'mednist_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", - " container name: priceless_ptolemy\n", + "[2024-04-10 16:25:21,410] [INFO] (common) - Launching container (7cf522bd06c4) using image 'mednist_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", + " container name: dazzling_hugle\n", " host name: mingq-dt\n", " network: host\n", " user: 1000:1000\n", @@ -818,61 +911,54 @@ " ipc mode: host\n", " shared memory size: 67108864\n", " devices: \n", - "2023-11-16 02:37:59 [INFO] Launching application python3 /opt/holoscan/app/mednist_classifier_monaideploy.py ...\n", + " group_add: 44\n", + "2024-04-10 23:25:22 [INFO] Launching application python3 /opt/holoscan/app/mednist_classifier_monaideploy.py ...\n", "\n", - "[2023-11-16 02:38:01,849] [INFO] (root) - Parsed args: Namespace(argv=['/opt/holoscan/app/mednist_classifier_monaideploy.py'], input=None, log_level=None, model=None, output=None, workdir=None)\n", + "[2024-04-10 23:25:31,408] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['/opt/holoscan/app/mednist_classifier_monaideploy.py'])\n", "\n", - "[2023-11-16 02:38:01,857] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", + "[2024-04-10 23:25:31,419] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", "\n", - "[info] [app_driver.cpp:1025] Launching the driver/health checking service\n", + "[info] [app_driver.cpp:1161] Launching the driver/health checking service\n", "\n", - "[info] [gxf_executor.cpp:210] Creating context\n", + "[info] [gxf_executor.cpp:211] Creating context\n", "\n", - "[info] [server.cpp:73] Health checking server listening on 0.0.0.0:8777\n", + "[info] [server.cpp:87] Health checking server listening on 0.0.0.0:8777\n", "\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", "\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", "\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", "\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "\n", "[info] [greedy_scheduler.cpp:190] Scheduling 3 entities\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:111: FutureWarning: : Class `AddChannel` has been deprecated since version 0.8. It will be removed in version 1.3. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead with `channel_dim='no_channel'`.\n", - "\n", - " warn_deprecated(obj, msg, warning_category)\n", - "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", + "/home/holoscan/.local/lib/python3.10/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", "\n", - " return torch.as_tensor(x, *args, **_kwargs).as_subclass(cls) # type: ignore\n", - "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", - "\n", - " warnings.warn(msg)\n", + " return torch.as_tensor(x, *args, **_kwargs).as_subclass(cls)\n", "\n", - "[2023-11-16 02:38:04,038] [INFO] (root) - Finished writing DICOM instance to file /var/holoscan/output/1.2.826.0.1.3680043.8.498.16497775401758865936247607314643258908.dcm\n", + "[2024-04-10 23:25:34,063] [INFO] (root) - Finished writing DICOM instance to file /var/holoscan/output/1.2.826.0.1.3680043.8.498.23303108191806495091599558367348667557.dcm\n", "\n", - "[2023-11-16 02:38:04,039] [INFO] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - DICOM SOP instance saved in /var/holoscan/output/1.2.826.0.1.3680043.8.498.16497775401758865936247607314643258908.dcm\n", + "[2024-04-10 23:25:34,064] [INFO] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - DICOM SOP instance saved in /var/holoscan/output/1.2.826.0.1.3680043.8.498.23303108191806495091599558367348667557.dcm\n", "\n", "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", "\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", "\n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", "\n", - "[info] [gxf_executor.cpp:229] Destroying context\n", + "[info] [gxf_executor.cpp:230] Destroying context\n", "\n", "AbdomenCT\n", "\n", - "[2023-11-15 18:38:04,955] [INFO] (common) - Container 'priceless_ptolemy'(e8950f0a463a) exited.\n" + "[2024-04-10 16:25:35,771] [INFO] (common) - Container 'dazzling_hugle'(7cf522bd06c4) exited.\n" ] } ], @@ -1236,21 +1322,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-11-15 18:38:10,416] [INFO] (root) - Parsed args: Namespace(argv=[], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-11-15 18:38:10,440] [INFO] (root) - AppContext object: AppContext(input_path=input, output_path=output, model_path=models, workdir=)\n", - "[info] [gxf_executor.cpp:210] Creating context\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 16:25:39,884] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])\n", + "[2024-04-10 16:25:39,898] [INFO] (root) - AppContext object: AppContext(input_path=input, output_path=output, model_path=models, workdir=)\n", + "[info] [gxf_executor.cpp:211] Creating context\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[info] [greedy_scheduler.cpp:190] Scheduling 3 entities\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", " return torch.as_tensor(x, *args, **_kwargs).as_subclass(cls)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", " warnings.warn(msg)\n", - "[2023-11-15 18:38:11,717] [INFO] (root) - Finished writing DICOM instance to file output/1.2.826.0.1.3680043.8.498.12540892677700748616860638452010005160.dcm\n", - "[2023-11-15 18:38:11,720] [INFO] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - DICOM SOP instance saved in output/1.2.826.0.1.3680043.8.498.12540892677700748616860638452010005160.dcm\n" + "[2024-04-10 16:25:42,470] [INFO] (root) - Finished writing DICOM instance to file output/1.2.826.0.1.3680043.8.498.71636533308287156684391922232397043508.dcm\n", + "[2024-04-10 16:25:42,472] [INFO] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - DICOM SOP instance saved in output/1.2.826.0.1.3680043.8.498.71636533308287156684391922232397043508.dcm\n" ] }, { @@ -1266,10 +1352,10 @@ "text": [ "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[info] [gxf_executor.cpp:229] Destroying context\n" + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[info] [gxf_executor.cpp:230] Destroying context\n" ] } ], @@ -1596,50 +1682,50 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-11-15 18:38:17,975] [INFO] (root) - Parsed args: Namespace(argv=['mednist_app/mednist_classifier_monaideploy.py', '-i', 'input', '-o', 'output', '-m', 'models', '-l', 'DEBUG'], input=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/input'), log_level='DEBUG', model=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models'), output=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output'), workdir=None)\n", - "[2023-11-15 18:38:17,979] [INFO] (root) - AppContext object: AppContext(input_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/input, output_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output, model_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models, workdir=)\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:210] Creating context\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1741] Activating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1771] Running Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 16:25:46,989] [INFO] (root) - Parsed args: Namespace(log_level='DEBUG', input=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/input'), output=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output'), model=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models'), workdir=None, argv=['mednist_app/mednist_classifier_monaideploy.py', '-i', 'input', '-o', 'output', '-m', 'models', '-l', 'DEBUG'])\n", + "[2024-04-10 16:25:46,993] [INFO] (root) - AppContext object: AppContext(input_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/input, output_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output, model_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models, workdir=)\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:211] Creating context\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1864] Activating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1894] Running Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:190] Scheduling 3 entities\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", " return torch.as_tensor(x, *args, **_kwargs).as_subclass(cls)\n", "AbdomenCT\n", - "[2023-11-15 18:38:19,019] [DEBUG] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - Writing DICOM object...\n", + "[2024-04-10 16:25:49,239] [DEBUG] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - Writing DICOM object...\n", "\n", - "[2023-11-15 18:38:19,019] [DEBUG] (root) - Writing DICOM common modules...\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", + "[2024-04-10 16:25:49,239] [DEBUG] (root) - Writing DICOM common modules...\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", " warnings.warn(msg)\n", - "[2023-11-15 18:38:19,021] [DEBUG] (root) - DICOM common modules written:\n", + "[2024-04-10 16:25:49,242] [DEBUG] (root) - DICOM common modules written:\n", "Dataset.file_meta -------------------------------\n", "(0002, 0000) File Meta Information Group Length UL: 198\n", "(0002, 0001) File Meta Information Version OB: b'01'\n", "(0002, 0002) Media Storage SOP Class UID UI: Basic Text SR Storage\n", - "(0002, 0003) Media Storage SOP Instance UID UI: 1.2.826.0.1.3680043.8.498.67182506684910194313532021844767536558\n", + "(0002, 0003) Media Storage SOP Instance UID UI: 1.2.826.0.1.3680043.8.498.10219575881227434107206425977009168141\n", "(0002, 0010) Transfer Syntax UID UI: Implicit VR Little Endian\n", "(0002, 0012) Implementation Class UID UI: 1.2.40.0.13.1.1.1\n", - "(0002, 0013) Implementation Version Name SH: '0.6.0'\n", + "(0002, 0013) Implementation Version Name SH: '0.5.1+25.g31e41'\n", "-------------------------------------------------\n", "(0008, 0005) Specific Character Set CS: 'ISO_IR 100'\n", - "(0008, 0012) Instance Creation Date DA: '20231115'\n", - "(0008, 0013) Instance Creation Time TM: '183819'\n", + "(0008, 0012) Instance Creation Date DA: '20240410'\n", + "(0008, 0013) Instance Creation Time TM: '162549'\n", "(0008, 0016) SOP Class UID UI: Basic Text SR Storage\n", - "(0008, 0018) SOP Instance UID UI: 1.2.826.0.1.3680043.8.498.67182506684910194313532021844767536558\n", - "(0008, 0020) Study Date DA: '20231115'\n", - "(0008, 0021) Series Date DA: '20231115'\n", - "(0008, 0023) Content Date DA: '20231115'\n", - "(0008, 002a) Acquisition DateTime DT: '20231115183819'\n", - "(0008, 0030) Study Time TM: '183819'\n", - "(0008, 0031) Series Time TM: '183819'\n", - "(0008, 0033) Content Time TM: '183819'\n", + "(0008, 0018) SOP Instance UID UI: 1.2.826.0.1.3680043.8.498.10219575881227434107206425977009168141\n", + "(0008, 0020) Study Date DA: '20240410'\n", + "(0008, 0021) Series Date DA: '20240410'\n", + "(0008, 0023) Content Date DA: '20240410'\n", + "(0008, 002a) Acquisition DateTime DT: '20240410162549'\n", + "(0008, 0030) Study Time TM: '162549'\n", + "(0008, 0031) Series Time TM: '162549'\n", + "(0008, 0033) Content Time TM: '162549'\n", "(0008, 0050) Accession Number SH: ''\n", "(0008, 0060) Modality CS: 'SR'\n", "(0008, 0070) Manufacturer LO: 'MOANI Deploy App SDK'\n", "(0008, 0090) Referring Physician's Name PN: ''\n", - "(0008, 0201) Timezone Offset From UTC SH: '-0800'\n", + "(0008, 0201) Timezone Offset From UTC SH: '-0700'\n", "(0008, 1030) Study Description LO: 'AI results.'\n", "(0008, 103e) Series Description LO: 'CAUTION: Not for Diagnostic Use, for research use only.'\n", "(0008, 1090) Manufacturer's Model Name LO: 'DICOM SR Writer'\n", @@ -1649,7 +1735,7 @@ "(0010, 0030) Patient's Birth Date DA: ''\n", "(0010, 0040) Patient's Sex CS: ''\n", "(0018, 0015) Body Part Examined CS: ''\n", - "(0018, 1020) Software Versions LO: '0.6.0'\n", + "(0018, 1020) Software Versions LO: '0.5.1+25.g31e41'\n", "(0018, a001) Contributing Equipment Sequence 1 item(s) ---- \n", " (0008, 0070) Manufacturer LO: 'MONAI WG Trainer'\n", " (0008, 1090) Manufacturer's Model Name LO: 'MEDNIST Classifier'\n", @@ -1661,38 +1747,38 @@ " (0008, 0104) Code Meaning LO: '\"Processing Algorithm'\n", " ---------\n", " ---------\n", - "(0020, 000d) Study Instance UID UI: 1.2.826.0.1.3680043.8.498.12379609192731250420244173376142712446\n", - "(0020, 000e) Series Instance UID UI: 1.2.826.0.1.3680043.8.498.73656010492290419012392741095771197196\n", + "(0020, 000d) Study Instance UID UI: 1.2.826.0.1.3680043.8.498.99895869772559254532078552857455521250\n", + "(0020, 000e) Series Instance UID UI: 1.2.826.0.1.3680043.8.498.11097411469528473023282906105369639673\n", "(0020, 0010) Study ID SH: '1'\n", - "(0020, 0011) Series Number IS: '9783'\n", + "(0020, 0011) Series Number IS: '7823'\n", "(0020, 0013) Instance Number IS: '1'\n", "(0040, 1001) Requested Procedure ID SH: ''\n", - "[2023-11-15 18:38:19,022] [DEBUG] (root) - DICOM dataset to be written:Dataset.file_meta -------------------------------\n", + "[2024-04-10 16:25:49,242] [DEBUG] (root) - DICOM dataset to be written:Dataset.file_meta -------------------------------\n", "(0002, 0000) File Meta Information Group Length UL: 198\n", "(0002, 0001) File Meta Information Version OB: b'01'\n", "(0002, 0002) Media Storage SOP Class UID UI: Basic Text SR Storage\n", - "(0002, 0003) Media Storage SOP Instance UID UI: 1.2.826.0.1.3680043.8.498.67182506684910194313532021844767536558\n", + "(0002, 0003) Media Storage SOP Instance UID UI: 1.2.826.0.1.3680043.8.498.10219575881227434107206425977009168141\n", "(0002, 0010) Transfer Syntax UID UI: Implicit VR Little Endian\n", "(0002, 0012) Implementation Class UID UI: 1.2.40.0.13.1.1.1\n", - "(0002, 0013) Implementation Version Name SH: '0.6.0'\n", + "(0002, 0013) Implementation Version Name SH: '0.5.1+25.g31e41'\n", "-------------------------------------------------\n", "(0008, 0005) Specific Character Set CS: 'ISO_IR 100'\n", - "(0008, 0012) Instance Creation Date DA: '20231115'\n", - "(0008, 0013) Instance Creation Time TM: '183819'\n", + "(0008, 0012) Instance Creation Date DA: '20240410'\n", + "(0008, 0013) Instance Creation Time TM: '162549'\n", "(0008, 0016) SOP Class UID UI: Basic Text SR Storage\n", - "(0008, 0018) SOP Instance UID UI: 1.2.826.0.1.3680043.8.498.67182506684910194313532021844767536558\n", - "(0008, 0020) Study Date DA: '20231115'\n", - "(0008, 0021) Series Date DA: '20231115'\n", - "(0008, 0023) Content Date DA: '20231115'\n", - "(0008, 002a) Acquisition DateTime DT: '20231115183819'\n", - "(0008, 0030) Study Time TM: '183819'\n", - "(0008, 0031) Series Time TM: '183819'\n", - "(0008, 0033) Content Time TM: '183819'\n", + "(0008, 0018) SOP Instance UID UI: 1.2.826.0.1.3680043.8.498.10219575881227434107206425977009168141\n", + "(0008, 0020) Study Date DA: '20240410'\n", + "(0008, 0021) Series Date DA: '20240410'\n", + "(0008, 0023) Content Date DA: '20240410'\n", + "(0008, 002a) Acquisition DateTime DT: '20240410162549'\n", + "(0008, 0030) Study Time TM: '162549'\n", + "(0008, 0031) Series Time TM: '162549'\n", + "(0008, 0033) Content Time TM: '162549'\n", "(0008, 0050) Accession Number SH: ''\n", "(0008, 0060) Modality CS: 'SR'\n", "(0008, 0070) Manufacturer LO: 'MOANI Deploy App SDK'\n", "(0008, 0090) Referring Physician's Name PN: ''\n", - "(0008, 0201) Timezone Offset From UTC SH: '-0800'\n", + "(0008, 0201) Timezone Offset From UTC SH: '-0700'\n", "(0008, 1030) Study Description LO: 'AI results.'\n", "(0008, 103e) Series Description LO: 'Not for clinical use. The result is for research use only.'\n", "(0008, 1090) Manufacturer's Model Name LO: 'DICOM SR Writer'\n", @@ -1702,7 +1788,7 @@ "(0010, 0030) Patient's Birth Date DA: ''\n", "(0010, 0040) Patient's Sex CS: ''\n", "(0018, 0015) Body Part Examined CS: ''\n", - "(0018, 1020) Software Versions LO: '0.6.0'\n", + "(0018, 1020) Software Versions LO: '0.5.1+25.g31e41'\n", "(0018, a001) Contributing Equipment Sequence 1 item(s) ---- \n", " (0008, 0070) Manufacturer LO: 'MONAI WG Trainer'\n", " (0008, 1090) Manufacturer's Model Name LO: 'MEDNIST Classifier'\n", @@ -1714,10 +1800,10 @@ " (0008, 0104) Code Meaning LO: '\"Processing Algorithm'\n", " ---------\n", " ---------\n", - "(0020, 000d) Study Instance UID UI: 1.2.826.0.1.3680043.8.498.12379609192731250420244173376142712446\n", - "(0020, 000e) Series Instance UID UI: 1.2.826.0.1.3680043.8.498.73656010492290419012392741095771197196\n", + "(0020, 000d) Study Instance UID UI: 1.2.826.0.1.3680043.8.498.99895869772559254532078552857455521250\n", + "(0020, 000e) Series Instance UID UI: 1.2.826.0.1.3680043.8.498.11097411469528473023282906105369639673\n", "(0020, 0010) Study ID SH: '1'\n", - "(0020, 0011) Series Number IS: '9783'\n", + "(0020, 0011) Series Number IS: '7823'\n", "(0020, 0013) Instance Number IS: '1'\n", "(0040, 1001) Requested Procedure ID SH: ''\n", "(0040, a040) Value Type CS: 'CONTAINER'\n", @@ -1738,14 +1824,14 @@ " ---------\n", " (0040, a160) Text Value UT: 'AbdomenCT'\n", " ---------\n", - "[2023-11-15 18:38:19,026] [INFO] (root) - Finished writing DICOM instance to file /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/1.2.826.0.1.3680043.8.498.67182506684910194313532021844767536558.dcm\n", - "[2023-11-15 18:38:19,027] [INFO] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - DICOM SOP instance saved in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/1.2.826.0.1.3680043.8.498.67182506684910194313532021844767536558.dcm\n", + "[2024-04-10 16:25:49,245] [INFO] (root) - Finished writing DICOM instance to file /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/1.2.826.0.1.3680043.8.498.10219575881227434107206425977009168141.dcm\n", + "[2024-04-10 16:25:49,246] [INFO] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - DICOM SOP instance saved in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/1.2.826.0.1.3680043.8.498.10219575881227434107206425977009168141.dcm\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:229] Destroying context\n" + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:230] Destroying context\n" ] } ], @@ -1857,7 +1943,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/notebooks/tutorials/02_mednist_app.ipynb b/notebooks/tutorials/02_mednist_app.ipynb index 703d3b54..595cb471 100644 --- a/notebooks/tutorials/02_mednist_app.ipynb +++ b/notebooks/tutorials/02_mednist_app.ipynb @@ -29,17 +29,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"\", line 1, in \n", - "ModuleNotFoundError: No module named 'ignite'\n" - ] - } - ], + "outputs": [], "source": [ "# Install necessary packages for MONAI Core\n", "!python -c \"import monai\" || pip install -q \"monai[pillow, tqdm]\"\n", @@ -71,23 +61,23 @@ "output_type": "stream", "text": [ "MONAI version: 1.3.0\n", - "Numpy version: 1.24.4\n", - "Pytorch version: 2.1.1+cu121\n", + "Numpy version: 1.26.4\n", + "Pytorch version: 2.0.1+cu117\n", "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", "MONAI rev id: 865972f7a791bf7b42efbcd87c8402bd865b329e\n", - "MONAI __file__: /home//src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/__init__.py\n", + "MONAI __file__: /home//src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/monai/__init__.py\n", "\n", "Optional dependencies:\n", "Pytorch Ignite version: 0.4.11\n", "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n", - "Nibabel version: 5.1.0\n", - "scikit-image version: 0.21.0\n", - "scipy version: 1.10.1\n", - "Pillow version: 10.0.1\n", + "Nibabel version: 5.2.1\n", + "scikit-image version: 0.22.0\n", + "scipy version: 1.13.0\n", + "Pillow version: 10.3.0\n", "Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n", - "gdown version: 4.7.1\n", + "gdown version: 4.7.3\n", "TorchVision version: NOT INSTALLED or UNKNOWN VERSION.\n", - "tqdm version: 4.66.1\n", + "tqdm version: 4.66.2\n", "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n", "psutil version: 5.9.6\n", "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n", @@ -173,7 +163,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/tmp/tmpjh72rafb\n" + "/tmp/tmpz1q9ch5_\n" ] }, { @@ -181,19 +171,17 @@ "output_type": "stream", "text": [ "Downloading...\n", - "From (uriginal): https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE\n", - "From (redirected): https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE&confirm=t&uuid=8946f974-8b80-4bd3-8696-ac8716b357ed\n", - "To: /tmp/tmpv4hps2d5/MedNIST.tar.gz\n", - "100%|██████████| 61.8M/61.8M [00:02<00:00, 26.6MB/s]" + "From (original): https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE\n", + "From (redirected): https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE&confirm=t&uuid=6052caf3-cb8c-4cb3-b8b8-804d8dc90e06\n", + "To: /tmp/tmpq9pcg2c8/MedNIST.tar.gz\n", + "100%|██████████| 61.8M/61.8M [00:00<00:00, 81.1MB/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-15 19:10:32,776 - INFO - Downloaded: /tmp/tmpjh72rafb/MedNIST.tar.gz\n", - "2023-11-15 19:10:32,883 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", - "2023-11-15 19:10:32,884 - INFO - Writing into directory: /tmp/tmpjh72rafb.\n" + "2024-04-10 16:28:05,802 - INFO - Downloaded: /tmp/tmpz1q9ch5_/MedNIST.tar.gz\n" ] }, { @@ -202,6 +190,14 @@ "text": [ "\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-10 16:28:05,912 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2024-04-10 16:28:05,914 - INFO - Writing into directory: /tmp/tmpz1q9ch5_.\n" + ] } ], "source": [ @@ -326,11 +322,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/5 Loss: 0.1891738623380661\n", - "Epoch 2/5 Loss: 0.06714393198490143\n", - "Epoch 3/5 Loss: 0.028867393732070923\n", - "Epoch 4/5 Loss: 0.0186357069760561\n", - "Epoch 5/5 Loss: 0.0193067267537117\n" + "Epoch 1/5 Loss: 0.18928290903568268\n", + "Epoch 2/5 Loss: 0.06710730493068695\n", + "Epoch 3/5 Loss: 0.029032323509454727\n", + "Epoch 4/5 Loss: 0.01877668686211109\n", + "Epoch 5/5 Loss: 0.01939055137336254\n" ] } ], @@ -773,21 +769,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-11-15 19:18:17,922] [INFO] (root) - Parsed args: Namespace(argv=[], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-11-15 19:18:17,941] [INFO] (root) - AppContext object: AppContext(input_path=input, output_path=output, model_path=models, workdir=)\n", - "[info] [gxf_executor.cpp:210] Creating context\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 16:35:53,768] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])\n", + "[2024-04-10 16:35:53,778] [INFO] (root) - AppContext object: AppContext(input_path=input, output_path=output, model_path=models, workdir=)\n", + "[info] [gxf_executor.cpp:211] Creating context\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[info] [greedy_scheduler.cpp:190] Scheduling 3 entities\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", " return torch.as_tensor(x, *args, **_kwargs).as_subclass(cls)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", " warnings.warn(msg)\n", - "[2023-11-15 19:18:19,246] [INFO] (root) - Finished writing DICOM instance to file output/1.2.826.0.1.3680043.8.498.89399783846974532553567524226806601923.dcm\n", - "[2023-11-15 19:18:19,249] [INFO] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - DICOM SOP instance saved in output/1.2.826.0.1.3680043.8.498.89399783846974532553567524226806601923.dcm\n" + "[2024-04-10 16:35:54,545] [INFO] (root) - Finished writing DICOM instance to file output/1.2.826.0.1.3680043.8.498.91196297255331853052707757292596626343.dcm\n", + "[2024-04-10 16:35:54,548] [INFO] (monai.deploy.operators.dicom_text_sr_writer_operator.DICOMTextSRWriterOperator) - DICOM SOP instance saved in output/1.2.826.0.1.3680043.8.498.91196297255331853052707757292596626343.dcm\n" ] }, { @@ -803,10 +799,10 @@ "text": [ "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[info] [gxf_executor.cpp:229] Destroying context\n" + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[info] [gxf_executor.cpp:230] Destroying context\n" ] } ], @@ -1125,24 +1121,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:210] Creating context\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1741] Activating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1771] Running Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:211] Creating context\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1864] Activating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1894] Running Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:190] Scheduling 3 entities\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", " return torch.as_tensor(x, *args, **_kwargs).as_subclass(cls)\n", "AbdomenCT\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", " warnings.warn(msg)\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:229] Destroying context\n" + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:230] Destroying context\n" ] } ], @@ -1245,14 +1241,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-11-15 19:18:32,397] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/mednist_app/mednist_classifier_monaideploy.py\n", - "[2023-11-15 19:18:32,397] [INFO] (packager.parameters) - Detected application type: Python File\n", - "[2023-11-15 19:18:32,397] [INFO] (packager) - Scanning for models in {models_path}...\n", - "[2023-11-15 19:18:32,397] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.\n", - "[2023-11-15 19:18:32,397] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/mednist_app/app.yaml...\n", - "[2023-11-15 19:18:32,398] [INFO] (packager) - Generating app.json...\n", - "[2023-11-15 19:18:32,399] [INFO] (packager) - Generating pkg.json...\n", - "[2023-11-15 19:18:32,400] [DEBUG] (common) - \n", + "[2024-04-10 16:36:05,007] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/mednist_app/mednist_classifier_monaideploy.py\n", + "[2024-04-10 16:36:05,007] [INFO] (packager.parameters) - Detected application type: Python File\n", + "[2024-04-10 16:36:05,007] [INFO] (packager) - Scanning for models in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models...\n", + "[2024-04-10 16:36:05,007] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.\n", + "[2024-04-10 16:36:05,007] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/mednist_app/app.yaml...\n", + "[2024-04-10 16:36:05,009] [INFO] (packager) - Generating app.json...\n", + "[2024-04-10 16:36:05,009] [INFO] (packager) - Generating pkg.json...\n", + "[2024-04-10 16:36:05,015] [DEBUG] (common) - \n", "=============== Begin app.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", @@ -1280,21 +1276,21 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1.0,\n", " \"workingDirectory\": \"/var/holoscan\"\n", "}\n", "================ End app.json ================\n", " \n", - "[2023-11-15 19:18:32,400] [DEBUG] (common) - \n", + "[2024-04-10 16:36:05,015] [DEBUG] (common) - \n", "=============== Begin pkg.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", " \"applicationRoot\": \"/opt/holoscan/app\",\n", " \"modelRoot\": \"/opt/holoscan/models\",\n", " \"models\": {\n", - " \"model\": \"/opt/holoscan/models\"\n", + " \"model\": \"/opt/holoscan/models/model\"\n", " },\n", " \"resources\": {\n", " \"cpu\": 1,\n", @@ -1302,15 +1298,16 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"1Gi\"\n", " },\n", - " \"version\": 1.0\n", + " \"version\": 1.0,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "================ End pkg.json ================\n", " \n", - "[2023-11-15 19:18:32,429] [DEBUG] (packager.builder) - \n", + "[2024-04-10 16:36:05,050] [DEBUG] (packager.builder) - \n", "========== Begin Dockerfile ==========\n", "\n", "\n", - "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", + "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", "\n", "ENV DEBIAN_FRONTEND=noninteractive\n", "ENV TERM=xterm-256color\n", @@ -1326,11 +1323,13 @@ " && mkdir -p /var/holoscan/input \\\n", " && mkdir -p /var/holoscan/output\n", "\n", - "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\"\n", + "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\"\n", "LABEL tag=\"mednist_app:1.0\"\n", "LABEL org.opencontainers.image.title=\"MONAI Deploy App Package - MedNIST Classifier App\"\n", "LABEL org.opencontainers.image.version=\"1.0\"\n", - "LABEL org.nvidia.holoscan=\"0.6.0\"\n", + "LABEL org.nvidia.holoscan=\"1.0.3\"\n", + "LABEL org.monai.deploy.app-sdk=\"0.5.1\"\n", + "\n", "\n", "ENV HOLOSCAN_ENABLE_HEALTH_CHECK=true\n", "ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input\n", @@ -1355,7 +1354,7 @@ "\n", "\n", "\n", - "RUN groupadd -g $GID $UNAME\n", + "RUN groupadd -f -g $GID $UNAME\n", "RUN useradd -rm -d /home/$UNAME -s /bin/bash -g $GID -G sudo -u $UID $UNAME\n", "RUN chown -R holoscan /var/holoscan \n", "RUN chown -R holoscan /var/holoscan/input \n", @@ -1380,12 +1379,12 @@ "RUN pip install --upgrade pip\n", "RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "\n", - "# Install Holoscan from PyPI org\n", - "RUN pip install holoscan==0.6.0\n", + "# Install Holoscan from PyPI only when sdk_type is Holoscan. \n", + "# For MONAI Deploy, the APP SDK will install it unless user specifies the Holoscan SDK file.\n", "\n", - "\n", - "# Install MONAI Deploy from PyPI org\n", - "RUN pip install monai-deploy-app-sdk==0.6.0\n", + "# Copy user-specified MONAI Deploy SDK file\n", + "COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", "\n", "\n", "\n", @@ -1401,269 +1400,151 @@ "ENTRYPOINT [\"/var/holoscan/tools\"]\n", "=========== End Dockerfile ===========\n", "\n", - "[2023-11-15 19:18:32,429] [INFO] (packager.builder) - \n", + "[2024-04-10 16:36:05,050] [INFO] (packager.builder) - \n", "===============================================================================\n", "Building image for: x64-workstation\n", " Architecture: linux/amd64\n", - " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - " Build Image: N/A \n", + " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + " Build Image: N/A\n", " Cache: Enabled\n", " Configuration: dgpu\n", - " Holoiscan SDK Package: pypi.org\n", - " MONAI Deploy App SDK Package: pypi.org\n", + " Holoscan SDK Package: pypi.org\n", + " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", " gRPC Health Probe: N/A\n", - " SDK Version: 0.6.0\n", + " SDK Version: 1.0.3\n", " SDK: monai-deploy\n", " Tag: mednist_app-x64-workstation-dgpu-linux-amd64:1.0\n", " \n", - "[2023-11-15 19:18:32,690] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", - "[2023-11-15 19:18:32,691] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=mednist_app-x64-workstation-dgpu-linux-amd64:1.0\n", + "[2024-04-10 16:36:05,416] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", + "[2024-04-10 16:36:05,416] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=mednist_app-x64-workstation-dgpu-linux-amd64:1.0\n", "#0 building with \"holoscan_app_builder\" instance using docker-container driver\n", "\n", - "#1 [internal] load .dockerignore\n", - "#1 transferring context: 1.79kB done\n", + "#1 [internal] load build definition from Dockerfile\n", + "#1 transferring dockerfile: 2.81kB done\n", "#1 DONE 0.1s\n", "\n", - "#2 [internal] load build definition from Dockerfile\n", - "#2 transferring dockerfile: 2.49kB done\n", - "#2 DONE 0.1s\n", + "#2 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#2 DONE 0.5s\n", "\n", - "#3 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#3 DONE 0.4s\n", + "#3 [internal] load .dockerignore\n", + "#3 transferring context: 1.79kB done\n", + "#3 DONE 0.1s\n", "\n", - "#4 [internal] load build context\n", - "#4 DONE 0.0s\n", + "#4 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#4 ...\n", "\n", - "#5 importing cache manifest from local:12435489437730595250\n", + "#5 [internal] load build context\n", "#5 DONE 0.0s\n", "\n", - "#6 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#6 DONE 0.7s\n", + "#6 importing cache manifest from local:12491137658764693548\n", + "#6 inferred cache manifest type: application/vnd.oci.image.index.v1+json done\n", + "#6 DONE 0.0s\n", + "\n", + "#7 [ 1/21] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155\n", + "#7 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155 0.0s done\n", + "#7 DONE 0.0s\n", "\n", - "#7 [ 1/21] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc\n", - "#7 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc 0.1s done\n", - "#7 DONE 0.1s\n", + "#4 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#4 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done\n", + "#4 DONE 0.5s\n", "\n", - "#4 [internal] load build context\n", - "#4 transferring context: 28.62MB 0.2s done\n", - "#4 DONE 0.3s\n", + "#5 [internal] load build context\n", + "#5 transferring context: 28.75MB 0.2s done\n", + "#5 DONE 0.2s\n", "\n", - "#8 [ 6/21] RUN chown -R holoscan /var/holoscan\n", + "#8 [13/21] RUN pip install --upgrade pip\n", "#8 CACHED\n", "\n", - "#9 [ 7/21] RUN chown -R holoscan /var/holoscan/input\n", + "#9 [10/21] COPY ./tools /var/holoscan/tools\n", "#9 CACHED\n", "\n", "#10 [ 9/21] WORKDIR /var/holoscan\n", "#10 CACHED\n", "\n", - "#11 [ 2/21] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", + "#11 [ 7/21] RUN chown -R holoscan /var/holoscan/input\n", "#11 CACHED\n", "\n", - "#12 [ 3/21] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", + "#12 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", "#12 CACHED\n", "\n", - "#13 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", + "#13 [ 8/21] RUN chown -R holoscan /var/holoscan/output\n", "#13 CACHED\n", "\n", - "#14 [10/21] COPY ./tools /var/holoscan/tools\n", + "#14 [ 3/21] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", "#14 CACHED\n", "\n", - "#15 [ 4/21] RUN groupadd -g 1000 holoscan\n", + "#15 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt\n", "#15 CACHED\n", "\n", - "#16 [ 8/21] RUN chown -R holoscan /var/holoscan/output\n", + "#16 [11/21] RUN chmod +x /var/holoscan/tools\n", "#16 CACHED\n", "\n", - "#17 [11/21] RUN chmod +x /var/holoscan/tools\n", + "#17 [ 2/21] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", "#17 CACHED\n", "\n", - "#18 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt\n", - "#18 DONE 0.4s\n", - "\n", - "#19 [13/21] RUN pip install --upgrade pip\n", - "#19 1.132 Defaulting to user installation because normal site-packages is not writeable\n", - "#19 1.214 Requirement already satisfied: pip in /usr/local/lib/python3.8/dist-packages (22.0.4)\n", - "#19 1.417 Collecting pip\n", - "#19 1.467 Downloading pip-23.3.1-py3-none-any.whl (2.1 MB)\n", - "#19 1.538 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 32.7 MB/s eta 0:00:00\n", - "#19 1.658 Installing collected packages: pip\n", - "#19 2.774 Successfully installed pip-23.3.1\n", - "#19 2.906 WARNING: You are using pip version 22.0.4; however, version 23.3.1 is available.\n", - "#19 2.906 You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.\n", - "#19 DONE 3.1s\n", + "#18 [ 4/21] RUN groupadd -f -g 1000 holoscan\n", + "#18 CACHED\n", + "\n", + "#19 [ 6/21] RUN chown -R holoscan /var/holoscan\n", + "#19 CACHED\n", "\n", "#20 [14/21] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", - "#20 0.781 Collecting monai>=1.2.0 (from -r /tmp/requirements.txt (line 1))\n", - "#20 0.810 Downloading monai-1.3.0-202310121228-py3-none-any.whl.metadata (10 kB)\n", - "#20 1.152 Collecting Pillow>=8.4.0 (from -r /tmp/requirements.txt (line 2))\n", - "#20 1.165 Downloading Pillow-10.1.0-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (9.5 kB)\n", - "#20 1.216 Collecting pydicom>=2.3.0 (from -r /tmp/requirements.txt (line 3))\n", - "#20 1.227 Downloading pydicom-2.4.3-py3-none-any.whl.metadata (7.8 kB)\n", - "#20 1.342 Collecting highdicom>=0.18.2 (from -r /tmp/requirements.txt (line 4))\n", - "#20 1.351 Downloading highdicom-0.22.0-py3-none-any.whl.metadata (3.8 kB)\n", - "#20 1.430 Collecting SimpleITK>=2.0.0 (from -r /tmp/requirements.txt (line 5))\n", - "#20 1.439 Downloading SimpleITK-2.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.9 kB)\n", - "#20 1.777 Collecting setuptools>=59.5.0 (from -r /tmp/requirements.txt (line 6))\n", - "#20 1.785 Downloading setuptools-68.2.2-py3-none-any.whl.metadata (6.3 kB)\n", - "#20 1.912 Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.8/dist-packages (from monai>=1.2.0->-r /tmp/requirements.txt (line 1)) (1.22.3)\n", - "#20 1.972 Collecting torch>=1.9 (from monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 1.983 Downloading torch-2.1.1-cp38-cp38-manylinux1_x86_64.whl.metadata (25 kB)\n", - "#20 2.163 Collecting pillow-jpls>=1.0 (from highdicom>=0.18.2->-r /tmp/requirements.txt (line 4))\n", - "#20 2.182 Downloading pillow_jpls-1.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (340 kB)\n", - "#20 2.197 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 340.3/340.3 kB 76.9 MB/s eta 0:00:00\n", - "#20 2.400 Collecting filelock (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 2.411 Downloading filelock-3.13.1-py3-none-any.whl.metadata (2.8 kB)\n", - "#20 2.415 Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1)) (4.7.1)\n", - "#20 2.467 Collecting sympy (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 2.477 Downloading sympy-1.12-py3-none-any.whl (5.7 MB)\n", - "#20 2.531 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.7/5.7 MB 114.1 MB/s eta 0:00:00\n", - "#20 2.617 Collecting networkx (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 2.626 Downloading networkx-3.1-py3-none-any.whl (2.1 MB)\n", - "#20 2.652 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 95.7 MB/s eta 0:00:00\n", - "#20 2.668 Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1)) (3.1.2)\n", - "#20 2.721 Collecting fsspec (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 2.730 Downloading fsspec-2023.10.0-py3-none-any.whl.metadata (6.8 kB)\n", - "#20 2.760 Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 2.772 Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n", - "#20 2.981 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 23.7/23.7 MB 116.4 MB/s eta 0:00:00\n", - "#20 3.068 Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 3.109 Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n", - "#20 3.124 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 823.6/823.6 kB 94.7 MB/s eta 0:00:00\n", - "#20 3.163 Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 3.176 Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n", - "#20 3.309 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.1/14.1 MB 109.7 MB/s eta 0:00:00\n", - "#20 3.378 Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 3.386 Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n", - "#20 3.416 Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 3.430 Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n", - "#20 7.176 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 410.6/410.6 MB 107.3 MB/s eta 0:00:00\n", - "#20 8.220 Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 8.233 Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n", - "#20 9.379 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 121.6/121.6 MB 109.4 MB/s eta 0:00:00\n", - "#20 9.783 Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 9.793 Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n", - "#20 10.31 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 56.5/56.5 MB 116.7 MB/s eta 0:00:00\n", - "#20 10.48 Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 10.49 Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n", - "#20 11.57 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 124.2/124.2 MB 124.0 MB/s eta 0:00:00\n", - "#20 11.93 Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 11.94 Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n", - "#20 13.68 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 196.0/196.0 MB 112.6 MB/s eta 0:00:00\n", - "#20 14.22 Collecting nvidia-nccl-cu12==2.18.1 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 14.23 Downloading nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl (209.8 MB)\n", - "#20 16.25 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 209.8/209.8 MB 110.7 MB/s eta 0:00:00\n", - "#20 16.82 Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 16.83 Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n", - "#20 16.84 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 99.1/99.1 kB 177.1 MB/s eta 0:00:00\n", - "#20 16.88 Collecting triton==2.1.0 (from torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 16.89 Downloading triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB)\n", - "#20 16.94 Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 16.95 Downloading nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n", - "#20 17.06 Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.8/dist-packages (from jinja2->torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1)) (2.1.1)\n", - "#20 17.16 Collecting mpmath>=0.19 (from sympy->torch>=1.9->monai>=1.2.0->-r /tmp/requirements.txt (line 1))\n", - "#20 17.17 Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)\n", - "#20 17.18 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 kB 171.3 MB/s eta 0:00:00\n", - "#20 17.28 Downloading monai-1.3.0-202310121228-py3-none-any.whl (1.3 MB)\n", - "#20 17.30 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.3/1.3 MB 106.8 MB/s eta 0:00:00\n", - "#20 17.31 Downloading Pillow-10.1.0-cp38-cp38-manylinux_2_28_x86_64.whl (3.6 MB)\n", - "#20 17.36 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.6/3.6 MB 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MB)\n", - "#20 31.44 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 731.7/731.7 MB 65.6 MB/s eta 0:00:00\n", - "#20 31.46 Downloading triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89.2 MB)\n", - "#20 32.91 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 89.2/89.2 MB 90.3 MB/s eta 0:00:00\n", - "#20 32.92 Downloading filelock-3.13.1-py3-none-any.whl (11 kB)\n", - "#20 32.93 Downloading fsspec-2023.10.0-py3-none-any.whl (166 kB)\n", - "#20 32.94 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 166.4/166.4 kB 69.7 MB/s eta 0:00:00\n", - "#20 32.96 Downloading nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl (20.5 MB)\n", - "#20 34.08 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 20.5/20.5 MB 19.1 MB/s eta 0:00:00\n", - "#20 38.74 Installing collected packages: SimpleITK, mpmath, sympy, setuptools, pydicom, Pillow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, fsspec, filelock, triton, pillow-jpls, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, highdicom, torch, monai\n", - "#20 85.54 Successfully installed Pillow-10.1.0 SimpleITK-2.3.1 filelock-3.13.1 fsspec-2023.10.0 highdicom-0.22.0 monai-1.3.0 mpmath-1.3.0 networkx-3.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.3.101 nvidia-nvtx-cu12-12.1.105 pillow-jpls-1.2.0 pydicom-2.4.3 setuptools-68.2.2 sympy-1.12 torch-2.1.1 triton-2.1.0\n", - "#20 DONE 87.6s\n", - "\n", - "#21 [15/21] RUN pip install holoscan==0.6.0\n", - "#21 0.757 Defaulting to user installation because normal site-packages is not writeable\n", - "#21 1.044 Collecting holoscan==0.6.0\n", - "#21 1.084 Downloading holoscan-0.6.0-cp38-cp38-manylinux2014_x86_64.whl.metadata (4.4 kB)\n", - "#21 1.124 Requirement already satisfied: cloudpickle~=2.2 in /usr/local/lib/python3.8/dist-packages (from holoscan==0.6.0) (2.2.1)\n", - "#21 1.126 Requirement already satisfied: python-on-whales~=0.60 in /usr/local/lib/python3.8/dist-packages (from holoscan==0.6.0) (0.63.0)\n", - "#21 1.128 Requirement already satisfied: Jinja2~=3.1 in /usr/local/lib/python3.8/dist-packages (from holoscan==0.6.0) (3.1.2)\n", - "#21 1.130 Requirement already satisfied: packaging~=23.1 in /usr/local/lib/python3.8/dist-packages (from holoscan==0.6.0) (23.1)\n", - "#21 1.131 Requirement already satisfied: pyyaml~=6.0 in /usr/local/lib/python3.8/dist-packages (from holoscan==0.6.0) (6.0.1)\n", - "#21 1.132 Requirement already satisfied: requests~=2.28 in /usr/local/lib/python3.8/dist-packages (from holoscan==0.6.0) (2.31.0)\n", - "#21 1.134 Requirement already satisfied: pip>=20.2 in 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/usr/local/lib/python3.8/dist-packages (from typer>=0.4.1->python-on-whales~=0.60->holoscan~=0.6.0->monai-deploy-app-sdk==0.6.0) (8.1.6)\n", - "#22 1.945 Installing collected packages: zipp, colorama, importlib-metadata, typeguard, monai-deploy-app-sdk\n", - "#22 2.185 Successfully installed colorama-0.4.6 importlib-metadata-6.8.0 monai-deploy-app-sdk-0.6.0 typeguard-4.1.5 zipp-3.17.0\n", - "#22 2.190 WARNING: You are using pip version 22.0.4; however, version 23.3.1 is available.\n", - "#22 2.190 You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.\n", - "#22 DONE 2.4s\n", + "#20 CACHED\n", + "\n", + "#21 [15/21] COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#21 DONE 0.3s\n", + "\n", + "#22 [16/21] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#22 0.701 Defaulting to user installation because normal site-packages is not writeable\n", + "#22 0.799 Processing /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#22 0.810 Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (1.23.5)\n", + "#22 0.996 Collecting holoscan~=1.0 (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.066 Downloading holoscan-1.0.3-cp310-cp310-manylinux_2_35_x86_64.whl.metadata (4.1 kB)\n", + "#22 1.137 Collecting colorama>=0.4.1 (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.141 Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n", + "#22 1.222 Collecting typeguard>=3.0.0 (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.227 Downloading typeguard-4.2.1-py3-none-any.whl.metadata (3.7 kB)\n", + "#22 1.322 Collecting pip==23.3.2 (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.327 Downloading 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monai-deploy-app-sdk\n", + "#22 2.915 Attempting uninstall: pip\n", + "#22 2.916 Found existing installation: pip 24.0\n", + "#22 2.967 Uninstalling pip-24.0:\n", + "#22 3.365 Successfully uninstalled pip-24.0\n", + "#22 5.013 Successfully installed colorama-0.4.6 holoscan-1.0.3 monai-deploy-app-sdk-0.5.1+25.g31e4165.dirty pip-23.3.2 typeguard-4.2.1 wheel-axle-runtime-0.0.5\n", + "#22 DONE 5.6s\n", "\n", "#23 [17/21] COPY ./models /opt/holoscan/models\n", - "#23 DONE 0.2s\n", + "#23 DONE 0.3s\n", "\n", "#24 [18/21] COPY ./map/app.json /etc/holoscan/app.json\n", "#24 DONE 0.1s\n", @@ -1679,102 +1560,115 @@ "\n", "#28 exporting to docker image format\n", "#28 exporting layers\n", - "#28 exporting layers 157.5s done\n", - "#28 exporting manifest sha256:6d3e7548287a6a3abc70110c29982b2b32483515fb249876f50112b03dac40a6 0.0s done\n", - "#28 exporting config sha256:69287893ca549aef4897eb7391ab557b1cc5802f1c547ac41b761808e04a7fa4 0.0s done\n", + "#28 exporting layers 5.9s done\n", + "#28 exporting manifest sha256:261bfd883479734974f9f01500b63a394537a84df186d0552794645b0152f0f5 0.0s done\n", + "#28 exporting config sha256:47a95542f89e8e3174bba11729dc605a923542cf7c48c180ae2eb42290619826 0.0s done\n", "#28 sending tarball\n", "#28 ...\n", "\n", "#29 importing to docker\n", - "#29 DONE 90.4s\n", + "#29 loading layer 2c6ff491304f 32.77kB / 125.57kB\n", + "#29 loading layer 04072fb0fc22 557.06kB / 73.96MB\n", + "#29 loading layer 04072fb0fc22 71.86MB / 73.96MB 2.1s\n", + "#29 loading layer 1982c4813c35 262.14kB / 26.20MB\n", + "#29 loading layer dc0acf48e445 513B / 513B\n", + "#29 loading layer a50eb25f7721 320B / 320B\n", + "#29 loading layer 838ef774fdf1 298B / 298B\n", + "#29 loading layer 3401c98a4ff8 4.00kB / 4.00kB\n", + "#29 loading layer 838ef774fdf1 298B / 298B 0.8s done\n", + "#29 loading layer 2c6ff491304f 32.77kB / 125.57kB 4.0s done\n", + "#29 loading layer 04072fb0fc22 71.86MB / 73.96MB 3.8s done\n", + "#29 loading layer 1982c4813c35 262.14kB / 26.20MB 1.3s done\n", + "#29 loading layer dc0acf48e445 513B / 513B 0.9s done\n", + "#29 loading layer a50eb25f7721 320B / 320B 0.9s done\n", + "#29 loading layer 3401c98a4ff8 4.00kB / 4.00kB 0.8s done\n", + "#29 DONE 4.0s\n", "\n", "#28 exporting to docker image format\n", - "#28 sending tarball 132.9s done\n", - "#28 DONE 290.5s\n", + "#28 sending tarball 68.3s done\n", + "#28 DONE 74.3s\n", "\n", - "#30 exporting content cache\n", + "#30 exporting cache to client directory\n", "#30 preparing build cache for export\n", - "#30 writing layer sha256:0709800848b4584780b40e7e81200689870e890c38b54e96b65cd0a3b1942f2d\n", - "#30 writing layer sha256:0709800848b4584780b40e7e81200689870e890c38b54e96b65cd0a3b1942f2d done\n", - "#30 writing layer sha256:0ce020987cfa5cd1654085af3bb40779634eb3d792c4a4d6059036463ae0040d done\n", - "#30 writing layer sha256:0f65089b284381bf795d15b1a186e2a8739ea957106fa526edef0d738e7cda70 done\n", - "#30 writing layer sha256:12a47450a9f9cc5d4edab65d0f600dbbe8b23a1663b0b3bb2c481d40e074b580 done\n", - "#30 writing layer sha256:1de965777e2e37c7fabe00bdbf3d0203ca83ed30a71a5479c3113fe4fc48c4bb done\n", - "#30 writing layer sha256:24b5aa2448e920814dd67d7d3c0169b2cdacb13c4048d74ded3b4317843b13ff done\n", - "#30 writing layer sha256:2d42104dbf0a7cc962b791f6ab4f45a803f8a36d296f996aca180cfb2f3e30d0 done\n", - "#30 writing layer sha256:2fa1ce4fa3fec6f9723380dc0536b7c361d874add0baaddc4bbf2accac82d2ff done\n", - "#30 writing layer sha256:3783d0dc66925772df1dfb27f94eaa99034d14162095ac959cd3963ec714d1f4 0.0s done\n", - "#30 writing layer sha256:38794be1b5dc99645feabf89b22cd34fb5bdffb5164ad920e7df94f353efe9c0 done\n", - "#30 writing layer sha256:38f963dc57c1e7b68a738fe39ed9f9345df7188111a047e2163a46648d7f1d88 done\n", - "#30 writing layer sha256:394546a9b772ece8edef536c1ed208c87a1c39293207cc101fc7d94cc5ff364f\n", - "#30 writing layer sha256:394546a9b772ece8edef536c1ed208c87a1c39293207cc101fc7d94cc5ff364f 54.5s done\n", - "#30 writing layer sha256:3e7e4c9bc2b136814c20c04feb4eea2b2ecf972e20182d88759931130cfb4181 done\n", - "#30 writing layer sha256:3fd77037ad585442cd82d64e337f49a38ddba50432b2a1e563a48401d25c79e6 done\n", - "#30 writing layer sha256:40c61fe78b843bfb1e890001a8d40e2dbe8f3e2f0ddb65d10c91147cfb0f1af3\n", - "#30 writing layer sha256:40c61fe78b843bfb1e890001a8d40e2dbe8f3e2f0ddb65d10c91147cfb0f1af3 0.1s done\n", - "#30 writing layer sha256:41814ed91034b30ac9c44dfc604a4bade6138005ccf682372c02e0bead66dbc0 done\n", - "#30 writing layer sha256:45893188359aca643d5918c9932da995364dc62013dfa40c075298b1baabece3 done\n", - "#30 writing layer sha256:49bc651b19d9e46715c15c41b7c0daa007e8e25f7d9518f04f0f06592799875a done\n", - "#30 writing layer sha256:4c12db5118d8a7d909e4926d69a2192d2b3cd8b110d49c7504a4f701258c1ccc done\n", - "#30 writing layer sha256:4cc43a803109d6e9d1fd35495cef9b1257035f5341a2db54f7a1940815b6cc65 done\n", - "#30 writing layer sha256:4d32b49e2995210e8937f0898327f196d3fcc52486f0be920e8b2d65f150a7ab done\n", - "#30 writing layer sha256:4d6fe980bad9cd7b2c85a478c8033cae3d098a81f7934322fb64658b0c8f9854 done\n", + "#30 writing layer sha256:00bb4c1319ba1a33ac3edcb3aa1240d8abcb8d0383c6267ed8028d3b6228a8a4\n", + "#30 writing layer sha256:00bb4c1319ba1a33ac3edcb3aa1240d8abcb8d0383c6267ed8028d3b6228a8a4 done\n", + "#30 writing layer sha256:014cff740c9ec6e9a30d0b859219a700ae880eb385d62095d348f5ea136d6015 done\n", + "#30 writing layer sha256:0a1756432df4a4350712d8ae5c003f1526bd2180800b3ae6301cfc9ccf370254 done\n", + "#30 writing layer sha256:0a77dcbd0e648ddc4f8e5230ade8fdb781d99e24fa4f13ca96a360c7f7e6751f done\n", + "#30 writing layer sha256:0ec682bf99715a9f88631226f3749e2271b8b9f254528ef61f65ed829984821c done\n", + "#30 writing layer sha256:1133dfcee0e851b490d17b3567f50c4b25ba5750da02ba4b3f3630655d0b1a7b done\n", + "#30 writing layer sha256:1294b2835667d633f938174d9fecb18a60bbbebb6fb49788a1f939893a25d1af done\n", + "#30 writing layer sha256:16a03c6e0373b62f9713416da0229bb7ce2585183141081d3ea8427ad2e84408 done\n", + "#30 writing layer sha256:20d331454f5fb557f2692dfbdbe092c718fd2cb55d5db9d661b62228dacca5c2 done\n", + "#30 writing layer sha256:2232aeb26b5b7ea57227e9a5b84da4fb229624d7bc976a5f7ce86d9c8653d277 done\n", + "#30 writing layer sha256:238f69a43816e481f0295995fcf5fe74d59facf0f9f99734c8d0a2fb140630e0 done\n", + "#30 writing layer sha256:2ad84487f9d4d31cd1e0a92697a5447dd241935253d036b272ef16d31620c1e7 done\n", + "#30 writing layer sha256:2bb73464628bd4a136c4937f42d522c847bea86b2215ae734949e24c1caf450e done\n", + "#30 writing layer sha256:2ca59f23482f8bc9a313f15326cc9326efd2553b0480274dc62b6213b864e2ed 0.0s done\n", + "#30 writing layer sha256:32ccfe43297de5eb7d872ac37cb2e4b356a9fdd75b37a1d4e9c0a96f26d3a1eb 0.0s done\n", + "#30 writing layer sha256:3e3e04011ebdba380ab129f0ee390626cb2a600623815ca756340c18bedb9517 done\n", + "#30 writing layer sha256:42619ce4a0c9e54cfd0ee41a8e5f27d58b3f51becabd1ac6de725fbe6c42b14a done\n", + "#30 writing layer sha256:43a21fb6c76bd2b3715cc09d9f8c3865dc61c51dd9e2327b429f5bec8fff85d1 done\n", + "#30 writing layer sha256:49bdc9abf8a437ccff67cc11490ba52c976577992909856a86be872a34d3b950 done\n", + "#30 writing layer sha256:4b691ba9f48b41eaa0c754feba8366f1c030464fcbc55eeffa6c86675990933a done\n", + "#30 writing layer sha256:4d04a8db404f16c2704fa10739cb6745a0187713a21a6ef0deb34b48629b54c1 done\n", "#30 writing layer sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 done\n", - "#30 writing layer sha256:5150182f1ff123399b300ca469e00f6c4d82e1b9b72652fb8ee7eab370245236 done\n", - "#30 writing layer sha256:595c38fa102c61c3dda19bdab70dcd26a0e50465b986d022a84fa69023a05d0f done\n", - "#30 writing layer sha256:599c7444a380d72214895c595ea8a776b249a23b4bde9c029c5b3b737fd44cf1 0.0s done\n", - "#30 writing layer sha256:59d451175f6950740e26d38c322da0ef67cb59da63181eb32996f752ba8a2f17 done\n", - "#30 writing layer sha256:5ad1f2004580e415b998124ea394e9d4072a35d70968118c779f307204d6bd17 done\n", - "#30 writing layer sha256:62598eafddf023e7f22643485f4321cbd51ff7eee743b970db12454fd3c8c675 done\n", - "#30 writing layer sha256:63d7e616a46987136f4cc9eba95db6f6327b4854cfe3c7e20fed6db0c966e380 done\n", - "#30 writing layer sha256:689393d5c3926910ebc9e4c6c377ea651c84cf0134a1aa69cadcf309ecef9e02 0.0s done\n", - "#30 writing layer sha256:6939d591a6b09b14a437e5cd2d6082a52b6d76bec4f72d960440f097721da34f\n", - "#30 writing layer sha256:6939d591a6b09b14a437e5cd2d6082a52b6d76bec4f72d960440f097721da34f done\n", - "#30 writing layer sha256:698318e5a60e5e0d48c45bf992f205a9532da567fdfe94bd59be2e192975dd6f done\n", - "#30 writing layer sha256:6d907abcbcc8c4fea9f9678d5b7a9a0171b441c35bed212a634d58d27d8fb5cb\n", - "#30 writing layer sha256:6d907abcbcc8c4fea9f9678d5b7a9a0171b441c35bed212a634d58d27d8fb5cb 0.4s done\n", - "#30 writing layer sha256:6ddc1d0f91833b36aac1c6f0c8cea005c87d94bab132d46cc06d9b060a81cca3 done\n", - "#30 writing layer sha256:7073fc2251eff329a82af3e4f73a2b5e75b8fe8c6d744183f08d11f395277e9c 0.0s done\n", - "#30 writing layer sha256:74ac1f5a47c0926bff1e997bb99985a09926f43bd0895cb27ceb5fa9e95f8720 done\n", - "#30 writing layer sha256:7577973918dd30e764733a352a93f418000bc3181163ca451b2307492c1a6ba9 done\n", - "#30 writing layer sha256:7f256c83fad20862afc50cdf843f2b48a9be6bb58f9f17ef9f63e26f047ba31a 0.0s done\n", - "#30 writing layer sha256:886c886d8a09d8befb92df75dd461d4f97b77d7cff4144c4223b0d2f6f2c17f2\n", - "#30 writing layer sha256:886c886d8a09d8befb92df75dd461d4f97b77d7cff4144c4223b0d2f6f2c17f2 done\n", - "#30 writing layer sha256:8a7451db9b4b817b3b33904abddb7041810a4ffe8ed4a034307d45d9ae9b3f2a done\n", - "#30 writing layer sha256:8bf04775f408495a1ab7de439b0fc5f981bd282834c6d940f5eb7b865fcb2aa0 0.0s done\n", - "#30 writing layer sha256:916f4054c6e7f10de4fd7c08ffc75fa23ebecca4eceb8183cb1023b33b1696c9 done\n", - "#30 writing layer sha256:9463aa3f56275af97693df69478a2dc1d171f4e763ca6f7b6f370a35e605c154 done\n", - "#30 writing layer sha256:955fd173ed884230c2eded4542d10a97384b408537be6bbb7c4ae09ccd6fb2d0 done\n", - "#30 writing layer sha256:9c42a4ee99755f441251e6043b2cbba16e49818a88775e7501ec17e379ce3cfd done\n", - "#30 writing layer sha256:9c63be0a86e3dc4168db3814bf464e40996afda0031649d9faa8ff7568c3154f done\n", - "#30 writing layer sha256:9e04bda98b05554953459b5edef7b2b14d32f1a00b979a23d04b6eb5c191e66b done\n", - "#30 writing layer sha256:a4a0c690bc7da07e592514dccaa26098a387e8457f69095e922b6d73f7852502 done\n", - "#30 writing layer sha256:a4aafbc094d78a85bef41036173eb816a53bcd3e2564594a32f542facdf2aba6 done\n", - "#30 writing layer sha256:ae36a4d38b76948e39a5957025c984a674d2de18ce162a8caaa536e6f06fccea done\n", - "#30 writing layer sha256:b2fa40114a4a0725c81b327df89c0c3ed5c05ca9aa7f1157394d5096cf5460ce done\n", + "#30 writing layer sha256:5275a41be8f6691a490c0a15589e0910c73bf971169ad33a850ef570d37f63dd done\n", + "#30 writing layer sha256:52fbfeaf78318d843054ce2bfb5bfc9f71278939a815f6035ab5b14573ad017b done\n", + "#30 writing layer sha256:5792b18b6f162bae61ff5840cdb9e8567e6847a56ac886f940b47e7271c529a7 done\n", + "#30 writing layer sha256:57f244836ad318f9bbb3b29856ae1a5b31038bfbb9b43d2466d51c199eb55041 done\n", + "#30 writing layer sha256:5b5b131e0f20db4cb8e568b623a95f8fc16ed1c6b322a9366df70b59a881f24f done\n", + "#30 writing layer sha256:5ccb787d371fd3697122101438ddd0f55b537832e9756d2c51ab1d8158710ac5 done\n", + "#30 writing layer sha256:62452179df7c18e292f141d4aec29e6aba9ff8270c893731169fc6f41dc07631 done\n", + "#30 writing layer sha256:6630c387f5f2115bca2e646fd0c2f64e1f3d5431c2e050abe607633883eda230 done\n", + "#30 writing layer sha256:69af4b756272a77f683a8d118fd5ca55c03ad5f1bacc673b463f54d16b833da5 done\n", + "#30 writing layer sha256:6ae1f1fb92c0cb2b6e219f687b08c8e511501a7af696c943ca20d119eba7cd02 done\n", + "#30 writing layer sha256:6deb3d550b15a5e099c0b3d0cbc242e351722ca16c058d3a6c28ba1a02824d0f done\n", + "#30 writing layer sha256:7386814d57100e2c7389fbf4e16f140f5c549d31434c62c3884a85a3ee5cd2a7 done\n", + "#30 writing layer sha256:7852b73ea931e3a8d3287ee7ef3cf4bad068e44f046583bfc2b81336fb299284 done\n", + "#30 writing layer sha256:7e73869c74822e4539e104a3d2aff853f4622cd0bb873576db1db53c9e91f621 done\n", + "#30 writing layer sha256:7eae142b38745fe88962874372374deb672998600264a17e638c010b79e6b535 done\n", + "#30 writing layer sha256:7f2e5ab2c599fa36698918d3e73c991d8616fff9037077cd230529e7cd1c5e0e done\n", + "#30 writing layer sha256:81b2d4e60f6b67ed37f95e3d15237a436e76056fb4babcb9a188fd2b337c897b 0.0s done\n", + "#30 writing layer sha256:82a3436133b2b17bb407c7fe488932aa0ca55411f23ab55c34a6134b287c6a27 done\n", + "#30 writing layer sha256:90eae6faa5cc5ba62f12c25915cdfb1a7a51abfba0d05cb5818c3f908f4e345f\n", + "#30 writing layer sha256:90eae6faa5cc5ba62f12c25915cdfb1a7a51abfba0d05cb5818c3f908f4e345f done\n", + "#30 writing layer sha256:93e2013abbc3bc85f24d4739ac397584f6332aec7d8e80f8d95d9c961978fe90 0.0s done\n", + "#30 writing layer sha256:9723201c31b4e56a2dff5c3769790d4d6a7c069d75bdd3996395600bd0d067cd done\n", + "#30 writing layer sha256:9ac855545fa90ed2bf3b388fdff9ef06ac9427b0c0fca07c9e59161983d8827e done\n", + "#30 writing layer sha256:9d19ee268e0d7bcf6716e6658ee1b0384a71d6f2f9aa1ae2085610cf7c7b316f done\n", + "#30 writing layer sha256:a10c8d7d2714eabf661d1f43a1ccb87a51748cbb9094d5bc0b713e2481b5d329 done\n", + "#30 writing layer sha256:a1748eee9d376f97bd19225ba61dfada9986f063f4fc429e435f157abb629fc6 done\n", + "#30 writing layer sha256:a68f4e0ec09ec3b78cb4cf8e4511d658e34e7b6f676d7806ad9703194ff17604 done\n", + "#30 writing layer sha256:a8e4decc8f7289623b8fd7b9ba1ca555b5a755ebdbf81328d68209f148d9e602 done\n", + "#30 writing layer sha256:a9cc9b4b42ca5455c9da9b048ab2cc36e82bd335f51c23817f4bcf330bbb96f1 done\n", + "#30 writing layer sha256:afde1c269453ce68a0f2b54c1ba8c5ecddeb18a19e5618a4acdef1f0fe3921af done\n", "#30 writing layer sha256:b48a5fafcaba74eb5d7e7665601509e2889285b50a04b5b639a23f8adc818157 done\n", - "#30 writing layer sha256:bc094183f34f419fbf8d0d5a76d88f741675287a26603b98896c4161a0218d63\n", - "#30 writing layer sha256:bc094183f34f419fbf8d0d5a76d88f741675287a26603b98896c4161a0218d63 1.6s done\n", - "#30 writing layer sha256:c86976a083599e36a6441f36f553627194d05ea82bb82a78682e718fe62fccf6\n", - "#30 preparing build cache for export 57.9s done\n", - "#30 writing layer sha256:c86976a083599e36a6441f36f553627194d05ea82bb82a78682e718fe62fccf6 done\n", - "#30 writing layer sha256:cb506fbdedc817e3d074f609e2edbf9655aacd7784610a1bbac52f2d7be25438 done\n", - "#30 writing layer sha256:d2a6fe65a1f84edb65b63460a75d1cac1aa48b72789006881b0bcfd54cd01ffd done\n", - "#30 writing layer sha256:d8d16d6af76dc7c6b539422a25fdad5efb8ada5a8188069fcd9d113e3b783304 done\n", - "#30 writing layer sha256:ddc2ade4f6fe866696cb638c8a102cb644fa842c2ca578392802b3e0e5e3bcb7 done\n", - "#30 writing layer sha256:e2cfd7f6244d6f35befa6bda1caa65f1786cecf3f00ef99d7c9a90715ce6a03c done\n", - "#30 writing layer sha256:e94a4481e9334ff402bf90628594f64a426672debbdfb55f1290802e52013907 done\n", - "#30 writing layer sha256:eaf45e9f32d1f5a9983945a1a9f8dedbb475bc0f578337610e00b4dedec87c20 done\n", - "#30 writing layer sha256:eb411bef39c013c9853651e68f00965dbd826d829c4e478884a2886976e9c989 done\n", - "#30 writing layer sha256:edfe4a95eb6bd3142aeda941ab871ffcc8c19cf50c33561c210ba8ead2424759 done\n", - "#30 writing layer sha256:ef4466d6f927d29d404df9c5af3ef5733c86fa14e008762c90110b963978b1e7 done\n", - "#30 writing layer sha256:f346e3ecdf0bee048fa1e3baf1d3128ff0283b903f03e97524944949bd8882e5 done\n", - "#30 writing layer sha256:f3f9a00a1ce9aadda250aacb3e66a932676badc5d8519c41517fdf7ea14c13ed done\n", - "#30 writing layer sha256:fd849d9bd8889edd43ae38e9f21a912430c8526b2c18f3057a3b2cd74eb27b31 done\n", - "#30 writing config sha256:7cfec7bd2b3ff69855a31c8535d3935c07e6028f6e6f6ad8d1ee72dca22a059e 0.0s done\n", - "#30 writing manifest sha256:ae24e011466a0d1cad0f5738f6d2871e7cc99b4f959833c08e056e8dadd6f56c 0.0s done\n", - "#30 DONE 57.9s\n", - "[2023-11-15 19:26:02,805] [INFO] (packager) - Build Summary:\n", + "#30 writing layer sha256:ba9f7c75e4dd7942b944679995365aab766d3677da2e69e1d74472f471a484dd done\n", + "#30 writing layer sha256:bdfc73b2a0fa11b4086677e117a2f9feb6b4ffeccb23a3d58a30543339607e31 done\n", + "#30 writing layer sha256:c175bb235295e50de2961fa1e1a2235c57e6eba723a914287dfc26d3be0eac11 done\n", + "#30 writing layer sha256:c98533d2908f36a5e9b52faae83809b3b6865b50e90e2817308acfc64cd3655f done\n", + "#30 writing layer sha256:cb6c95b33bc30dd285c5b3cf99a05281b8f12decae1c932ab64bd58f56354021 done\n", + "#30 writing layer sha256:cc985f61e92a80cbc59a150c5758becb75f8eddbbbaf17d46374ede3cd01a51f\n", + "#30 writing layer 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69287893ca54 5 minutes ago 15.6GB\n" + "mednist_app-x64-workstation-dgpu-linux-amd64 1.0 47a95542f89e About a minute ago 17.5GB\n" ] } ], @@ -1836,20 +1730,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-11-15 19:26:07,374] [INFO] (runner) - Checking dependencies...\n", - "[2023-11-15 19:26:07,375] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "[2024-04-10 16:37:33,094] [INFO] (runner) - Checking dependencies...\n", + "[2024-04-10 16:37:33,094] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "\n", + "[2024-04-10 16:37:33,094] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", "\n", - "[2023-11-15 19:26:07,375] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", + "[2024-04-10 16:37:33,094] [INFO] (runner) - --> Verifying if \"mednist_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", "\n", - "[2023-11-15 19:26:07,375] [INFO] (runner) - --> Verifying if \"mednist_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", + "[2024-04-10 16:37:33,168] [INFO] (runner) - Reading HAP/MAP manifest...\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmp96catisy/app.json\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmp96catisy/pkg.json\n", + "[2024-04-10 16:37:33,777] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", "\n", - "[2023-11-15 19:26:07,454] [INFO] (runner) - Reading HAP/MAP manifest...\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmp7n6pc6u1/app.json\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmp7n6pc6u1/pkg.json\n", - "[2023-11-15 19:26:07,741] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", + "[2024-04-10 16:37:33,778] [INFO] (runner) - --> Verifying \"nvidia-ctk\" version...\n", "\n", - "[2023-11-15 19:26:07,994] [INFO] (common) - Launching container (c634a4b0db9a) using image 'mednist_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", - " container name: quizzical_hopper\n", + "[2024-04-10 16:37:33,934] [INFO] (common) - Launching container (4b3bba81606f) using image 'mednist_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", + " container name: flamboyant_jepsen\n", " host name: mingq-dt\n", " network: host\n", " user: 1000:1000\n", @@ -1858,49 +1754,50 @@ " ipc mode: host\n", " shared memory size: 67108864\n", " devices: \n", - "2023-11-16 03:26:08 [INFO] Launching application python3 /opt/holoscan/app/mednist_classifier_monaideploy.py ...\n", + " group_add: 44\n", + "2024-04-10 23:37:34 [INFO] Launching application python3 /opt/holoscan/app/mednist_classifier_monaideploy.py ...\n", "\n", - "[info] [app_driver.cpp:1025] Launching the driver/health checking service\n", + "[info] [app_driver.cpp:1161] Launching the driver/health checking service\n", "\n", - "[info] [gxf_executor.cpp:210] Creating context\n", + "[info] [gxf_executor.cpp:211] Creating context\n", "\n", - "[info] [server.cpp:73] Health checking server listening on 0.0.0.0:8777\n", + "[info] [server.cpp:87] Health checking server listening on 0.0.0.0:8777\n", "\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", "\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", "\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", "\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "\n", "[info] [greedy_scheduler.cpp:190] Scheduling 3 entities\n", "\n", - "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", + "/home/holoscan/.local/lib/python3.10/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", "\n", - "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", + " return torch.as_tensor(x, *args, **_kwargs).as_subclass(cls)\n", "\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", + "/home/holoscan/.local/lib/python3.10/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", "\n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", + " warnings.warn(msg)\n", "\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", + "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "\n", - "[info] [gxf_executor.cpp:229] Destroying context\n", + "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/monai/data/meta_tensor.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", "\n", - " return torch.as_tensor(x, *args, **_kwargs).as_subclass(cls)\n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: Invalid value for VR UI: 'xyz'. Please see for allowed values for each VR.\n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", "\n", - " warnings.warn(msg)\n", + "[info] [gxf_executor.cpp:230] Destroying context\n", "\n", "AbdomenCT\n", "\n", - "[2023-11-15 19:26:14,982] [INFO] (common) - Container 'quizzical_hopper'(c634a4b0db9a) exited.\n" + "[2024-04-10 16:37:42,228] [INFO] (common) - Container 'flamboyant_jepsen'(4b3bba81606f) exited.\n" ] } ], @@ -1962,7 +1859,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/notebooks/tutorials/03_segmentation_app.ipynb b/notebooks/tutorials/03_segmentation_app.ipynb index 11fc08ce..946e2420 100644 --- a/notebooks/tutorials/03_segmentation_app.ipynb +++ b/notebooks/tutorials/03_segmentation_app.ipynb @@ -132,23 +132,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: gdown in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (4.7.1)\n", - "Requirement already satisfied: filelock in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (3.12.2)\n", - "Requirement already satisfied: requests[socks] in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (2.31.0)\n", - "Requirement already satisfied: six in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (1.16.0)\n", - "Requirement already satisfied: tqdm in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (4.66.1)\n", - "Requirement already satisfied: beautifulsoup4 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (4.12.2)\n", - "Requirement already satisfied: soupsieve>1.2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from beautifulsoup4->gdown) (2.4.1)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (3.2.0)\n", - "Requirement already satisfied: idna<4,>=2.5 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (3.4)\n", - 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"From (uriginal): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ\n", - "From (redirected): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ&confirm=t&uuid=583abdda-51b2-449f-b609-992374b4ac1a\n", + "From (original): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ\n", + "From (redirected): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ&confirm=t&uuid=03efbee4-6b67-4413-8b8e-522d9c7cc472\n", "To: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/ai_spleen_seg_bundle_data.zip\n", - "100%|██████████████████████████████████████| 79.4M/79.4M [00:01<00:00, 56.1MB/s]\n", + "100%|███████████████████████████████████████| 79.4M/79.4M [00:00<00:00, 101MB/s]\n", "Archive: ai_spleen_seg_bundle_data.zip\n", " inflating: dcm/1-001.dcm \n", " inflating: dcm/1-002.dcm \n", @@ -745,189 +747,95 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-08-30 00:58:15,457] [INFO] (root) - Parsed args: Namespace(argv=[], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-08-30 00:58:15,465] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", - "[2023-08-30 00:58:15,466] [INFO] (__main__.AISpleenSegApp) - App input and output path: dcm, output\n", - "[info] [gxf_executor.cpp:210] Creating context\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 16:41:09,106] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])\n", + "[2024-04-10 16:41:09,114] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", + "[2024-04-10 16:41:09,116] [INFO] (__main__.AISpleenSegApp) - App input and output path: dcm, output\n", + "[info] [gxf_executor.cpp:211] Creating context\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[info] [greedy_scheduler.cpp:190] Scheduling 6 entities\n", - "[2023-08-30 00:58:15,557] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", - "[2023-08-30 00:58:15,886] [INFO] (root) - Finding series for Selection named: CT Series\n", - "[2023-08-30 00:58:15,887] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 16:41:09,164] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 16:41:09,742] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 16:41:09,743] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", " # of series: 1\n", - "[2023-08-30 00:58:15,888] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 00:58:15,889] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "[2023-08-30 00:58:15,889] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", - "[2023-08-30 00:58:15,890] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 00:58:15,891] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", - "[2023-08-30 00:58:15,891] [INFO] (root) - Series attribute Modality value: CT\n", - "[2023-08-30 00:58:15,892] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 00:58:15,892] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", - "[2023-08-30 00:58:15,893] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", - "[2023-08-30 00:58:15,893] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 00:58:15,894] [INFO] (root) - On attribute: 'ImageType' to match value: ['PRIMARY', 'ORIGINAL']\n", - "[2023-08-30 00:58:15,894] [INFO] (root) - Series attribute ImageType value: None\n", - "[2023-08-30 00:58:15,895] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "[2023-08-30 00:58:16,110] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Converted Image object metadata:\n", - "[2023-08-30 00:58:16,111] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239, type \n", - "[2023-08-30 00:58:16,112] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDate: 20090831, type \n", - "[2023-08-30 00:58:16,112] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesTime: 101721.452, type \n", - "[2023-08-30 00:58:16,113] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Modality: CT, type \n", - "[2023-08-30 00:58:16,114] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDescription: ABD/PANC 3.0 B31f, type \n", - "[2023-08-30 00:58:16,115] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - PatientPosition: HFS, type \n", - "[2023-08-30 00:58:16,116] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesNumber: 8, type \n", - "[2023-08-30 00:58:16,116] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_pixel_spacing: 0.7890625, type \n", - "[2023-08-30 00:58:16,117] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_pixel_spacing: 0.7890625, type \n", - "[2023-08-30 00:58:16,117] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_pixel_spacing: 1.5, type \n", - "[2023-08-30 00:58:16,118] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_direction_cosine: [1.0, 0.0, 0.0], type \n", - "[2023-08-30 00:58:16,119] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_direction_cosine: [0.0, 1.0, 0.0], type \n", - "[2023-08-30 00:58:16,119] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_direction_cosine: [0.0, 0.0, 1.0], type \n", - "[2023-08-30 00:58:16,120] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - dicom_affine_transform: [[ 0.7890625 0. 0. -197.60547 ]\n", + "[2024-04-10 16:41:09,744] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 16:41:09,745] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2024-04-10 16:41:09,746] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 16:41:09,747] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 16:41:09,748] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 16:41:09,749] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 16:41:09,750] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 16:41:09,751] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 16:41:09,753] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 16:41:09,754] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 16:41:09,755] [INFO] (root) - On attribute: 'ImageType' to match value: ['PRIMARY', 'ORIGINAL']\n", + "[2024-04-10 16:41:09,756] [INFO] (root) - Series attribute ImageType value: None\n", + "[2024-04-10 16:41:09,757] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 16:41:10,007] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Converted Image object metadata:\n", + "[2024-04-10 16:41:10,009] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239, type \n", + "[2024-04-10 16:41:10,009] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDate: 20090831, type \n", + "[2024-04-10 16:41:10,010] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesTime: 101721.452, type \n", + "[2024-04-10 16:41:10,011] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Modality: CT, type \n", + "[2024-04-10 16:41:10,012] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDescription: ABD/PANC 3.0 B31f, type \n", + "[2024-04-10 16:41:10,012] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - PatientPosition: HFS, type \n", + "[2024-04-10 16:41:10,013] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesNumber: 8, type \n", + "[2024-04-10 16:41:10,014] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_pixel_spacing: 0.7890625, type \n", + "[2024-04-10 16:41:10,015] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_pixel_spacing: 0.7890625, type \n", + "[2024-04-10 16:41:10,015] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_pixel_spacing: 1.5, type \n", + "[2024-04-10 16:41:10,016] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_direction_cosine: [1.0, 0.0, 0.0], type \n", + "[2024-04-10 16:41:10,017] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_direction_cosine: [0.0, 1.0, 0.0], type \n", + "[2024-04-10 16:41:10,018] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_direction_cosine: [0.0, 0.0, 1.0], type \n", + "[2024-04-10 16:41:10,019] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - dicom_affine_transform: [[ 0.7890625 0. 0. -197.60547 ]\n", " [ 0. 0.7890625 0. -398.60547 ]\n", " [ 0. 0. 1.5 -383. ]\n", " [ 0. 0. 0. 1. ]], type \n", - "[2023-08-30 00:58:16,121] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - nifti_affine_transform: [[ -0.7890625 -0. -0. 197.60547 ]\n", + "[2024-04-10 16:41:10,020] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - nifti_affine_transform: [[ -0.7890625 -0. -0. 197.60547 ]\n", " [ -0. -0.7890625 -0. 398.60547 ]\n", " [ 0. 0. 1.5 -383. ]\n", " [ 0. 0. 0. 1. ]], type \n", - "[2023-08-30 00:58:16,122] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291, type \n", - "[2023-08-30 00:58:16,123] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyID: , type \n", - "[2023-08-30 00:58:16,123] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDate: 20090831, type \n", - "[2023-08-30 00:58:16,124] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyTime: 095948.599, type \n", - "[2023-08-30 00:58:16,124] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDescription: CT ABDOMEN W IV CONTRAST, type \n", - "[2023-08-30 00:58:16,125] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - AccessionNumber: 5471978513296937, type \n", - "[2023-08-30 00:58:16,125] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - selection_name: CT Series, type \n" + "[2024-04-10 16:41:10,021] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291, type \n", + "[2024-04-10 16:41:10,022] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyID: , type \n", + "[2024-04-10 16:41:10,023] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDate: 20090831, type \n", + "[2024-04-10 16:41:10,025] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyTime: 095948.599, type \n", + "[2024-04-10 16:41:10,026] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDescription: CT ABDOMEN W IV CONTRAST, type \n", + "[2024-04-10 16:41:10,027] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - AccessionNumber: 5471978513296937, type \n", + "[2024-04-10 16:41:10,029] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - selection_name: CT Series, type \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-08-30 00:58:16,933 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n", - "2023-08-30 00:58:23,738 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n" + "2024-04-10 16:41:10,797 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n", + "2024-04-10 16:41:17,104 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2023-08-30 00:58:25,340] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image numpy array shaped: (204, 512, 512)\n", - "[2023-08-30 00:58:25,348] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image pixel max value: 1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "[2024-04-10 16:41:19,060] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image numpy array shaped: (204, 512, 512)\n", + "[2024-04-10 16:41:19,067] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image pixel max value: 1\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", " warnings.warn(\n", - "[2023-08-30 00:58:27,813] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - " warnings.warn(msg)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - " warnings.warn(msg)\n", - "[2023-08-30 00:58:27,816] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 00:58:27,818] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 00:58:27,819] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 00:58:27,820] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 00:58:27,821] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "[2023-08-30 00:58:27,822] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "[2023-08-30 00:58:27,823] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "[2023-08-30 00:58:27,825] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "[2023-08-30 00:58:27,826] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "[2023-08-30 00:58:27,827] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "[2023-08-30 00:58:27,828] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "[2023-08-30 00:58:27,829] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "[2023-08-30 00:58:27,830] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "[2023-08-30 00:58:27,833] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "[2023-08-30 00:58:27,834] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "[2023-08-30 00:58:27,836] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "[2023-08-30 00:58:27,837] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "[2023-08-30 00:58:27,838] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "[2023-08-30 00:58:27,839] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "[2023-08-30 00:58:27,841] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "[2023-08-30 00:58:27,842] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "[2023-08-30 00:58:27,843] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "[2023-08-30 00:58:27,845] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "[2023-08-30 00:58:27,846] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "[2023-08-30 00:58:27,848] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "[2023-08-30 00:58:27,849] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "[2023-08-30 00:58:27,850] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "[2023-08-30 00:58:27,851] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "[2023-08-30 00:58:27,853] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "[2023-08-30 00:58:27,854] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "[2023-08-30 00:58:27,856] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "[2023-08-30 00:58:27,857] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "[2023-08-30 00:58:27,858] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "[2023-08-30 00:58:27,860] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "[2023-08-30 00:58:27,861] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "[2023-08-30 00:58:27,863] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "[2023-08-30 00:58:27,864] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "[2023-08-30 00:58:27,866] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "[2023-08-30 00:58:27,867] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "[2023-08-30 00:58:27,869] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "[2023-08-30 00:58:27,871] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "[2023-08-30 00:58:27,872] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "[2023-08-30 00:58:27,874] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "[2023-08-30 00:58:27,875] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "[2023-08-30 00:58:27,877] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "[2023-08-30 00:58:27,879] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "[2023-08-30 00:58:27,881] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "[2023-08-30 00:58:27,882] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "[2023-08-30 00:58:27,884] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "[2023-08-30 00:58:27,886] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "[2023-08-30 00:58:27,888] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "[2023-08-30 00:58:27,890] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "[2023-08-30 00:58:27,892] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "[2023-08-30 00:58:27,894] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "[2023-08-30 00:58:27,896] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "[2023-08-30 00:58:27,898] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "[2023-08-30 00:58:27,900] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "[2023-08-30 00:58:27,903] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "[2023-08-30 00:58:27,908] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "[2023-08-30 00:58:27,912] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "[2023-08-30 00:58:27,916] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 00:58:27,918] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 00:58:27,921] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 00:58:27,923] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 00:58:27,925] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 00:58:27,927] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 00:58:27,929] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 00:58:27,931] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "[2023-08-30 00:58:27,934] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "[2023-08-30 00:58:27,936] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "[2023-08-30 00:58:27,938] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "[2023-08-30 00:58:27,940] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "[2023-08-30 00:58:27,942] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "[2023-08-30 00:58:27,944] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "[2023-08-30 00:58:27,946] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "[2023-08-30 00:58:27,948] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "[2023-08-30 00:58:27,950] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "[2023-08-30 00:58:27,952] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "[2023-08-30 00:58:27,956] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "[2023-08-30 00:58:27,957] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "[2023-08-30 00:58:27,959] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "[2023-08-30 00:58:27,960] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "[2023-08-30 00:58:27,962] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "[2023-08-30 00:58:27,964] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "[2023-08-30 00:58:27,965] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "[2023-08-30 00:58:27,967] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "[2023-08-30 00:58:27,968] [INFO] (highdicom.seg.sop) - add plane #87 for segment #1\n", - "[2023-08-30 00:58:28,028] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 00:58:28,029] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 00:58:28,030] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 00:58:28,031] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 00:58:28,032] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 00:58:28,033] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 00:58:28,034] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 00:58:28,035] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 00:58:28,036] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 16:41:20,496] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 16:41:20,497] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 16:41:20,498] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 16:41:20,499] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 16:41:20,500] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 16:41:20,501] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 16:41:20,502] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 16:41:20,503] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 16:41:20,503] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[2023-08-30 00:58:28,138] [INFO] (__main__.AISpleenSegApp) - End run\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n" + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[2024-04-10 16:41:20,610] [INFO] (__main__.AISpleenSegApp) - End run\n" ] } ], @@ -1366,178 +1274,84 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 00:58:35,056] [INFO] (root) - Parsed args: Namespace(argv=['my_app'], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-08-30 00:58:35,058] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", - "[2023-08-30 00:58:35,058] [INFO] (app.AISpleenSegApp) - App input and output path: dcm, output\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:210] Creating context\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1741] Activating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1771] Running Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 16:41:25,305] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['my_app'])\n", + "[2024-04-10 16:41:25,472] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", + "[2024-04-10 16:41:25,472] [INFO] (app.AISpleenSegApp) - App input and output path: dcm, output\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:211] Creating context\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1864] Activating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1894] Running Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:190] Scheduling 6 entities\n", - "[2023-08-30 00:58:35,114] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", - "[2023-08-30 00:58:35,624] [INFO] (root) - Finding series for Selection named: CT Series\n", - "[2023-08-30 00:58:35,624] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 16:41:25,505] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 16:41:25,850] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 16:41:25,850] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", " # of series: 1\n", - "[2023-08-30 00:58:35,624] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - Series attribute Modality value: CT\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - On attribute: 'ImageType' to match value: ['PRIMARY', 'ORIGINAL']\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - Series attribute ImageType value: None\n", - "[2023-08-30 00:58:35,625] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Converted Image object metadata:\n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDate: 20090831, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesTime: 101721.452, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Modality: CT, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDescription: ABD/PANC 3.0 B31f, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - PatientPosition: HFS, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesNumber: 8, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_pixel_spacing: 0.7890625, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_pixel_spacing: 0.7890625, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_pixel_spacing: 1.5, type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_direction_cosine: [1.0, 0.0, 0.0], type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_direction_cosine: [0.0, 1.0, 0.0], type \n", - "[2023-08-30 00:58:35,841] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_direction_cosine: [0.0, 0.0, 1.0], type \n", - "[2023-08-30 00:58:35,842] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - dicom_affine_transform: [[ 0.7890625 0. 0. -197.60547 ]\n", + "[2024-04-10 16:41:25,850] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 16:41:25,850] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2024-04-10 16:41:25,850] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 16:41:25,850] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 16:41:25,851] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 16:41:25,851] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 16:41:25,851] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 16:41:25,851] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 16:41:25,851] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 16:41:25,851] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 16:41:25,851] [INFO] (root) - On attribute: 'ImageType' to match value: ['PRIMARY', 'ORIGINAL']\n", + "[2024-04-10 16:41:25,851] [INFO] (root) - Series attribute ImageType value: None\n", + "[2024-04-10 16:41:25,851] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Converted Image object metadata:\n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDate: 20090831, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesTime: 101721.452, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Modality: CT, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDescription: ABD/PANC 3.0 B31f, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - PatientPosition: HFS, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesNumber: 8, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_pixel_spacing: 0.7890625, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_pixel_spacing: 0.7890625, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_pixel_spacing: 1.5, type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_direction_cosine: [1.0, 0.0, 0.0], type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_direction_cosine: [0.0, 1.0, 0.0], type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_direction_cosine: [0.0, 0.0, 1.0], type \n", + "[2024-04-10 16:41:26,402] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - dicom_affine_transform: [[ 0.7890625 0. 0. -197.60547 ]\n", " [ 0. 0.7890625 0. -398.60547 ]\n", " [ 0. 0. 1.5 -383. ]\n", " [ 0. 0. 0. 1. ]], type \n", - "[2023-08-30 00:58:35,842] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - nifti_affine_transform: [[ -0.7890625 -0. -0. 197.60547 ]\n", + "[2024-04-10 16:41:26,403] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - nifti_affine_transform: [[ -0.7890625 -0. -0. 197.60547 ]\n", " [ -0. -0.7890625 -0. 398.60547 ]\n", " [ 0. 0. 1.5 -383. ]\n", " [ 0. 0. 0. 1. ]], type \n", - "[2023-08-30 00:58:35,842] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291, type \n", - "[2023-08-30 00:58:35,842] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyID: , type \n", - "[2023-08-30 00:58:35,842] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDate: 20090831, type \n", - "[2023-08-30 00:58:35,842] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyTime: 095948.599, type \n", - "[2023-08-30 00:58:35,842] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDescription: CT ABDOMEN W IV CONTRAST, type \n", - "[2023-08-30 00:58:35,842] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - AccessionNumber: 5471978513296937, type \n", - "[2023-08-30 00:58:35,842] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - selection_name: CT Series, type \n", - "2023-08-30 00:58:36,678 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n", - "2023-08-30 00:58:42,872 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n", - "[2023-08-30 00:58:44,629] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image numpy array shaped: (204, 512, 512)\n", - "[2023-08-30 00:58:44,635] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image pixel max value: 1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "[2024-04-10 16:41:26,403] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291, type \n", + "[2024-04-10 16:41:26,403] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyID: , type \n", + "[2024-04-10 16:41:26,403] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDate: 20090831, type \n", + "[2024-04-10 16:41:26,403] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyTime: 095948.599, type \n", + "[2024-04-10 16:41:26,403] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDescription: CT ABDOMEN W IV CONTRAST, type \n", + "[2024-04-10 16:41:26,403] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - AccessionNumber: 5471978513296937, type \n", + "[2024-04-10 16:41:26,403] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - selection_name: CT Series, type \n", + "2024-04-10 16:41:27,452 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n", + "2024-04-10 16:41:34,092 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n", + "[2024-04-10 16:41:35,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image numpy array shaped: (204, 512, 512)\n", + "[2024-04-10 16:41:35,720] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image pixel max value: 1\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", " warnings.warn(\n", - "[2023-08-30 00:58:47,369] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - " warnings.warn(msg)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - " warnings.warn(msg)\n", - "[2023-08-30 00:58:47,371] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 00:58:47,372] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 00:58:47,373] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 00:58:47,374] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 00:58:47,374] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "[2023-08-30 00:58:47,375] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "[2023-08-30 00:58:47,376] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "[2023-08-30 00:58:47,377] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "[2023-08-30 00:58:47,378] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "[2023-08-30 00:58:47,378] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "[2023-08-30 00:58:47,379] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "[2023-08-30 00:58:47,380] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "[2023-08-30 00:58:47,381] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "[2023-08-30 00:58:47,382] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "[2023-08-30 00:58:47,382] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "[2023-08-30 00:58:47,383] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "[2023-08-30 00:58:47,384] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "[2023-08-30 00:58:47,385] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "[2023-08-30 00:58:47,385] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "[2023-08-30 00:58:47,386] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "[2023-08-30 00:58:47,387] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "[2023-08-30 00:58:47,387] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "[2023-08-30 00:58:47,388] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "[2023-08-30 00:58:47,389] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "[2023-08-30 00:58:47,390] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "[2023-08-30 00:58:47,390] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "[2023-08-30 00:58:47,391] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "[2023-08-30 00:58:47,392] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "[2023-08-30 00:58:47,393] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "[2023-08-30 00:58:47,393] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "[2023-08-30 00:58:47,394] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "[2023-08-30 00:58:47,395] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "[2023-08-30 00:58:47,396] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "[2023-08-30 00:58:47,397] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "[2023-08-30 00:58:47,398] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "[2023-08-30 00:58:47,399] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "[2023-08-30 00:58:47,400] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "[2023-08-30 00:58:47,401] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "[2023-08-30 00:58:47,401] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "[2023-08-30 00:58:47,402] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "[2023-08-30 00:58:47,403] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "[2023-08-30 00:58:47,403] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "[2023-08-30 00:58:47,404] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "[2023-08-30 00:58:47,404] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "[2023-08-30 00:58:47,405] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "[2023-08-30 00:58:47,405] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "[2023-08-30 00:58:47,406] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "[2023-08-30 00:58:47,407] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "[2023-08-30 00:58:47,407] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "[2023-08-30 00:58:47,408] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "[2023-08-30 00:58:47,409] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "[2023-08-30 00:58:47,410] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "[2023-08-30 00:58:47,410] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "[2023-08-30 00:58:47,411] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "[2023-08-30 00:58:47,411] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "[2023-08-30 00:58:47,412] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "[2023-08-30 00:58:47,413] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "[2023-08-30 00:58:47,413] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "[2023-08-30 00:58:47,414] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "[2023-08-30 00:58:47,414] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "[2023-08-30 00:58:47,415] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 00:58:47,415] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 00:58:47,416] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 00:58:47,417] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 00:58:47,417] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 00:58:47,418] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 00:58:47,418] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 00:58:47,419] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "[2023-08-30 00:58:47,420] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "[2023-08-30 00:58:47,420] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "[2023-08-30 00:58:47,421] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "[2023-08-30 00:58:47,421] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "[2023-08-30 00:58:47,422] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "[2023-08-30 00:58:47,423] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "[2023-08-30 00:58:47,423] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "[2023-08-30 00:58:47,424] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "[2023-08-30 00:58:47,424] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "[2023-08-30 00:58:47,425] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "[2023-08-30 00:58:47,426] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "[2023-08-30 00:58:47,426] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "[2023-08-30 00:58:47,427] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "[2023-08-30 00:58:47,428] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "[2023-08-30 00:58:47,429] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "[2023-08-30 00:58:47,430] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "[2023-08-30 00:58:47,430] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "[2023-08-30 00:58:47,431] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "[2023-08-30 00:58:47,432] [INFO] (highdicom.seg.sop) - add plane #87 for segment #1\n", - "[2023-08-30 00:58:47,528] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 00:58:47,528] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 00:58:47,528] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 00:58:47,528] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 00:58:47,529] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 00:58:47,529] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 00:58:47,529] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 00:58:47,529] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 00:58:47,529] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 16:41:37,387] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 16:41:37,387] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 16:41:37,387] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 16:41:37,387] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 16:41:37,387] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 16:41:37,388] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 16:41:37,388] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 16:41:37,388] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 16:41:37,388] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[2023-08-30 00:58:47,626] [INFO] (app.AISpleenSegApp) - End run\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:229] Destroying context\n" + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[2024-04-10 16:41:37,477] [INFO] (app.AISpleenSegApp) - End run\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:230] Destroying context\n" ] } ], @@ -1556,7 +1370,7 @@ "output_type": "stream", "text": [ "output:\n", - "1.2.826.0.1.3680043.10.511.3.70000117896150756142576971005940679.dcm\n", + "1.2.826.0.1.3680043.10.511.3.12733408477402210746640758069824301.dcm\n", "saved_images_folder\n", "\n", "output/saved_images_folder:\n", @@ -1656,21 +1470,21 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 00:59:31,113] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app\n", - "[2023-08-30 00:59:31,114] [INFO] (packager.parameters) - Detected application type: Python Module\n", - "[2023-08-30 00:59:31,114] [INFO] (packager) - Scanning for models in {models_path}...\n", - "[2023-08-30 00:59:31,115] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.\n", - "[2023-08-30 00:59:31,115] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...\n", - "[2023-08-30 00:59:31,117] [INFO] (packager) - Generating app.json...\n", - "[2023-08-30 00:59:31,117] [INFO] (packager) - Generating pkg.json...\n", - "[2023-08-30 00:59:31,118] [DEBUG] (common) - \n", + "[2024-04-10 16:41:40,386] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app\n", + "[2024-04-10 16:41:40,386] [INFO] (packager.parameters) - Detected application type: Python Module\n", + "[2024-04-10 16:41:40,386] [INFO] (packager) - Scanning for models in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models...\n", + "[2024-04-10 16:41:40,386] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.\n", + "[2024-04-10 16:41:40,386] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...\n", + "[2024-04-10 16:41:40,387] [INFO] (packager) - Generating app.json...\n", + "[2024-04-10 16:41:40,387] [INFO] (packager) - Generating pkg.json...\n", + "[2024-04-10 16:41:40,394] [DEBUG] (common) - \n", "=============== Begin app.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", @@ -1698,21 +1512,21 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1.0,\n", " \"workingDirectory\": \"/var/holoscan\"\n", "}\n", "================ End app.json ================\n", " \n", - "[2023-08-30 00:59:31,118] [DEBUG] (common) - \n", + "[2024-04-10 16:41:40,394] [DEBUG] (common) - \n", "=============== Begin pkg.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", " \"applicationRoot\": \"/opt/holoscan/app\",\n", " \"modelRoot\": \"/opt/holoscan/models\",\n", " \"models\": {\n", - " \"model\": \"/opt/holoscan/models\"\n", + " \"model\": \"/opt/holoscan/models/model\"\n", " },\n", " \"resources\": {\n", " \"cpu\": 1,\n", @@ -1720,15 +1534,16 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"6Gi\"\n", " },\n", - " \"version\": 1.0\n", + " \"version\": 1.0,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "================ End pkg.json ================\n", " \n", - "[2023-08-30 00:59:31,179] [DEBUG] (packager.builder) - \n", + "[2024-04-10 16:41:40,461] [DEBUG] (packager.builder) - \n", "========== Begin Dockerfile ==========\n", "\n", "\n", - "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", + "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", "\n", "ENV DEBIAN_FRONTEND=noninteractive\n", "ENV TERM=xterm-256color\n", @@ -1744,11 +1559,13 @@ " && mkdir -p /var/holoscan/input \\\n", " && mkdir -p /var/holoscan/output\n", "\n", - "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\"\n", + "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\"\n", "LABEL tag=\"my_app:1.0\"\n", "LABEL org.opencontainers.image.title=\"MONAI Deploy App Package - MONAI Bundle AI App\"\n", "LABEL org.opencontainers.image.version=\"1.0\"\n", - "LABEL org.nvidia.holoscan=\"0.6.0\"\n", + "LABEL org.nvidia.holoscan=\"1.0.3\"\n", + "LABEL org.monai.deploy.app-sdk=\"0.5.1\"\n", + "\n", "\n", "ENV HOLOSCAN_ENABLE_HEALTH_CHECK=true\n", "ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input\n", @@ -1773,7 +1590,7 @@ "\n", "\n", "\n", - "RUN groupadd -g $GID $UNAME\n", + "RUN groupadd -f -g $GID $UNAME\n", "RUN useradd -rm -d /home/$UNAME -s /bin/bash -g $GID -G sudo -u $UID $UNAME\n", "RUN chown -R holoscan /var/holoscan \n", "RUN chown -R holoscan /var/holoscan/input \n", @@ -1798,13 +1615,12 @@ "RUN pip install --upgrade pip\n", "RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "\n", - "# Install Holoscan from PyPI org\n", - "RUN pip install holoscan==0.6.0\n", - "\n", + "# Install Holoscan from PyPI only when sdk_type is Holoscan. \n", + "# For MONAI Deploy, the APP SDK will install it unless user specifies the Holoscan SDK file.\n", "\n", "# Copy user-specified MONAI Deploy SDK file\n", - "COPY ./monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", - "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + "COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", "\n", "\n", "\n", @@ -1820,312 +1636,210 @@ "ENTRYPOINT [\"/var/holoscan/tools\"]\n", "=========== End Dockerfile ===========\n", "\n", - "[2023-08-30 00:59:31,179] [INFO] (packager.builder) - \n", + "[2024-04-10 16:41:40,461] [INFO] (packager.builder) - \n", "===============================================================================\n", "Building image for: x64-workstation\n", " Architecture: linux/amd64\n", - " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - " Build Image: N/A \n", + " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + " Build Image: N/A\n", " Cache: Enabled\n", " Configuration: dgpu\n", - " Holoiscan SDK Package: pypi.org\n", - " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + " Holoscan SDK Package: pypi.org\n", + " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", " gRPC Health Probe: N/A\n", - " SDK Version: 0.6.0\n", + " SDK Version: 1.0.3\n", " SDK: monai-deploy\n", " Tag: my_app-x64-workstation-dgpu-linux-amd64:1.0\n", " \n", - "[2023-08-30 00:59:31,807] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", - "[2023-08-30 00:59:31,808] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=my_app-x64-workstation-dgpu-linux-amd64:1.0\n", + "[2024-04-10 16:41:41,020] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", + "[2024-04-10 16:41:41,021] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=my_app-x64-workstation-dgpu-linux-amd64:1.0\n", + "#0 building with \"holoscan_app_builder\" instance using docker-container driver\n", + "\n", "#1 [internal] load build definition from Dockerfile\n", - "#1 transferring dockerfile: 2.67kB done\n", + "#1 transferring dockerfile: 2.80kB done\n", "#1 DONE 0.1s\n", "\n", - "#2 [internal] load .dockerignore\n", - "#2 transferring context: 1.79kB 0.0s done\n", + "#2 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", "#2 DONE 0.1s\n", "\n", - "#3 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#3 DONE 0.6s\n", + "#3 [internal] load .dockerignore\n", + "#3 transferring context: 1.79kB done\n", + "#3 DONE 0.1s\n", "\n", "#4 [internal] load build context\n", "#4 DONE 0.0s\n", "\n", - "#5 importing cache manifest from local:8636426000862419753\n", + "#5 importing cache manifest from local:6394528277147153176\n", + "#5 inferred cache manifest type: application/vnd.oci.image.index.v1+json done\n", "#5 DONE 0.0s\n", "\n", - "#6 [ 1/22] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc\n", - "#6 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc 0.0s done\n", - "#6 DONE 0.0s\n", + "#6 [ 1/21] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155\n", + "#6 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155 0.0s done\n", + "#6 DONE 0.1s\n", "\n", - "#7 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", + "#7 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#7 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done\n", "#7 DONE 0.9s\n", "\n", "#4 [internal] load build context\n", - "#4 transferring context: 19.57MB 0.1s done\n", + "#4 transferring context: 19.56MB 0.1s done\n", "#4 DONE 0.2s\n", "\n", - "#8 [ 7/22] RUN chown -R holoscan /var/holoscan/input\n", + "#8 [ 3/21] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", "#8 CACHED\n", "\n", - "#9 [12/22] COPY ./pip/requirements.txt /tmp/requirements.txt\n", + "#9 [14/21] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "#9 CACHED\n", "\n", - "#10 [10/22] COPY ./tools /var/holoscan/tools\n", + "#10 [ 4/21] RUN groupadd -f -g 1000 holoscan\n", "#10 CACHED\n", "\n", - "#11 [14/22] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", + "#11 [11/21] RUN chmod +x /var/holoscan/tools\n", "#11 CACHED\n", "\n", - "#12 [ 5/22] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", + "#12 [ 2/21] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", "#12 CACHED\n", "\n", - "#13 [13/22] RUN pip install --upgrade pip\n", + "#13 [ 9/21] WORKDIR /var/holoscan\n", "#13 CACHED\n", "\n", - "#14 [ 6/22] RUN chown -R holoscan /var/holoscan\n", + "#14 [ 6/21] RUN chown -R holoscan /var/holoscan\n", "#14 CACHED\n", "\n", - "#15 [ 9/22] WORKDIR /var/holoscan\n", + "#15 [16/21] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", "#15 CACHED\n", "\n", - "#16 [ 4/22] RUN groupadd -g 1000 holoscan\n", + "#16 [ 8/21] RUN chown -R holoscan /var/holoscan/output\n", "#16 CACHED\n", "\n", - "#17 [ 3/22] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", + "#17 [18/21] COPY ./map/app.json /etc/holoscan/app.json\n", "#17 CACHED\n", "\n", - "#18 [ 8/22] RUN chown -R holoscan /var/holoscan/output\n", + "#18 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", "#18 CACHED\n", "\n", - "#19 [ 2/22] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", + "#19 [ 7/21] RUN chown -R holoscan /var/holoscan/input\n", "#19 CACHED\n", "\n", - "#20 [11/22] RUN chmod +x /var/holoscan/tools\n", + "#20 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt\n", "#20 CACHED\n", "\n", - "#21 [15/22] RUN pip install holoscan==0.6.0\n", + "#21 [17/21] COPY ./models /opt/holoscan/models\n", "#21 CACHED\n", "\n", - "#22 [16/22] COPY ./monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", - "#22 DONE 0.3s\n", - "\n", - "#23 [17/22] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", - "#23 0.878 Defaulting to user installation because normal site-packages is not writeable\n", - "#23 0.949 Processing /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", - "#23 1.347 Collecting numpy>=1.21.6 (from monai-deploy-app-sdk==0.5.1+22.g029f8bc.dirty)\n", - "#23 1.347 Obtaining dependency information for numpy>=1.21.6 from https://files.pythonhosted.org/packages/98/5d/5738903efe0ecb73e51eb44feafba32bdba2081263d40c5043568ff60faf/numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", - "#23 1.394 Downloading numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)\n", - "#23 1.409 Requirement already satisfied: networkx>=2.4 in /home/holoscan/.local/lib/python3.8/site-packages (from monai-deploy-app-sdk==0.5.1+22.g029f8bc.dirty) (3.1)\n", - "#23 1.411 Requirement already satisfied: holoscan>=0.5.0 in /home/holoscan/.local/lib/python3.8/site-packages (from monai-deploy-app-sdk==0.5.1+22.g029f8bc.dirty) (0.6.0)\n", - "#23 1.475 Collecting colorama>=0.4.1 (from monai-deploy-app-sdk==0.5.1+22.g029f8bc.dirty)\n", - "#23 1.484 Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n", - "#23 1.557 Collecting typeguard>=3.0.0 (from monai-deploy-app-sdk==0.5.1+22.g029f8bc.dirty)\n", - "#23 1.557 Obtaining dependency information for typeguard>=3.0.0 from https://files.pythonhosted.org/packages/84/99/bfa960dcc0386e240f823f7f4b1b028a18126a72216febf892f84b872444/typeguard-4.1.3-py3-none-any.whl.metadata\n", - "#23 1.566 Downloading typeguard-4.1.3-py3-none-any.whl.metadata (3.7 kB)\n", - "#23 1.634 Collecting cloudpickle~=2.2 (from holoscan>=0.5.0->monai-deploy-app-sdk==0.5.1+22.g029f8bc.dirty)\n", - "#23 1.642 Downloading cloudpickle-2.2.1-py3-none-any.whl (25 kB)\n", - "#23 1.738 Collecting python-on-whales~=0.60 (from holoscan>=0.5.0->monai-deploy-app-sdk==0.5.1+22.g029f8bc.dirty)\n", - "#23 1.738 Obtaining dependency information for python-on-whales~=0.60 from https://files.pythonhosted.org/packages/b1/3b/84494b632d8964e51cb06db89988f2155e1ea62537ba0d70d974fc2a8967/python_on_whales-0.64.2-py3-none-any.whl.metadata\n", - "#23 1.751 Downloading python_on_whales-0.64.2-py3-none-any.whl.metadata (16 kB)\n", - "#23 1.825 Collecting Jinja2~=3.1 (from holoscan>=0.5.0->monai-deploy-app-sdk==0.5.1+22.g029f8bc.dirty)\n", - "#23 1.835 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"#31 writing layer sha256:62598eafddf023e7f22643485f4321cbd51ff7eee743b970db12454fd3c8c675 done\n", - "#31 writing layer sha256:63d7e616a46987136f4cc9eba95db6f6327b4854cfe3c7e20fed6db0c966e380 done\n", - "#31 writing layer sha256:6939d591a6b09b14a437e5cd2d6082a52b6d76bec4f72d960440f097721da34f done\n", - "#31 writing layer sha256:698318e5a60e5e0d48c45bf992f205a9532da567fdfe94bd59be2e192975dd6f done\n", - "#31 writing layer sha256:6ddc1d0f91833b36aac1c6f0c8cea005c87d94bab132d46cc06d9b060a81cca3 done\n", - "#31 writing layer sha256:74ac1f5a47c0926bff1e997bb99985a09926f43bd0895cb27ceb5fa9e95f8720 done\n", - "#31 writing layer sha256:7577973918dd30e764733a352a93f418000bc3181163ca451b2307492c1a6ba9 done\n", - "#31 writing layer sha256:886c886d8a09d8befb92df75dd461d4f97b77d7cff4144c4223b0d2f6f2c17f2 done\n", - "#31 writing layer sha256:8a7451db9b4b817b3b33904abddb7041810a4ffe8ed4a034307d45d9ae9b3f2a done\n", - "#31 writing layer 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"#31 writing layer sha256:b48a5fafcaba74eb5d7e7665601509e2889285b50a04b5b639a23f8adc818157 done\n", - "#31 writing layer sha256:c657dd855c8726b050f2b5bd6f4999883fff6803fe9f22add96f6d3ff89cd477 done\n", - "#31 writing layer sha256:c86976a083599e36a6441f36f553627194d05ea82bb82a78682e718fe62fccf6 done\n", - "#31 writing layer sha256:cb506fbdedc817e3d074f609e2edbf9655aacd7784610a1bbac52f2d7be25438 done\n", - "#31 writing layer sha256:d2a6fe65a1f84edb65b63460a75d1cac1aa48b72789006881b0bcfd54cd01ffd done\n", - "#31 writing layer sha256:d2cafa18c788d3e44592cf8dcabf80e138db8389aa89e765550691199861d4fe 0.0s done\n", - "#31 writing layer sha256:d6a198fd2a224cb803248e86953a164439f1a64889df0861dc5cc7eef4c66664 0.0s done\n", - "#31 writing layer sha256:d8d16d6af76dc7c6b539422a25fdad5efb8ada5a8188069fcd9d113e3b783304\n", - "#31 writing layer sha256:d8d16d6af76dc7c6b539422a25fdad5efb8ada5a8188069fcd9d113e3b783304 done\n", - "#31 writing layer sha256:ddc2ade4f6fe866696cb638c8a102cb644fa842c2ca578392802b3e0e5e3bcb7 done\n", - "#31 writing layer sha256:e2cfd7f6244d6f35befa6bda1caa65f1786cecf3f00ef99d7c9a90715ce6a03c done\n", - "#31 writing layer sha256:e3d62e9dfa6b71c784d14517790438fabbc4adfca340fc7e2c2fa3ae76eb6917 0.0s done\n", - "#31 writing layer sha256:e42e7ccc889dd8eabf5148a4e91eb843e32688cf109fa7c074d87862f8da5da0\n", - "#31 writing layer sha256:e42e7ccc889dd8eabf5148a4e91eb843e32688cf109fa7c074d87862f8da5da0 0.4s done\n", - "#31 writing layer sha256:e94a4481e9334ff402bf90628594f64a426672debbdfb55f1290802e52013907 done\n", - "#31 writing layer sha256:eaf45e9f32d1f5a9983945a1a9f8dedbb475bc0f578337610e00b4dedec87c20 done\n", - "#31 writing layer sha256:eb411bef39c013c9853651e68f00965dbd826d829c4e478884a2886976e9c989 done\n", - "#31 writing layer sha256:edfe4a95eb6bd3142aeda941ab871ffcc8c19cf50c33561c210ba8ead2424759 done\n", - "#31 writing layer sha256:ef4466d6f927d29d404df9c5af3ef5733c86fa14e008762c90110b963978b1e7 done\n", - "#31 writing layer sha256:f346e3ecdf0bee048fa1e3baf1d3128ff0283b903f03e97524944949bd8882e5 done\n", - "#31 writing layer sha256:f3f9a00a1ce9aadda250aacb3e66a932676badc5d8519c41517fdf7ea14c13ed done\n", - "#31 writing layer sha256:f7a50dafd51c2bcaad0ede31fbf29c38fe66776ade008a7fbdb07dba39de7f97 done\n", - "#31 writing layer sha256:fd849d9bd8889edd43ae38e9f21a912430c8526b2c18f3057a3b2cd74eb27b31 done\n", - "#31 writing config sha256:b3ecef29d7ecd014606ec1a5bc12ec4a44ab5cdaedc016d07e70d0bebbaf471a 0.0s done\n", - "#31 preparing build cache for export 2.2s done\n", - "#31 writing manifest sha256:41770de5232f0f9ed1171d161204bcf55abf5a318aa784a38c8de9e07d4b7c3d 0.0s done\n", - "#31 DONE 2.2s\n", - "[2023-08-30 01:00:53,452] [INFO] (packager) - Build Summary:\n", + "#22 [10/21] COPY ./tools /var/holoscan/tools\n", + "#22 CACHED\n", + "\n", + "#23 [15/21] COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#23 CACHED\n", + "\n", + "#24 [19/21] COPY ./app.config /var/holoscan/app.yaml\n", + "#24 CACHED\n", + "\n", + "#25 [13/21] RUN pip install --upgrade pip\n", + "#25 CACHED\n", + "\n", + "#26 [20/21] COPY ./map/pkg.json /etc/holoscan/pkg.json\n", + "#26 CACHED\n", + "\n", + "#27 [21/21] COPY ./app /opt/holoscan/app\n", + "#27 DONE 0.3s\n", + "\n", + "#28 exporting to docker image format\n", + "#28 exporting layers\n", + "#28 exporting layers 0.2s done\n", + "#28 exporting manifest sha256:814514f05787bbad414758721a0e6e6c3bc4c8e15be135868bd5d22125ba1323 0.0s done\n", + "#28 exporting config sha256:5effa5125f3b256ed2e1063cc763040b2eac0f3a49c281b3fecbe3ebbddedce9 0.0s done\n", + "#28 sending tarball\n", + "#28 ...\n", + "\n", + "#29 importing to docker\n", + "#29 loading layer bc0556e272e1 3.91kB / 3.91kB\n", + "#29 loading layer bc0556e272e1 3.91kB / 3.91kB 0.7s done\n", + "#29 DONE 0.7s\n", + "\n", + "#28 exporting to docker image format\n", + "#28 sending tarball 74.9s done\n", + "#28 DONE 75.2s\n", + "\n", + "#30 exporting cache to client directory\n", + "#30 preparing build cache for export\n", + "#30 writing layer sha256:00bb4c1319ba1a33ac3edcb3aa1240d8abcb8d0383c6267ed8028d3b6228a8a4\n", + "#30 writing layer sha256:00bb4c1319ba1a33ac3edcb3aa1240d8abcb8d0383c6267ed8028d3b6228a8a4 done\n", + "#30 writing layer sha256:014cff740c9ec6e9a30d0b859219a700ae880eb385d62095d348f5ea136d6015 done\n", + "#30 writing layer sha256:0a1756432df4a4350712d8ae5c003f1526bd2180800b3ae6301cfc9ccf370254 done\n", + "#30 writing layer sha256:0a77dcbd0e648ddc4f8e5230ade8fdb781d99e24fa4f13ca96a360c7f7e6751f done\n", + "#30 writing layer sha256:0bf3a16e4f3f9ec99796b99e331a5c62472bc9377925e1fdc05f64709ed09895 done\n", + "#30 writing layer sha256:0ec682bf99715a9f88631226f3749e2271b8b9f254528ef61f65ed829984821c done\n", + "#30 writing layer sha256:1133dfcee0e851b490d17b3567f50c4b25ba5750da02ba4b3f3630655d0b1a7b done\n", + "#30 writing layer sha256:1294b2835667d633f938174d9fecb18a60bbbebb6fb49788a1f939893a25d1af done\n", + "#30 writing layer sha256:16a03c6e0373b62f9713416da0229bb7ce2585183141081d3ea8427ad2e84408 done\n", + "#30 writing layer sha256:183aa7032b52e859f5de3dac98da7c8398ed5f8a984d74865561f126c0eecef2 done\n", + "#30 writing layer sha256:20d331454f5fb557f2692dfbdbe092c718fd2cb55d5db9d661b62228dacca5c2 done\n", + "#30 writing layer sha256:2232aeb26b5b7ea57227e9a5b84da4fb229624d7bc976a5f7ce86d9c8653d277 done\n", + "#30 writing layer sha256:238f69a43816e481f0295995fcf5fe74d59facf0f9f99734c8d0a2fb140630e0 done\n", + "#30 writing layer sha256:2ad84487f9d4d31cd1e0a92697a5447dd241935253d036b272ef16d31620c1e7 done\n", + "#30 writing layer sha256:2bb73464628bd4a136c4937f42d522c847bea86b2215ae734949e24c1caf450e done\n", + "#30 writing layer sha256:3e3e04011ebdba380ab129f0ee390626cb2a600623815ca756340c18bedb9517 done\n", + "#30 writing layer sha256:3f0770bfaa7c2f6e0a801dbbdeb644aedfdfeccb547611d3bf9faef04222aeba 0.0s done\n", + "#30 writing layer sha256:42619ce4a0c9e54cfd0ee41a8e5f27d58b3f51becabd1ac6de725fbe6c42b14a done\n", + "#30 writing layer sha256:43a21fb6c76bd2b3715cc09d9f8c3865dc61c51dd9e2327b429f5bec8fff85d1 done\n", + "#30 writing layer sha256:4482079b5d33963eb55191bf404b70095535d4a8e2b64dab7373500515f896b4 done\n", + "#30 writing layer sha256:49bdc9abf8a437ccff67cc11490ba52c976577992909856a86be872a34d3b950 done\n", + "#30 writing layer sha256:4b691ba9f48b41eaa0c754feba8366f1c030464fcbc55eeffa6c86675990933a done\n", + "#30 writing layer sha256:4d04a8db404f16c2704fa10739cb6745a0187713a21a6ef0deb34b48629b54c1 done\n", + "#30 writing layer sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 done\n", + "#30 writing layer sha256:5275a41be8f6691a490c0a15589e0910c73bf971169ad33a850ef570d37f63dd done\n", + "#30 writing layer sha256:52fbfeaf78318d843054ce2bfb5bfc9f71278939a815f6035ab5b14573ad017b done\n", + "#30 writing layer sha256:5792b18b6f162bae61ff5840cdb9e8567e6847a56ac886f940b47e7271c529a7 done\n", + "#30 writing layer sha256:57f244836ad318f9bbb3b29856ae1a5b31038bfbb9b43d2466d51c199eb55041 done\n", + "#30 writing layer sha256:5b5b131e0f20db4cb8e568b623a95f8fc16ed1c6b322a9366df70b59a881f24f done\n", + "#30 writing layer sha256:5ccb787d371fd3697122101438ddd0f55b537832e9756d2c51ab1d8158710ac5 done\n", + "#30 writing layer sha256:5ea668ffc2fc267d241dbf17ca283bc879643a189be4f7e3d9034a82fc64a1ea done\n", + "#30 writing layer sha256:62452179df7c18e292f141d4aec29e6aba9ff8270c893731169fc6f41dc07631 done\n", + "#30 writing layer sha256:6630c387f5f2115bca2e646fd0c2f64e1f3d5431c2e050abe607633883eda230 done\n", + "#30 writing layer sha256:69af4b756272a77f683a8d118fd5ca55c03ad5f1bacc673b463f54d16b833da5 done\n", + "#30 writing layer sha256:6ae1f1fb92c0cb2b6e219f687b08c8e511501a7af696c943ca20d119eba7cd02 done\n", + "#30 writing layer sha256:6deb3d550b15a5e099c0b3d0cbc242e351722ca16c058d3a6c28ba1a02824d0f done\n", + "#30 writing layer sha256:6e80a527af94a864094c4f9116c2d29d3d7548ec8388579d9cf3f8a39a4b8178 done\n", + "#30 writing layer sha256:7386814d57100e2c7389fbf4e16f140f5c549d31434c62c3884a85a3ee5cd2a7 done\n", + "#30 writing layer sha256:7852b73ea931e3a8d3287ee7ef3cf4bad068e44f046583bfc2b81336fb299284 done\n", + "#30 writing layer sha256:7e73869c74822e4539e104a3d2aff853f4622cd0bb873576db1db53c9e91f621 done\n", + "#30 writing layer sha256:7eae142b38745fe88962874372374deb672998600264a17e638c010b79e6b535 done\n", + "#30 writing layer sha256:7f2e5ab2c599fa36698918d3e73c991d8616fff9037077cd230529e7cd1c5e0e done\n", + "#30 writing layer sha256:82a3436133b2b17bb407c7fe488932aa0ca55411f23ab55c34a6134b287c6a27 done\n", + "#30 writing layer sha256:90eae6faa5cc5ba62f12c25915cdfb1a7a51abfba0d05cb5818c3f908f4e345f\n", + "#30 preparing build cache for export 0.7s done\n", + "#30 writing layer sha256:90eae6faa5cc5ba62f12c25915cdfb1a7a51abfba0d05cb5818c3f908f4e345f done\n", + "#30 writing layer sha256:9ac855545fa90ed2bf3b388fdff9ef06ac9427b0c0fca07c9e59161983d8827e done\n", + "#30 writing layer sha256:9d19ee268e0d7bcf6716e6658ee1b0384a71d6f2f9aa1ae2085610cf7c7b316f done\n", + "#30 writing layer sha256:a10c8d7d2714eabf661d1f43a1ccb87a51748cbb9094d5bc0b713e2481b5d329 done\n", + "#30 writing layer sha256:a1748eee9d376f97bd19225ba61dfada9986f063f4fc429e435f157abb629fc6 done\n", + "#30 writing layer sha256:a68f4e0ec09ec3b78cb4cf8e4511d658e34e7b6f676d7806ad9703194ff17604 done\n", + "#30 writing layer sha256:a8e4decc8f7289623b8fd7b9ba1ca555b5a755ebdbf81328d68209f148d9e602 done\n", + "#30 writing layer sha256:afde1c269453ce68a0f2b54c1ba8c5ecddeb18a19e5618a4acdef1f0fe3921af done\n", + "#30 writing layer sha256:b48a5fafcaba74eb5d7e7665601509e2889285b50a04b5b639a23f8adc818157 done\n", + "#30 writing layer sha256:ba9f7c75e4dd7942b944679995365aab766d3677da2e69e1d74472f471a484dd done\n", + "#30 writing layer sha256:bc42865e1c27a9b1bee751f3c99ad2c12a906d32aca396ace7a07231c9cafbd1 done\n", + "#30 writing layer sha256:bdfc73b2a0fa11b4086677e117a2f9feb6b4ffeccb23a3d58a30543339607e31 done\n", + "#30 writing layer sha256:c175bb235295e50de2961fa1e1a2235c57e6eba723a914287dfc26d3be0eac11 done\n", + "#30 writing layer sha256:c98533d2908f36a5e9b52faae83809b3b6865b50e90e2817308acfc64cd3655f done\n", + "#30 writing layer sha256:cb6c95b33bc30dd285c5b3cf99a05281b8f12decae1c932ab64bd58f56354021 done\n", + "#30 writing layer sha256:d6b5d6e098aacb316146a428c6b5aef9692011c6dce0932e3bbfbf27a514b7ed done\n", + "#30 writing layer sha256:d7da5c5e9a40c476c4b3188a845e3276dedfd752e015ea5113df5af64d4d43f7 done\n", + "#30 writing layer sha256:e4297ff4df6f7a8f25cb109e5b24483c314c2e72b8e824f9669173919fc159c9 done\n", + "#30 writing layer sha256:e4aedc686433c0ec5e676e6cc54a164345f7016aa0eb714f00c07e11664a1168 done\n", + "#30 writing layer sha256:e8640a108802cd7519cc53dceb74f7a5c94b562662f1c3c040c2aa6571acf0f3 done\n", + "#30 writing layer sha256:e8acb678f16bc0c369d5cf9c184f2d3a1c773986816526e5e3e9c0354f7e757f done\n", + "#30 writing layer sha256:e9225f7ab6606813ec9acba98a064826ebfd6713a9645a58cd068538af1ecddb done\n", + "#30 writing layer sha256:f33546e75bf1a7d9dc9e21b9a2c54c9d09b24790ad7a4192a8509002ceb14688 done\n", + "#30 writing layer sha256:f608e2fbff86e98627b7e462057e7d2416522096d73fe4664b82fe6ce8a4047d done\n", + "#30 writing layer sha256:f7702077ced42a1ee35e7f5e45f72634328ff3bcfe3f57735ba80baa5ec45daf done\n", + "#30 writing layer sha256:fa66a49172c6e821a1bace57c007c01da10cbc61507c44f8cdfeed8c4e5febab done\n", + "#30 writing config sha256:374c8d5f4f72f0b0a709492f153a59ebd070e903971483f2cefb3e9e45bda48a 0.0s done\n", + "#30 writing cache manifest sha256:00a618573e1678dbe93ffce1675eee120201710fa121207b43875632f6799a58 0.0s done\n", + "#30 DONE 0.7s\n", + "[2024-04-10 16:43:00,358] [INFO] (packager) - Build Summary:\n", "\n", "Platform: x64-workstation/dgpu\n", " Status: Succeeded\n", @@ -2151,14 +1865,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "my_app-x64-workstation-dgpu-linux-amd64 1.0 17be984e3846 About a minute ago 15.4GB\n" + "my_app-x64-workstation-dgpu-linux-amd64 1.0 5effa5125f3b About a minute ago 17.5GB\n" ] } ], @@ -2177,27 +1891,31 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 01:00:57,619] [INFO] (runner) - Checking dependencies...\n", - "[2023-08-30 01:00:57,619] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "output\n", + "dcm\n", + "[2024-04-10 16:43:03,135] [INFO] (runner) - Checking dependencies...\n", + "[2024-04-10 16:43:03,135] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", "\n", - "[2023-08-30 01:00:57,620] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", + "[2024-04-10 16:43:03,135] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", "\n", - "[2023-08-30 01:00:57,620] [INFO] (runner) - --> Verifying if \"my_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", + "[2024-04-10 16:43:03,135] [INFO] (runner) - --> Verifying if \"my_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", "\n", - "[2023-08-30 01:00:57,670] [INFO] (runner) - Reading HAP/MAP manifest...\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmpah05b7ex/app.json\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmpah05b7ex/pkg.json\n", - "[2023-08-30 01:00:58,237] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", + "[2024-04-10 16:43:03,211] [INFO] (runner) - Reading HAP/MAP manifest...\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmpbbeybrjp/app.json\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmpbbeybrjp/pkg.json\n", + "[2024-04-10 16:43:03,567] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", "\n", - "[2023-08-30 01:00:58,414] [INFO] (common) - Launching container (8ca19e2ad332) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", - " container name: peaceful_goldstine\n", + "[2024-04-10 16:43:03,568] [INFO] (runner) - --> Verifying \"nvidia-ctk\" version...\n", + "\n", + "[2024-04-10 16:43:03,864] [INFO] (common) - Launching container (5135fc45ca94) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", + " container name: optimistic_jang\n", " host name: mingq-dt\n", " network: host\n", " user: 1000:1000\n", @@ -2206,99 +1924,96 @@ " ipc mode: host\n", " shared memory size: 67108864\n", " devices: \n", - "2023-08-30 08:00:59 [INFO] Launching application python3 /opt/holoscan/app ...\n", + " group_add: 44\n", + "2024-04-10 23:43:05 [INFO] Launching application python3 /opt/holoscan/app ...\n", "\n", - "[2023-08-30 08:01:03,315] [INFO] (root) - Parsed args: Namespace(argv=['/opt/holoscan/app'], input=None, log_level=None, model=None, output=None, workdir=None)\n", + "[2024-04-10 23:43:09,781] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['/opt/holoscan/app'])\n", "\n", - "[2023-08-30 08:01:03,318] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", + "[2024-04-10 23:43:09,784] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", "\n", - "[2023-08-30 08:01:03,318] [INFO] (app.AISpleenSegApp) - App input and output path: /var/holoscan/input, /var/holoscan/output\n", + "[2024-04-10 23:43:09,784] [INFO] (app.AISpleenSegApp) - App input and output path: /var/holoscan/input, /var/holoscan/output\n", "\n", - "[info] [app_driver.cpp:1025] Launching the driver/health checking service\n", + "[info] [app_driver.cpp:1161] Launching the driver/health checking service\n", "\n", - "[info] [gxf_executor.cpp:210] Creating context\n", + "[info] [gxf_executor.cpp:211] Creating context\n", "\n", - "[info] [server.cpp:73] Health checking server listening on 0.0.0.0:8777\n", + "[info] [server.cpp:87] Health checking server listening on 0.0.0.0:8777\n", "\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", "\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", "\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", "\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "\n", "[info] [greedy_scheduler.cpp:190] Scheduling 6 entities\n", "\n", - "[2023-08-30 08:01:03,434] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 23:43:09,895] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", "\n", - "[2023-08-30 08:01:04,283] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 23:43:10,851] [INFO] (root) - Finding series for Selection named: CT Series\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 23:43:10,851] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", "\n", " # of series: 1\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 23:43:10,851] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 23:43:10,851] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - Series attribute Modality value: CT\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - On attribute: 'ImageType' to match value: ['PRIMARY', 'ORIGINAL']\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", "\n", - "[2023-08-30 08:01:04,284] [INFO] (root) - Series attribute ImageType value: None\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", "\n", - "[2023-08-30 08:01:04,285] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - On attribute: 'ImageType' to match value: ['PRIMARY', 'ORIGINAL']\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - Series attribute ImageType value: None\n", "\n", - " warn_deprecated(argname, msg, warning_category)\n", + "[2024-04-10 23:43:10,852] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Converted Image object metadata:\n", + "[2024-04-10 23:43:11,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Converted Image object metadata:\n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239, type \n", + "[2024-04-10 23:43:11,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDate: 20090831, type \n", + "[2024-04-10 23:43:11,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDate: 20090831, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesTime: 101721.452, type \n", + "[2024-04-10 23:43:11,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesTime: 101721.452, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Modality: CT, type \n", + "[2024-04-10 23:43:11,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Modality: CT, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDescription: ABD/PANC 3.0 B31f, type \n", + "[2024-04-10 23:43:11,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDescription: ABD/PANC 3.0 B31f, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - PatientPosition: HFS, type \n", + "[2024-04-10 23:43:11,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - PatientPosition: HFS, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesNumber: 8, type \n", + "[2024-04-10 23:43:11,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesNumber: 8, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_pixel_spacing: 0.7890625, type \n", + "[2024-04-10 23:43:11,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_pixel_spacing: 0.7890625, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_pixel_spacing: 0.7890625, type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_pixel_spacing: 0.7890625, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_pixel_spacing: 1.5, type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_pixel_spacing: 1.5, type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_direction_cosine: [1.0, 0.0, 0.0], type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_direction_cosine: [1.0, 0.0, 0.0], type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_direction_cosine: [0.0, 1.0, 0.0], type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_direction_cosine: [0.0, 1.0, 0.0], type \n", "\n", - "[2023-08-30 08:01:04,713] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_direction_cosine: [0.0, 0.0, 1.0], type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_direction_cosine: [0.0, 0.0, 1.0], type \n", "\n", - "[2023-08-30 08:01:04,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - dicom_affine_transform: [[ 0.7890625 0. 0. -197.60547 ]\n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - dicom_affine_transform: [[ 0.7890625 0. 0. -197.60547 ]\n", "\n", " [ 0. 0.7890625 0. -398.60547 ]\n", "\n", @@ -2306,7 +2021,7 @@ "\n", " [ 0. 0. 0. 1. ]], type \n", "\n", - "[2023-08-30 08:01:04,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - nifti_affine_transform: [[ -0.7890625 -0. -0. 197.60547 ]\n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - nifti_affine_transform: [[ -0.7890625 -0. -0. 197.60547 ]\n", "\n", " [ -0. -0.7890625 -0. 398.60547 ]\n", "\n", @@ -2314,261 +2029,79 @@ "\n", " [ 0. 0. 0. 1. ]], type \n", "\n", - "[2023-08-30 08:01:04,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291, type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291, type \n", "\n", - "[2023-08-30 08:01:04,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyID: , type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyID: , type \n", "\n", - "[2023-08-30 08:01:04,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDate: 20090831, type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDate: 20090831, type \n", "\n", - "[2023-08-30 08:01:04,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyTime: 095948.599, type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyTime: 095948.599, type \n", "\n", - "[2023-08-30 08:01:04,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDescription: CT ABDOMEN W IV CONTRAST, type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDescription: CT ABDOMEN W IV CONTRAST, type \n", "\n", - "[2023-08-30 08:01:04,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - AccessionNumber: 5471978513296937, type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - AccessionNumber: 5471978513296937, type \n", "\n", - "[2023-08-30 08:01:04,714] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - selection_name: CT Series, type \n", + "[2024-04-10 23:43:11,264] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - selection_name: CT Series, type \n", "\n", - "2023-08-30 08:01:05,687 INFO image_writer.py:197 - writing: /var/holoscan/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n", + "2024-04-10 23:43:12,277 INFO image_writer.py:197 - writing: /var/holoscan/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n", "\n", - "2023-08-30 08:01:16,263 INFO image_writer.py:197 - writing: /var/holoscan/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n", + "2024-04-10 23:43:16,177 INFO image_writer.py:197 - writing: /var/holoscan/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n", "\n", - "[2023-08-30 08:01:17,894] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image numpy array shaped: (204, 512, 512)\n", + "[2024-04-10 23:43:17,870] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image numpy array shaped: (204, 512, 512)\n", "\n", - "[2023-08-30 08:01:17,935] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image pixel max value: 1\n", + "[2024-04-10 23:43:17,876] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image pixel max value: 1\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "/home/holoscan/.local/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", "\n", " warnings.warn(\n", "\n", - "[2023-08-30 08:01:20,578] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - "\n", - " warnings.warn(msg)\n", - "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - "\n", - " warnings.warn(msg)\n", - "\n", - "[2023-08-30 08:01:20,582] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,583] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,584] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,585] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,586] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,587] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,587] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,588] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,589] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,590] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,591] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,591] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,592] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,593] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,594] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,595] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,596] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,597] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,598] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,599] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,600] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,601] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,602] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,603] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,604] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,605] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,606] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,607] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,608] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,609] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,610] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,611] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,612] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,613] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,614] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,615] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,615] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,616] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,617] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,618] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,619] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,620] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,621] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,622] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,623] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,624] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,625] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,626] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,627] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,628] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,629] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,630] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,631] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,632] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,633] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,633] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,635] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,635] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,636] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,637] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,638] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,639] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,640] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,641] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,642] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,643] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,644] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,818] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,819] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,820] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,821] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,822] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,822] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,823] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,824] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,825] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,826] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,826] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,827] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,828] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,828] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,829] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,830] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,831] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,832] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,832] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,833] [INFO] (highdicom.seg.sop) - add plane #87 for segment #1\n", - "\n", - "[2023-08-30 08:01:20,936] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 23:43:19,386] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:01:20,936] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 23:43:19,386] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", "\n", - "[2023-08-30 08:01:20,937] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 23:43:19,386] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:01:20,937] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 23:43:19,386] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", "\n", - "[2023-08-30 08:01:20,937] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 23:43:19,387] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", "\n", - "[2023-08-30 08:01:20,937] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 23:43:19,387] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:01:20,937] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 23:43:19,387] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", "\n", - "[2023-08-30 08:01:20,938] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 23:43:19,387] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", "\n", - "[2023-08-30 08:01:20,938] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 23:43:19,388] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "\n", "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", "\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", "\n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", "\n", - "[2023-08-30 08:01:21,087] [INFO] (app.AISpleenSegApp) - End run\n", + "[2024-04-10 23:43:19,487] [INFO] (app.AISpleenSegApp) - End run\n", "\n", - "[info] [gxf_executor.cpp:229] Destroying context\n", + "[info] [gxf_executor.cpp:230] Destroying context\n", "\n", - "[2023-08-30 01:01:23,201] [INFO] (common) - Container 'peaceful_goldstine'(8ca19e2ad332) exited.\n" + "[2024-04-10 16:43:21,271] [INFO] (common) - Container 'optimistic_jang'(5135fc45ca94) exited.\n" ] } ], "source": [ "# Clear the output folder and run the MAP. The input is expected to be a folder.\n", + "!echo $HOLOSCAN_OUTPUT_PATH\n", + "!echo $HOLOSCAN_INPUT_PATH\n", "!rm -rf $HOLOSCAN_OUTPUT_PATH\n", "!monai-deploy run -i $HOLOSCAN_INPUT_PATH -o $HOLOSCAN_OUTPUT_PATH my_app-x64-workstation-dgpu-linux-amd64:1.0" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2576,7 +2109,7 @@ "output_type": "stream", "text": [ "output:\n", - "1.2.826.0.1.3680043.10.511.3.11368427294546636595990283269631758.dcm\n", + "1.2.826.0.1.3680043.10.511.3.10550615266418892085330010762562517.dcm\n", "saved_images_folder\n", "\n", "output/saved_images_folder:\n", @@ -2609,7 +2142,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" }, "vscode": { "interpreter": { diff --git a/notebooks/tutorials/03_segmentation_viz_app.ipynb b/notebooks/tutorials/03_segmentation_viz_app.ipynb index da4aef74..fbaab63e 100644 --- a/notebooks/tutorials/03_segmentation_viz_app.ipynb +++ b/notebooks/tutorials/03_segmentation_viz_app.ipynb @@ -130,23 +130,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: gdown in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (4.7.1)\n", - "Requirement already satisfied: filelock in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (3.12.2)\n", - "Requirement already satisfied: requests[socks] in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (2.31.0)\n", - "Requirement already satisfied: six in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (1.16.0)\n", - "Requirement already satisfied: tqdm in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (4.66.1)\n", - "Requirement already satisfied: beautifulsoup4 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (4.12.2)\n", - "Requirement already satisfied: soupsieve>1.2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from beautifulsoup4->gdown) (2.4.1)\n", - 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"From (uriginal): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ\n", - "From (redirected): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ&confirm=t&uuid=f144e02e-9680-4015-80c4-36e47679d481\n", + "From (original): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ\n", + "From (redirected): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ&confirm=t&uuid=dcd169d3-fd74-406b-84df-e849918958a1\n", "To: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/ai_spleen_seg_bundle_data.zip\n", - "100%|██████████████████████████████████████| 79.4M/79.4M [00:01<00:00, 61.7MB/s]\n", + "100%|██████████████████████████████████████| 79.4M/79.4M [00:00<00:00, 81.5MB/s]\n", "Archive: ai_spleen_seg_bundle_data.zip\n", " inflating: dcm/1-001.dcm \n", " inflating: dcm/1-002.dcm \n", @@ -633,42 +635,38 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-08-30 01:41:49,185] [INFO] (root) - Parsed args: Namespace(argv=[], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-08-30 01:41:49,192] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", - "[2023-08-30 01:41:49,198] [INFO] (root) - End compose\n", - "[info] [gxf_executor.cpp:210] Creating context\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 10:46:21,853] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])\n", + "[2024-04-10 10:46:21,859] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", + "[2024-04-10 10:46:21,866] [INFO] (root) - End compose\n", + "[info] [gxf_executor.cpp:211] Creating context\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[info] [greedy_scheduler.cpp:190] Scheduling 10 entities\n", - "[2023-08-30 01:41:49,276] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", - "[2023-08-30 01:41:49,592] [INFO] (root) - Finding series for Selection named: CT Series\n", - "[2023-08-30 01:41:49,593] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 10:46:21,911] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 10:46:22,254] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 10:46:22,256] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", " # of series: 1\n", - "[2023-08-30 01:41:49,594] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:41:49,595] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:41:49,596] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", - "[2023-08-30 01:41:49,597] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:41:49,598] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", - "[2023-08-30 01:41:49,599] [INFO] (root) - Series attribute Modality value: CT\n", - "[2023-08-30 01:41:49,599] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:41:49,600] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:41:49,601] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", - "[2023-08-30 01:41:49,601] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:41:49,602] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:41:49,852] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary SaveImaged.__init__:resample: Current default value of argument `resample=True` has been deprecated since version 1.1. It will be changed to `resample=False` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n" + "[2024-04-10 10:46:22,257] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 10:46:22,257] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2024-04-10 10:46:22,258] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 10:46:22,259] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 10:46:22,261] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 10:46:22,262] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 10:46:22,262] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 10:46:22,263] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 10:46:22,264] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 10:46:22,265] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 10:46:22,265] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 10:46:22,497] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "33630c7b7f7e48029eccf86fc414c544", + "model_id": "76b80cc0d2af48c58237d1f8d5d1ec88", "version_major": 2, "version_minor": 0 }, @@ -683,117 +681,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-08-30 01:42:01,875] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file output/stl/spleen.stl.\n", - "[2023-08-30 01:42:03,204] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", - "[2023-08-30 01:42:03,205] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "[2024-04-10 10:46:35,947] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file output/stl/spleen.stl.\n", + "[2024-04-10 10:46:37,415] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", + "[2024-04-10 10:46:37,416] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", " warnings.warn(\n", - "[2023-08-30 01:42:14,899] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - " warnings.warn(msg)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - " warnings.warn(msg)\n", - "[2023-08-30 01:42:14,902] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 01:42:14,903] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 01:42:14,905] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 01:42:14,906] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 01:42:14,907] [INFO] (highdicom.seg.sop) - 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"[2023-08-30 01:42:14,924] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "[2023-08-30 01:42:14,925] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "[2023-08-30 01:42:14,927] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "[2023-08-30 01:42:14,929] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "[2023-08-30 01:42:14,931] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "[2023-08-30 01:42:14,932] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "[2023-08-30 01:42:14,939] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "[2023-08-30 01:42:14,941] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "[2023-08-30 01:42:14,943] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "[2023-08-30 01:42:14,946] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "[2023-08-30 01:42:14,948] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "[2023-08-30 01:42:14,950] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "[2023-08-30 01:42:14,953] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "[2023-08-30 01:42:14,955] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "[2023-08-30 01:42:14,957] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "[2023-08-30 01:42:14,959] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "[2023-08-30 01:42:14,961] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "[2023-08-30 01:42:14,963] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "[2023-08-30 01:42:14,965] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "[2023-08-30 01:42:14,967] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "[2023-08-30 01:42:14,969] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "[2023-08-30 01:42:14,971] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "[2023-08-30 01:42:14,973] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "[2023-08-30 01:42:14,975] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "[2023-08-30 01:42:14,977] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "[2023-08-30 01:42:14,980] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "[2023-08-30 01:42:14,982] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "[2023-08-30 01:42:14,984] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "[2023-08-30 01:42:14,986] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "[2023-08-30 01:42:14,987] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "[2023-08-30 01:42:14,990] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "[2023-08-30 01:42:14,992] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "[2023-08-30 01:42:14,995] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "[2023-08-30 01:42:14,998] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "[2023-08-30 01:42:15,001] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "[2023-08-30 01:42:15,003] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "[2023-08-30 01:42:15,006] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "[2023-08-30 01:42:15,009] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "[2023-08-30 01:42:15,011] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "[2023-08-30 01:42:15,014] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "[2023-08-30 01:42:15,016] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "[2023-08-30 01:42:15,018] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "[2023-08-30 01:42:15,021] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "[2023-08-30 01:42:15,024] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "[2023-08-30 01:42:15,026] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 01:42:15,028] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 01:42:15,030] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 01:42:15,032] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 01:42:15,035] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 01:42:15,037] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 01:42:15,040] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 01:42:15,042] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "[2023-08-30 01:42:15,045] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "[2023-08-30 01:42:15,047] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "[2023-08-30 01:42:15,049] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "[2023-08-30 01:42:15,053] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "[2023-08-30 01:42:15,056] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "[2023-08-30 01:42:15,059] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "[2023-08-30 01:42:15,061] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "[2023-08-30 01:42:15,064] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "[2023-08-30 01:42:15,067] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "[2023-08-30 01:42:15,069] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "[2023-08-30 01:42:15,072] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "[2023-08-30 01:42:15,074] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "[2023-08-30 01:42:15,077] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "[2023-08-30 01:42:15,079] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "[2023-08-30 01:42:15,082] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "[2023-08-30 01:42:15,084] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "[2023-08-30 01:42:15,086] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "[2023-08-30 01:42:15,089] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "[2023-08-30 01:42:15,154] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:42:15,155] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 01:42:15,156] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:42:15,157] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 01:42:15,157] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 01:42:15,158] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:42:15,159] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 01:42:15,160] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 01:42:15,160] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 10:46:47,813] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 10:46:47,814] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 10:46:47,815] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 10:46:47,816] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 10:46:47,817] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 10:46:47,818] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 10:46:47,819] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 10:46:47,821] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 10:46:47,822] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[2023-08-30 01:42:15,256] [INFO] (__main__.AISpleenSegApp) - End run\n" + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[2024-04-10 10:46:47,929] [INFO] (__main__.AISpleenSegApp) - End run\n" ] } ], @@ -1098,148 +1005,53 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 01:42:22,671] [INFO] (root) - Parsed args: Namespace(argv=['my_app'], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-08-30 01:42:22,674] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", - "[2023-08-30 01:42:22,675] [INFO] (root) - End compose\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:210] Creating context\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1741] Activating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1771] Running Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 10:46:52,705] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['my_app'])\n", + "[2024-04-10 10:46:52,707] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", + "[2024-04-10 10:46:52,709] [INFO] (root) - End compose\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:211] Creating context\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1864] Activating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1894] Running Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:190] Scheduling 10 entities\n", - "[2023-08-30 01:42:22,738] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Finding series for Selection named: CT Series\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 10:46:52,747] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", " # of series: 1\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Series attribute Modality value: CT\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:42:23,246] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:42:23,523] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary SaveImaged.__init__:resample: Current default value of argument `resample=True` has been deprecated since version 1.1. It will be changed to `resample=False` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 10:46:53,083] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 10:46:53,297] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts\n", "Box(children=(Widget(), VBox(children=(interactive(children=(Dropdown(description='View mode', index=2, options=(('Cinematic', 'CINEMATIC'), ('Slice', 'SLICE'), ('Slice Segmentation', 'SLICE_SEGMENTATION')), value='SLICE_SEGMENTATION'), Output()), _dom_classes=('widget-interact',)), interactive(children=(Dropdown(description='Camera', options=('Top', 'Right', 'Front'), value='Top'), Output()), _dom_classes=('widget-interact',))))))\n", - "[2023-08-30 01:42:35,981] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file output/stl/spleen.stl.\n", - "[2023-08-30 01:42:37,237] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", - "[2023-08-30 01:42:37,237] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "[2024-04-10 10:47:05,685] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file output/stl/spleen.stl.\n", + "[2024-04-10 10:47:07,271] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", + "[2024-04-10 10:47:07,271] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", " warnings.warn(\n", - "[2023-08-30 01:42:50,065] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - " warnings.warn(msg)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - " warnings.warn(msg)\n", - "[2023-08-30 01:42:50,068] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 01:42:50,068] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 01:42:50,069] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 01:42:50,069] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 01:42:50,070] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "[2023-08-30 01:42:50,071] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "[2023-08-30 01:42:50,071] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "[2023-08-30 01:42:50,072] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "[2023-08-30 01:42:50,072] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "[2023-08-30 01:42:50,073] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "[2023-08-30 01:42:50,074] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "[2023-08-30 01:42:50,074] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "[2023-08-30 01:42:50,075] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "[2023-08-30 01:42:50,075] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "[2023-08-30 01:42:50,076] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "[2023-08-30 01:42:50,076] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "[2023-08-30 01:42:50,077] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "[2023-08-30 01:42:50,078] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "[2023-08-30 01:42:50,078] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "[2023-08-30 01:42:50,079] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "[2023-08-30 01:42:50,079] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "[2023-08-30 01:42:50,080] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "[2023-08-30 01:42:50,081] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "[2023-08-30 01:42:50,081] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "[2023-08-30 01:42:50,082] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "[2023-08-30 01:42:50,082] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "[2023-08-30 01:42:50,083] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "[2023-08-30 01:42:50,084] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "[2023-08-30 01:42:50,084] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "[2023-08-30 01:42:50,085] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "[2023-08-30 01:42:50,085] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "[2023-08-30 01:42:50,086] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "[2023-08-30 01:42:50,086] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "[2023-08-30 01:42:50,088] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "[2023-08-30 01:42:50,088] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "[2023-08-30 01:42:50,089] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "[2023-08-30 01:42:50,089] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "[2023-08-30 01:42:50,090] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "[2023-08-30 01:42:50,091] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "[2023-08-30 01:42:50,091] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "[2023-08-30 01:42:50,092] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "[2023-08-30 01:42:50,092] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "[2023-08-30 01:42:50,093] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "[2023-08-30 01:42:50,093] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "[2023-08-30 01:42:50,094] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "[2023-08-30 01:42:50,095] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "[2023-08-30 01:42:50,095] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "[2023-08-30 01:42:50,096] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "[2023-08-30 01:42:50,096] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "[2023-08-30 01:42:50,097] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "[2023-08-30 01:42:50,098] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "[2023-08-30 01:42:50,098] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "[2023-08-30 01:42:50,099] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "[2023-08-30 01:42:50,099] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "[2023-08-30 01:42:50,100] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "[2023-08-30 01:42:50,100] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "[2023-08-30 01:42:50,101] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "[2023-08-30 01:42:50,102] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "[2023-08-30 01:42:50,102] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "[2023-08-30 01:42:50,103] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "[2023-08-30 01:42:50,103] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 01:42:50,104] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 01:42:50,105] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 01:42:50,106] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 01:42:50,107] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 01:42:50,108] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 01:42:50,108] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 01:42:50,109] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "[2023-08-30 01:42:50,110] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "[2023-08-30 01:42:50,110] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "[2023-08-30 01:42:50,111] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "[2023-08-30 01:42:50,111] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "[2023-08-30 01:42:50,112] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "[2023-08-30 01:42:50,113] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "[2023-08-30 01:42:50,113] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "[2023-08-30 01:42:50,114] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "[2023-08-30 01:42:50,114] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "[2023-08-30 01:42:50,115] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "[2023-08-30 01:42:50,115] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "[2023-08-30 01:42:50,116] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "[2023-08-30 01:42:50,117] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "[2023-08-30 01:42:50,117] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "[2023-08-30 01:42:50,118] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "[2023-08-30 01:42:50,118] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "[2023-08-30 01:42:50,119] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "[2023-08-30 01:42:50,120] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "[2023-08-30 01:42:50,171] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:42:50,171] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 01:42:50,171] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:42:50,171] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 01:42:50,172] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 01:42:50,172] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:42:50,172] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 01:42:50,172] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 01:42:50,172] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 10:47:18,769] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 10:47:18,769] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 10:47:18,769] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 10:47:18,769] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 10:47:18,769] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 10:47:18,769] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 10:47:18,769] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 10:47:18,769] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 10:47:18,769] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[2023-08-30 01:42:50,512] [INFO] (app.AISpleenSegApp) - End run\n" + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[2024-04-10 10:47:18,856] [INFO] (app.AISpleenSegApp) - End run\n" ] } ], @@ -1257,15 +1069,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.2.826.0.1.3680043.10.511.3.31568838222743473783316035623024660.dcm stl\n" - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + "1.2.826.0.1.3680043.10.511.3.65335369220270763710273609202620247.dcm stl\n" ] } ], @@ -1286,7 +1090,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Clara-Viz operators added in an application are used for interactive visualization, so the application shall not be packaged." + "Clara-Viz operators present in an application are used for interactive visualization, so the application shall not be packaged." ] } ], @@ -1306,7 +1110,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" }, "vscode": { "interpreter": { diff --git a/notebooks/tutorials/04_monai_bundle_app.ipynb b/notebooks/tutorials/04_monai_bundle_app.ipynb index 727bdd46..b546487b 100644 --- a/notebooks/tutorials/04_monai_bundle_app.ipynb +++ b/notebooks/tutorials/04_monai_bundle_app.ipynb @@ -135,23 +135,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: gdown in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (4.7.1)\n", - "Requirement already satisfied: filelock in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (3.12.2)\n", - "Requirement already satisfied: requests[socks] in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (2.31.0)\n", - "Requirement already satisfied: six in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (1.16.0)\n", - "Requirement already satisfied: tqdm in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (4.66.1)\n", - "Requirement already satisfied: beautifulsoup4 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (4.12.2)\n", - "Requirement already satisfied: soupsieve>1.2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from beautifulsoup4->gdown) (2.4.1)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (3.2.0)\n", - "Requirement already satisfied: idna<4,>=2.5 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (3.4)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (2.0.4)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (2023.7.22)\n", - "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (1.7.1)\n", + "Requirement already satisfied: gdown in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (5.1.0)\n", + "Requirement already satisfied: beautifulsoup4 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (4.12.3)\n", + "Requirement already satisfied: filelock in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (3.13.3)\n", + "Requirement already satisfied: requests[socks] in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (2.28.2)\n", + "Requirement already satisfied: tqdm in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (4.66.2)\n", + "Requirement already satisfied: soupsieve>1.2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from beautifulsoup4->gdown) (2.5)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (3.6)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (1.26.18)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (2024.2.2)\n", + "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (1.7.1)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Downloading...\n", - "From (uriginal): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ\n", - "From (redirected): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ&confirm=t&uuid=9c85302e-fe27-4e68-8eb5-cc19a530a25c\n", + "From (original): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ\n", + "From (redirected): https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ&confirm=t&uuid=e406b570-c957-4c12-9dc1-df625c4fb575\n", "To: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/ai_spleen_seg_bundle_data.zip\n", - "100%|██████████████████████████████████████| 79.4M/79.4M [00:01<00:00, 67.3MB/s]\n", + "100%|███████████████████████████████████████| 79.4M/79.4M [00:00<00:00, 115MB/s]\n", "Archive: ai_spleen_seg_bundle_data.zip\n", " inflating: dcm/1-001.dcm \n", " inflating: dcm/1-002.dcm \n", @@ -624,148 +626,53 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-08-30 01:19:59,977] [INFO] (root) - Parsed args: Namespace(argv=[], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-08-30 01:19:59,985] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", - "[2023-08-30 01:19:59,991] [INFO] (root) - End compose\n", - "[info] [gxf_executor.cpp:210] Creating context\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 15:03:17,151] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])\n", + "[2024-04-10 15:03:17,160] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n", + "[2024-04-10 15:03:17,167] [INFO] (root) - End compose\n", + "[info] [gxf_executor.cpp:211] Creating context\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[info] [greedy_scheduler.cpp:190] Scheduling 8 entities\n", - "[2023-08-30 01:20:00,118] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", - "[2023-08-30 01:20:00,446] [INFO] (root) - Finding series for Selection named: CT Series\n", - "[2023-08-30 01:20:00,448] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 15:03:17,216] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 15:03:17,807] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 15:03:17,809] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", " # of series: 1\n", - "[2023-08-30 01:20:00,448] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:20:00,449] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:20:00,449] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", - "[2023-08-30 01:20:00,450] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:20:00,451] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", - "[2023-08-30 01:20:00,451] [INFO] (root) - Series attribute Modality value: CT\n", - "[2023-08-30 01:20:00,452] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:20:00,453] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:20:00,453] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", - "[2023-08-30 01:20:00,454] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:20:00,454] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:20:00,662] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary SaveImaged.__init__:resample: Current default value of argument `resample=True` has been deprecated since version 1.1. It will be changed to `resample=False` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "[2023-08-30 01:20:10,739] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file output/stl/spleen.stl.\n", - "[2023-08-30 01:20:12,173] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", - "[2023-08-30 01:20:12,174] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "[2024-04-10 15:03:17,809] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 15:03:17,810] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2024-04-10 15:03:17,811] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 15:03:17,812] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:03:17,813] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 15:03:17,813] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 15:03:17,814] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:03:17,815] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 15:03:17,815] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 15:03:17,816] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:03:17,817] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 15:03:18,040] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts\n", + "[2024-04-10 15:03:24,142] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file output/stl/spleen.stl.\n", + "[2024-04-10 15:03:25,752] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", + "[2024-04-10 15:03:25,753] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", " warnings.warn(\n", - "[2023-08-30 01:20:22,822] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - " warnings.warn(msg)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - " warnings.warn(msg)\n", - "[2023-08-30 01:20:22,825] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 01:20:22,826] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 01:20:22,827] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 01:20:22,829] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 01:20:22,830] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "[2023-08-30 01:20:22,831] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "[2023-08-30 01:20:22,833] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "[2023-08-30 01:20:22,834] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "[2023-08-30 01:20:22,836] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "[2023-08-30 01:20:22,837] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "[2023-08-30 01:20:22,839] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "[2023-08-30 01:20:22,841] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "[2023-08-30 01:20:22,843] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "[2023-08-30 01:20:22,844] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "[2023-08-30 01:20:22,846] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "[2023-08-30 01:20:22,848] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "[2023-08-30 01:20:22,850] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "[2023-08-30 01:20:22,852] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "[2023-08-30 01:20:22,853] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "[2023-08-30 01:20:22,857] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "[2023-08-30 01:20:22,858] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "[2023-08-30 01:20:22,860] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "[2023-08-30 01:20:22,862] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "[2023-08-30 01:20:22,863] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "[2023-08-30 01:20:22,864] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "[2023-08-30 01:20:22,866] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "[2023-08-30 01:20:22,867] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "[2023-08-30 01:20:22,868] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "[2023-08-30 01:20:22,870] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "[2023-08-30 01:20:22,871] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "[2023-08-30 01:20:22,873] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "[2023-08-30 01:20:22,874] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "[2023-08-30 01:20:22,875] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "[2023-08-30 01:20:22,877] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "[2023-08-30 01:20:22,878] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "[2023-08-30 01:20:22,880] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "[2023-08-30 01:20:22,881] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "[2023-08-30 01:20:22,883] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "[2023-08-30 01:20:22,884] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "[2023-08-30 01:20:22,886] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "[2023-08-30 01:20:22,887] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "[2023-08-30 01:20:22,889] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "[2023-08-30 01:20:22,891] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "[2023-08-30 01:20:22,892] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "[2023-08-30 01:20:22,894] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "[2023-08-30 01:20:22,895] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "[2023-08-30 01:20:22,897] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "[2023-08-30 01:20:22,899] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "[2023-08-30 01:20:22,901] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "[2023-08-30 01:20:22,902] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "[2023-08-30 01:20:22,904] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "[2023-08-30 01:20:22,906] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "[2023-08-30 01:20:22,908] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "[2023-08-30 01:20:22,909] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "[2023-08-30 01:20:22,911] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "[2023-08-30 01:20:22,913] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "[2023-08-30 01:20:22,915] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "[2023-08-30 01:20:22,917] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "[2023-08-30 01:20:22,919] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "[2023-08-30 01:20:22,921] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "[2023-08-30 01:20:22,923] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 01:20:22,925] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 01:20:22,927] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 01:20:22,929] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 01:20:22,931] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 01:20:22,933] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 01:20:22,934] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 01:20:22,936] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "[2023-08-30 01:20:22,939] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "[2023-08-30 01:20:22,941] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "[2023-08-30 01:20:22,943] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "[2023-08-30 01:20:22,946] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "[2023-08-30 01:20:22,951] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "[2023-08-30 01:20:22,957] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "[2023-08-30 01:20:22,959] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "[2023-08-30 01:20:22,961] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "[2023-08-30 01:20:22,964] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "[2023-08-30 01:20:22,966] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "[2023-08-30 01:20:22,968] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "[2023-08-30 01:20:22,972] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "[2023-08-30 01:20:22,974] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "[2023-08-30 01:20:22,977] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "[2023-08-30 01:20:22,980] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "[2023-08-30 01:20:22,982] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "[2023-08-30 01:20:22,985] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "[2023-08-30 01:20:22,990] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "[2023-08-30 01:20:23,056] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:20:23,057] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 01:20:23,058] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:20:23,058] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 01:20:23,060] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 01:20:23,060] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:20:23,061] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 01:20:23,062] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 01:20:23,063] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 15:03:36,659] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:03:36,660] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 15:03:36,661] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:03:36,663] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 15:03:36,664] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 15:03:36,665] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:03:36,666] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 15:03:36,667] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 15:03:36,668] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[2023-08-30 01:20:23,159] [INFO] (__main__.AISpleenSegApp) - End run\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[2023-08-30 01:20:23,161] [INFO] (root) - End __main__\n" + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[2024-04-10 15:03:36,762] [INFO] (__main__.AISpleenSegApp) - End run\n", + "[2024-04-10 15:03:36,763] [INFO] (root) - End __main__\n" ] } ], @@ -1066,147 +973,52 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 01:20:29,553] [INFO] (root) - Parsed args: Namespace(argv=['my_app', '-i', 'dcm', '-o', 'output', '-m', 'models'], input=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/dcm'), log_level=None, model=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models'), output=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output'), workdir=None)\n", - "[2023-08-30 01:20:29,554] [INFO] (root) - AppContext object: AppContext(input_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/dcm, output_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output, model_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models, workdir=)\n", - "[2023-08-30 01:20:29,556] [INFO] (root) - End compose\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:210] Creating context\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1741] Activating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1771] Running Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 15:03:41,411] [INFO] (root) - Parsed args: Namespace(log_level=None, input=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/dcm'), output=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output'), model=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models'), workdir=None, argv=['my_app', '-i', 'dcm', '-o', 'output', '-m', 'models'])\n", + "[2024-04-10 15:03:41,413] [INFO] (root) - AppContext object: AppContext(input_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/dcm, output_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output, model_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models, workdir=)\n", + "[2024-04-10 15:03:41,414] [INFO] (root) - End compose\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:211] Creating context\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1864] Activating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1894] Running Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:190] Scheduling 8 entities\n", - "[2023-08-30 01:20:29,624] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Finding series for Selection named: CT Series\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 15:03:41,449] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 15:03:41,784] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 15:03:41,784] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", " # of series: 1\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Series attribute Modality value: CT\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:20:30,122] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:20:30,324] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary SaveImaged.__init__:resample: Current default value of argument `resample=True` has been deprecated since version 1.1. It will be changed to `resample=False` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "[2023-08-30 01:20:35,800] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/stl/spleen.stl.\n", - "[2023-08-30 01:20:37,096] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", - "[2023-08-30 01:20:37,096] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "[2024-04-10 15:03:41,784] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 15:03:41,784] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2024-04-10 15:03:41,784] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 15:03:41,784] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:03:41,785] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 15:03:41,785] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 15:03:41,785] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:03:41,785] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 15:03:41,785] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 15:03:41,785] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:03:41,785] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 15:03:42,204] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts\n", + "[2024-04-10 15:03:48,123] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/stl/spleen.stl.\n", + "[2024-04-10 15:03:49,607] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", + "[2024-04-10 15:03:49,607] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", " warnings.warn(\n", - "[2023-08-30 01:20:47,289] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - " warnings.warn(msg)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - " warnings.warn(msg)\n", - "[2023-08-30 01:20:47,291] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 01:20:47,291] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 01:20:47,292] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 01:20:47,293] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 01:20:47,293] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "[2023-08-30 01:20:47,294] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "[2023-08-30 01:20:47,294] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "[2023-08-30 01:20:47,295] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "[2023-08-30 01:20:47,296] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "[2023-08-30 01:20:47,296] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "[2023-08-30 01:20:47,297] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "[2023-08-30 01:20:47,297] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "[2023-08-30 01:20:47,298] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "[2023-08-30 01:20:47,298] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "[2023-08-30 01:20:47,299] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "[2023-08-30 01:20:47,300] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "[2023-08-30 01:20:47,300] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "[2023-08-30 01:20:47,301] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "[2023-08-30 01:20:47,301] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "[2023-08-30 01:20:47,302] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "[2023-08-30 01:20:47,302] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "[2023-08-30 01:20:47,304] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "[2023-08-30 01:20:47,304] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "[2023-08-30 01:20:47,305] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "[2023-08-30 01:20:47,305] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "[2023-08-30 01:20:47,306] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "[2023-08-30 01:20:47,306] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "[2023-08-30 01:20:47,307] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "[2023-08-30 01:20:47,308] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "[2023-08-30 01:20:47,308] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "[2023-08-30 01:20:47,309] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "[2023-08-30 01:20:47,309] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "[2023-08-30 01:20:47,310] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "[2023-08-30 01:20:47,310] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "[2023-08-30 01:20:47,311] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "[2023-08-30 01:20:47,312] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "[2023-08-30 01:20:47,312] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "[2023-08-30 01:20:47,313] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "[2023-08-30 01:20:47,313] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "[2023-08-30 01:20:47,314] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "[2023-08-30 01:20:47,314] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "[2023-08-30 01:20:47,315] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "[2023-08-30 01:20:47,316] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "[2023-08-30 01:20:47,316] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "[2023-08-30 01:20:47,317] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "[2023-08-30 01:20:47,317] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "[2023-08-30 01:20:47,318] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "[2023-08-30 01:20:47,318] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "[2023-08-30 01:20:47,319] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "[2023-08-30 01:20:47,320] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "[2023-08-30 01:20:47,320] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "[2023-08-30 01:20:47,321] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "[2023-08-30 01:20:47,321] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "[2023-08-30 01:20:47,322] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "[2023-08-30 01:20:47,322] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "[2023-08-30 01:20:47,323] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "[2023-08-30 01:20:47,324] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "[2023-08-30 01:20:47,324] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "[2023-08-30 01:20:47,325] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "[2023-08-30 01:20:47,325] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "[2023-08-30 01:20:47,326] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 01:20:47,327] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 01:20:47,327] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 01:20:47,328] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 01:20:47,328] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 01:20:47,329] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 01:20:47,329] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 01:20:47,330] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "[2023-08-30 01:20:47,331] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "[2023-08-30 01:20:47,331] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "[2023-08-30 01:20:47,332] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "[2023-08-30 01:20:47,332] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "[2023-08-30 01:20:47,333] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "[2023-08-30 01:20:47,333] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "[2023-08-30 01:20:47,334] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "[2023-08-30 01:20:47,335] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "[2023-08-30 01:20:47,335] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "[2023-08-30 01:20:47,336] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "[2023-08-30 01:20:47,336] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "[2023-08-30 01:20:47,337] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "[2023-08-30 01:20:47,337] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "[2023-08-30 01:20:47,338] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "[2023-08-30 01:20:47,339] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "[2023-08-30 01:20:47,339] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "[2023-08-30 01:20:47,340] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "[2023-08-30 01:20:47,340] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "[2023-08-30 01:20:47,387] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:20:47,387] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 01:20:47,388] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:20:47,388] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 01:20:47,388] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 01:20:47,388] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:20:47,388] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 01:20:47,388] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 01:20:47,388] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 15:03:59,411] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:03:59,412] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 15:03:59,412] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:03:59,412] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 15:03:59,412] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 15:03:59,412] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:03:59,412] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 15:03:59,412] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 15:03:59,412] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[2023-08-30 01:20:47,473] [INFO] (app.AISpleenSegApp) - End run\n" + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[2024-04-10 15:03:59,498] [INFO] (app.AISpleenSegApp) - End run\n" ] } ], @@ -1224,7 +1036,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.2.826.0.1.3680043.10.511.3.10023070564574692570777379407935822.dcm stl\n" + "1.2.826.0.1.3680043.10.511.3.39077956786032602526223766812104927.dcm stl\n" ] } ], @@ -1318,14 +1130,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 01:20:50,861] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app\n", - "[2023-08-30 01:20:50,861] [INFO] (packager.parameters) - Detected application type: Python Module\n", - "[2023-08-30 01:20:50,861] [INFO] (packager) - Scanning for models in {models_path}...\n", - "[2023-08-30 01:20:50,861] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.\n", - "[2023-08-30 01:20:50,861] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...\n", - "[2023-08-30 01:20:50,863] [INFO] (packager) - Generating app.json...\n", - "[2023-08-30 01:20:50,863] [INFO] (packager) - Generating pkg.json...\n", - "[2023-08-30 01:20:50,864] [DEBUG] (common) - \n", + "[2024-04-10 15:04:01,696] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app\n", + "[2024-04-10 15:04:01,697] [INFO] (packager.parameters) - Detected application type: Python Module\n", + "[2024-04-10 15:04:01,697] [INFO] (packager) - Scanning for models in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models...\n", + "[2024-04-10 15:04:01,697] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.\n", + "[2024-04-10 15:04:01,697] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...\n", + "[2024-04-10 15:04:01,701] [INFO] (packager) - Generating app.json...\n", + "[2024-04-10 15:04:01,701] [INFO] (packager) - Generating pkg.json...\n", + "[2024-04-10 15:04:01,714] [DEBUG] (common) - \n", "=============== Begin app.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", @@ -1353,21 +1165,21 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1.0,\n", " \"workingDirectory\": \"/var/holoscan\"\n", "}\n", "================ End app.json ================\n", " \n", - "[2023-08-30 01:20:50,864] [DEBUG] (common) - \n", + "[2024-04-10 15:04:01,715] [DEBUG] (common) - \n", "=============== Begin pkg.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", " \"applicationRoot\": \"/opt/holoscan/app\",\n", " \"modelRoot\": \"/opt/holoscan/models\",\n", " \"models\": {\n", - " \"model\": \"/opt/holoscan/models\"\n", + " \"model\": \"/opt/holoscan/models/model\"\n", " },\n", " \"resources\": {\n", " \"cpu\": 1,\n", @@ -1375,15 +1187,16 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"6Gi\"\n", " },\n", - " \"version\": 1.0\n", + " \"version\": 1.0,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "================ End pkg.json ================\n", " \n", - "[2023-08-30 01:20:50,888] [DEBUG] (packager.builder) - \n", + "[2024-04-10 15:04:01,749] [DEBUG] (packager.builder) - \n", "========== Begin Dockerfile ==========\n", "\n", "\n", - "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", + "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", "\n", "ENV DEBIAN_FRONTEND=noninteractive\n", "ENV TERM=xterm-256color\n", @@ -1399,11 +1212,13 @@ " && mkdir -p /var/holoscan/input \\\n", " && mkdir -p /var/holoscan/output\n", "\n", - "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\"\n", + "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\"\n", "LABEL tag=\"my_app:1.0\"\n", "LABEL org.opencontainers.image.title=\"MONAI Deploy App Package - MONAI Bundle AI App\"\n", "LABEL org.opencontainers.image.version=\"1.0\"\n", - "LABEL org.nvidia.holoscan=\"0.6.0\"\n", + "LABEL org.nvidia.holoscan=\"1.0.3\"\n", + "LABEL org.monai.deploy.app-sdk=\"0.5.1\"\n", + "\n", "\n", "ENV HOLOSCAN_ENABLE_HEALTH_CHECK=true\n", "ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input\n", @@ -1428,7 +1243,7 @@ "\n", "\n", "\n", - "RUN groupadd -g $GID $UNAME\n", + "RUN groupadd -f -g $GID $UNAME\n", "RUN useradd -rm -d /home/$UNAME -s /bin/bash -g $GID -G sudo -u $UID $UNAME\n", "RUN chown -R holoscan /var/holoscan \n", "RUN chown -R holoscan /var/holoscan/input \n", @@ -1453,13 +1268,12 @@ "RUN pip install --upgrade pip\n", "RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "\n", - "# Install Holoscan from PyPI org\n", - "RUN pip install holoscan==0.6.0\n", - "\n", + "# Install Holoscan from PyPI only when sdk_type is Holoscan. \n", + "# For MONAI Deploy, the APP SDK will install it unless user specifies the Holoscan SDK file.\n", "\n", "# Copy user-specified MONAI Deploy SDK file\n", - "COPY ./monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", - "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + "COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", "\n", "\n", "\n", @@ -1475,209 +1289,273 @@ "ENTRYPOINT [\"/var/holoscan/tools\"]\n", "=========== End Dockerfile ===========\n", "\n", - "[2023-08-30 01:20:50,889] [INFO] (packager.builder) - \n", + "[2024-04-10 15:04:01,749] [INFO] (packager.builder) - \n", "===============================================================================\n", "Building image for: x64-workstation\n", " Architecture: linux/amd64\n", - " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - " Build Image: N/A \n", + " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + " Build Image: N/A\n", " Cache: Enabled\n", " Configuration: dgpu\n", - " Holoiscan SDK Package: pypi.org\n", - " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + " Holoscan SDK Package: pypi.org\n", + " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", " gRPC Health Probe: N/A\n", - " SDK Version: 0.6.0\n", + " SDK Version: 1.0.3\n", " SDK: monai-deploy\n", " Tag: my_app-x64-workstation-dgpu-linux-amd64:1.0\n", " \n", - "[2023-08-30 01:20:51,572] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", - "[2023-08-30 01:20:51,573] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=my_app-x64-workstation-dgpu-linux-amd64:1.0\n", + "[2024-04-10 15:04:02,349] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", + "[2024-04-10 15:04:02,349] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=my_app-x64-workstation-dgpu-linux-amd64:1.0\n", + "#0 building with \"holoscan_app_builder\" instance using docker-container driver\n", + "\n", "#1 [internal] load build definition from Dockerfile\n", - "#1 transferring dockerfile: 2.67kB done\n", + "#1 transferring dockerfile: 2.80kB done\n", "#1 DONE 0.1s\n", "\n", - "#2 [internal] load .dockerignore\n", - "#2 transferring context: 1.79kB done\n", - "#2 DONE 0.1s\n", + "#2 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#2 DONE 0.4s\n", "\n", - "#3 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#3 DONE 0.4s\n", + "#3 [internal] load .dockerignore\n", + "#3 transferring context: 1.79kB done\n", + "#3 DONE 0.1s\n", "\n", "#4 [internal] load build context\n", "#4 DONE 0.0s\n", "\n", - "#5 importing cache manifest from local:2428133242780292460\n", + "#5 importing cache manifest from local:18341943591062161743\n", + "#5 inferred cache manifest type: application/vnd.oci.image.index.v1+json done\n", "#5 DONE 0.0s\n", "\n", - "#6 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#6 DONE 0.9s\n", + "#6 [ 1/21] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155\n", + "#6 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155 0.0s done\n", + "#6 DONE 0.1s\n", "\n", - "#7 [ 1/22] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc\n", - "#7 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc\n", - "#7 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc 0.1s done\n", - "#7 DONE 0.1s\n", + "#7 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#7 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done\n", + "#7 DONE 0.7s\n", "\n", "#4 [internal] load build context\n", - "#4 transferring context: 19.57MB 0.2s done\n", - "#4 DONE 0.3s\n", + "#4 transferring context: 19.56MB 0.1s done\n", + "#4 DONE 0.2s\n", "\n", - "#8 [ 6/22] RUN chown -R holoscan /var/holoscan\n", + "#8 [ 8/21] RUN chown -R holoscan /var/holoscan/output\n", "#8 CACHED\n", "\n", - "#9 [16/22] COPY ./monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + "#9 [13/21] RUN pip install --upgrade pip\n", "#9 CACHED\n", "\n", - "#10 [17/22] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + "#10 [ 4/21] RUN groupadd -f -g 1000 holoscan\n", "#10 CACHED\n", "\n", - "#11 [19/22] COPY ./map/app.json /etc/holoscan/app.json\n", + "#11 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", "#11 CACHED\n", "\n", - "#12 [ 5/22] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", + "#12 [10/21] COPY ./tools /var/holoscan/tools\n", "#12 CACHED\n", "\n", - "#13 [14/22] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", + "#13 [ 6/21] RUN chown -R holoscan /var/holoscan\n", "#13 CACHED\n", "\n", - "#14 [ 9/22] WORKDIR /var/holoscan\n", + "#14 [ 2/21] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", "#14 CACHED\n", "\n", - "#15 [ 8/22] RUN chown -R holoscan /var/holoscan/output\n", + "#15 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt\n", "#15 CACHED\n", "\n", - "#16 [13/22] RUN pip install --upgrade pip\n", + "#16 [ 9/21] WORKDIR /var/holoscan\n", "#16 CACHED\n", "\n", - "#17 [15/22] RUN pip install holoscan==0.6.0\n", + "#17 [ 7/21] RUN chown -R holoscan /var/holoscan/input\n", "#17 CACHED\n", "\n", - "#18 [10/22] COPY ./tools /var/holoscan/tools\n", + "#18 [11/21] RUN chmod +x /var/holoscan/tools\n", "#18 CACHED\n", "\n", - "#19 [18/22] COPY ./models /opt/holoscan/models\n", + "#19 [ 3/21] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", "#19 CACHED\n", "\n", - "#20 [20/22] COPY ./app.config /var/holoscan/app.yaml\n", + "#20 [14/21] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "#20 CACHED\n", "\n", - "#21 [12/22] COPY ./pip/requirements.txt /tmp/requirements.txt\n", - "#21 CACHED\n", - "\n", - "#22 [ 7/22] RUN chown -R holoscan /var/holoscan/input\n", - "#22 CACHED\n", - "\n", - "#23 [ 4/22] RUN groupadd -g 1000 holoscan\n", - "#23 CACHED\n", - "\n", - "#24 [ 3/22] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", - "#24 CACHED\n", - "\n", - "#25 [ 2/22] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", - "#25 CACHED\n", - "\n", - "#26 [11/22] RUN chmod +x /var/holoscan/tools\n", - "#26 CACHED\n", - "\n", - "#27 [21/22] COPY ./map/pkg.json /etc/holoscan/pkg.json\n", - "#27 CACHED\n", - "\n", - "#28 [22/22] COPY ./app /opt/holoscan/app\n", - "#28 DONE 0.3s\n", - "\n", - "#29 exporting to docker image format\n", - "#29 exporting layers\n", - "#29 exporting layers 0.2s done\n", - "#29 exporting manifest sha256:84725c6be7300f1d3487cf953efea1b7123df1b79dc893f79dd41e9b714cc971 0.0s done\n", - "#29 exporting config sha256:716356b4f3c03984961a626a47638b2538cf18516f267b54c7d0f502aa0ab077\n", - "#29 exporting config sha256:716356b4f3c03984961a626a47638b2538cf18516f267b54c7d0f502aa0ab077 0.0s done\n", - "#29 sending tarball\n", - "#29 ...\n", - "\n", - "#30 importing to docker\n", - "#30 DONE 0.8s\n", - "\n", - "#29 exporting to docker image format\n", - "#29 sending tarball 54.7s done\n", - "#29 DONE 54.9s\n", - "\n", - "#31 exporting content cache\n", - "#31 preparing build cache for export\n", - "#31 writing layer sha256:0709800848b4584780b40e7e81200689870e890c38b54e96b65cd0a3b1942f2d done\n", - "#31 writing layer sha256:0ce020987cfa5cd1654085af3bb40779634eb3d792c4a4d6059036463ae0040d done\n", - "#31 writing layer sha256:0f4bc5775dfef844ad94316d6cba08f7430019a5986278e18978fdf8fd6370d0 done\n", - "#31 writing layer sha256:0f65089b284381bf795d15b1a186e2a8739ea957106fa526edef0d738e7cda70 done\n", - "#31 writing layer sha256:12a47450a9f9cc5d4edab65d0f600dbbe8b23a1663b0b3bb2c481d40e074b580 done\n", - "#31 writing layer sha256:1de965777e2e37c7fabe00bdbf3d0203ca83ed30a71a5479c3113fe4fc48c4bb done\n", - "#31 writing layer sha256:1e6d878a29f0eee28390766120813fdf36893f516bcc029e698cd941eeb79616 done\n", - "#31 writing layer sha256:24b5aa2448e920814dd67d7d3c0169b2cdacb13c4048d74ded3b4317843b13ff done\n", - "#31 writing layer sha256:2789e1f0e19719b047679b4b490cab1edb9e151cd286aed22df08022c249f040 done\n", - "#31 writing layer sha256:2d42104dbf0a7cc962b791f6ab4f45a803f8a36d296f996aca180cfb2f3e30d0 done\n", - "#31 writing layer sha256:2fa1ce4fa3fec6f9723380dc0536b7c361d874add0baaddc4bbf2accac82d2ff done\n", - "#31 writing layer sha256:38794be1b5dc99645feabf89b22cd34fb5bdffb5164ad920e7df94f353efe9c0 done\n", - "#31 writing layer sha256:38f963dc57c1e7b68a738fe39ed9f9345df7188111a047e2163a46648d7f1d88 done\n", - "#31 writing layer sha256:3e7e4c9bc2b136814c20c04feb4eea2b2ecf972e20182d88759931130cfb4181 done\n", - "#31 writing layer sha256:3fd77037ad585442cd82d64e337f49a38ddba50432b2a1e563a48401d25c79e6 done\n", - "#31 writing layer sha256:41814ed91034b30ac9c44dfc604a4bade6138005ccf682372c02e0bead66dbc0\n", - "#31 writing layer sha256:41814ed91034b30ac9c44dfc604a4bade6138005ccf682372c02e0bead66dbc0 done\n", - "#31 writing layer sha256:45893188359aca643d5918c9932da995364dc62013dfa40c075298b1baabece3 done\n", - "#31 writing layer sha256:49bc651b19d9e46715c15c41b7c0daa007e8e25f7d9518f04f0f06592799875a done\n", - "#31 writing layer sha256:4c12db5118d8a7d909e4926d69a2192d2b3cd8b110d49c7504a4f701258c1ccc done\n", - "#31 writing layer sha256:4cc43a803109d6e9d1fd35495cef9b1257035f5341a2db54f7a1940815b6cc65 done\n", - "#31 writing layer sha256:4d32b49e2995210e8937f0898327f196d3fcc52486f0be920e8b2d65f150a7ab done\n", - "#31 writing layer sha256:4d6fe980bad9cd7b2c85a478c8033cae3d098a81f7934322fb64658b0c8f9854 done\n", - "#31 writing layer sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 done\n", - "#31 writing layer sha256:50b2500ad4a5ad2f73d71f4dedecabff852c74ea78a97dab0fc86b2ed44ddc77 done\n", - "#31 writing layer sha256:5150182f1ff123399b300ca469e00f6c4d82e1b9b72652fb8ee7eab370245236 done\n", - "#31 writing layer sha256:5450ec6233e924dcdedf28ae862b64cced3ba8d460257e793e0a31c605e8bbc8 0.0s done\n", - "#31 writing layer sha256:595c38fa102c61c3dda19bdab70dcd26a0e50465b986d022a84fa69023a05d0f done\n", - "#31 writing layer sha256:59d451175f6950740e26d38c322da0ef67cb59da63181eb32996f752ba8a2f17 done\n", - "#31 writing layer sha256:5ad1f2004580e415b998124ea394e9d4072a35d70968118c779f307204d6bd17 done\n", - "#31 writing layer sha256:5e2c1cbc09286c26c04d5b4257b11940ecdb161330319d54feadc7ef9a8dc8f6 done\n", - "#31 writing layer sha256:62598eafddf023e7f22643485f4321cbd51ff7eee743b970db12454fd3c8c675 done\n", - "#31 writing layer sha256:63d7e616a46987136f4cc9eba95db6f6327b4854cfe3c7e20fed6db0c966e380 done\n", - "#31 writing layer sha256:6939d591a6b09b14a437e5cd2d6082a52b6d76bec4f72d960440f097721da34f done\n", - "#31 writing layer sha256:698318e5a60e5e0d48c45bf992f205a9532da567fdfe94bd59be2e192975dd6f done\n", - "#31 writing layer sha256:6ddc1d0f91833b36aac1c6f0c8cea005c87d94bab132d46cc06d9b060a81cca3 done\n", - "#31 writing layer sha256:74ac1f5a47c0926bff1e997bb99985a09926f43bd0895cb27ceb5fa9e95f8720 done\n", - "#31 writing layer sha256:7577973918dd30e764733a352a93f418000bc3181163ca451b2307492c1a6ba9 done\n", - "#31 writing layer sha256:886c886d8a09d8befb92df75dd461d4f97b77d7cff4144c4223b0d2f6f2c17f2 done\n", - "#31 writing layer sha256:8a7451db9b4b817b3b33904abddb7041810a4ffe8ed4a034307d45d9ae9b3f2a done\n", - "#31 writing layer sha256:916f4054c6e7f10de4fd7c08ffc75fa23ebecca4eceb8183cb1023b33b1696c9 done\n", - "#31 writing layer sha256:9463aa3f56275af97693df69478a2dc1d171f4e763ca6f7b6f370a35e605c154 done\n", - "#31 writing layer sha256:955fd173ed884230c2eded4542d10a97384b408537be6bbb7c4ae09ccd6fb2d0 done\n", - "#31 writing layer sha256:9c42a4ee99755f441251e6043b2cbba16e49818a88775e7501ec17e379ce3cfd done\n", - "#31 writing layer sha256:9c63be0a86e3dc4168db3814bf464e40996afda0031649d9faa8ff7568c3154f done\n", - "#31 writing layer sha256:9e04bda98b05554953459b5edef7b2b14d32f1a00b979a23d04b6eb5c191e66b done\n", - "#31 writing layer sha256:a4a0c690bc7da07e592514dccaa26098a387e8457f69095e922b6d73f7852502 done\n", - "#31 writing layer sha256:a4aafbc094d78a85bef41036173eb816a53bcd3e2564594a32f542facdf2aba6 done\n", - "#31 writing layer sha256:ae36a4d38b76948e39a5957025c984a674d2de18ce162a8caaa536e6f06fccea done\n", - "#31 writing layer sha256:b2fa40114a4a0725c81b327df89c0c3ed5c05ca9aa7f1157394d5096cf5460ce done\n", - "#31 writing layer sha256:b48a5fafcaba74eb5d7e7665601509e2889285b50a04b5b639a23f8adc818157 done\n", - "#31 writing layer sha256:c657dd855c8726b050f2b5bd6f4999883fff6803fe9f22add96f6d3ff89cd477 done\n", - "#31 writing layer 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preparing build cache for export 0.7s done\n", - "#31 writing layer sha256:e94a4481e9334ff402bf90628594f64a426672debbdfb55f1290802e52013907 done\n", - "#31 writing layer sha256:eaf45e9f32d1f5a9983945a1a9f8dedbb475bc0f578337610e00b4dedec87c20 done\n", - "#31 writing layer sha256:eb411bef39c013c9853651e68f00965dbd826d829c4e478884a2886976e9c989 done\n", - "#31 writing layer sha256:edfe4a95eb6bd3142aeda941ab871ffcc8c19cf50c33561c210ba8ead2424759 done\n", - "#31 writing layer sha256:ef4466d6f927d29d404df9c5af3ef5733c86fa14e008762c90110b963978b1e7 done\n", - "#31 writing layer sha256:f346e3ecdf0bee048fa1e3baf1d3128ff0283b903f03e97524944949bd8882e5 done\n", - "#31 writing layer sha256:f3f9a00a1ce9aadda250aacb3e66a932676badc5d8519c41517fdf7ea14c13ed done\n", - "#31 writing layer sha256:f7a50dafd51c2bcaad0ede31fbf29c38fe66776ade008a7fbdb07dba39de7f97 done\n", - "#31 writing layer sha256:fd849d9bd8889edd43ae38e9f21a912430c8526b2c18f3057a3b2cd74eb27b31 done\n", - "#31 writing config sha256:b0a64afdeb53276373de9d6facb2d12c84bc72fa642ca0ff57e9fd720b1e7168 0.0s done\n", - "#31 writing manifest sha256:ec78676329581462682bcf9e88a75e1c58d7ddb5232df774218a8c110a6ed892 0.0s done\n", - "#31 DONE 0.7s\n", - "[2023-08-30 01:21:50,557] [INFO] (packager) - Build Summary:\n", + "#21 [15/21] COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#21 DONE 0.2s\n", + "\n", + "#22 [16/21] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#22 0.845 Defaulting to user installation because normal site-packages is not writeable\n", + "#22 0.979 Processing /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "#22 0.993 Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (1.23.5)\n", + "#22 1.183 Collecting holoscan~=1.0 (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.336 Downloading holoscan-1.0.3-cp310-cp310-manylinux_2_35_x86_64.whl.metadata (4.1 kB)\n", + "#22 1.412 Collecting colorama>=0.4.1 (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.416 Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n", + "#22 1.507 Collecting typeguard>=3.0.0 (from monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.512 Downloading typeguard-4.2.1-py3-none-any.whl.metadata (3.7 kB)\n", + "#22 1.611 Collecting pip==23.3.2 (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty)\n", + "#22 1.615 Downloading pip-23.3.2-py3-none-any.whl.metadata (3.5 kB)\n", + "#22 1.632 Requirement already satisfied: cupy-cuda12x==12.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (12.2.0)\n", + "#22 1.635 Requirement already satisfied: cloudpickle==2.2.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (2.2.1)\n", + "#22 1.638 Requirement already satisfied: python-on-whales==0.60.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (0.60.1)\n", + "#22 1.641 Requirement already satisfied: Jinja2==3.1.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (3.1.2)\n", + "#22 1.643 Requirement already satisfied: packaging==23.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (23.1)\n", + "#22 1.644 Requirement already satisfied: pyyaml==6.0 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (6.0)\n", + "#22 1.645 Requirement already satisfied: requests==2.28.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=1.0->monai-deploy-app-sdk==0.5.1+25.g31e4165.dirty) (2.28.2)\n", + "#22 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colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n", + "#22 3.052 Downloading holoscan-1.0.3-cp310-cp310-manylinux_2_35_x86_64.whl (33.6 MB)\n", + "#22 7.685 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 33.6/33.6 MB 7.3 MB/s eta 0:00:00\n", + "#22 7.692 Downloading pip-23.3.2-py3-none-any.whl (2.1 MB)\n", + "#22 7.738 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 52.2 MB/s eta 0:00:00\n", + "#22 7.745 Downloading typeguard-4.2.1-py3-none-any.whl (34 kB)\n", + "#22 7.771 Downloading wheel_axle_runtime-0.0.5-py3-none-any.whl (12 kB)\n", + "#22 8.166 Installing collected packages: wheel-axle-runtime, typeguard, pip, colorama, holoscan, monai-deploy-app-sdk\n", + "#22 8.245 Attempting uninstall: pip\n", + "#22 8.248 Found existing installation: pip 24.0\n", + "#22 8.310 Uninstalling pip-24.0:\n", + "#22 8.744 Successfully uninstalled pip-24.0\n", + "#22 10.41 Successfully installed colorama-0.4.6 holoscan-1.0.3 monai-deploy-app-sdk-0.5.1+25.g31e4165.dirty pip-23.3.2 typeguard-4.2.1 wheel-axle-runtime-0.0.5\n", + "#22 DONE 11.1s\n", + "\n", + "#23 [17/21] COPY ./models /opt/holoscan/models\n", + "#23 DONE 0.2s\n", + "\n", + "#24 [18/21] COPY ./map/app.json /etc/holoscan/app.json\n", + "#24 DONE 0.1s\n", + "\n", + "#25 [19/21] COPY ./app.config /var/holoscan/app.yaml\n", + "#25 DONE 0.1s\n", + "\n", + "#26 [20/21] COPY ./map/pkg.json /etc/holoscan/pkg.json\n", + "#26 DONE 0.1s\n", + "\n", + "#27 [21/21] COPY ./app /opt/holoscan/app\n", + "#27 DONE 0.1s\n", + "\n", + "#28 exporting to docker image format\n", + "#28 exporting layers\n", + "#28 exporting layers 6.0s done\n", + "#28 exporting manifest sha256:3b3b2102892c64900945ca4d84a328af1fb7350c84cb4d85ecee3b94f48ed85c 0.0s done\n", + "#28 exporting config sha256:d6463325d6640490c117da76b90d02687331b2ee1e486047f7c4c968023c3d89 0.0s done\n", + "#28 sending tarball\n", + "#28 ...\n", + "\n", + "#29 importing to docker\n", + "#29 loading layer a292666cef7b 32.77kB / 125.57kB\n", + "#29 loading layer 23d575e38e71 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sha256:e5d9fee7e7dacd6052fe7c78ac5738f1fb693aa068f7c6064c70e98941288a52 0.0s done\n", + "#30 writing layer sha256:e8640a108802cd7519cc53dceb74f7a5c94b562662f1c3c040c2aa6571acf0f3 0.0s done\n", + "#30 writing layer sha256:e8acb678f16bc0c369d5cf9c184f2d3a1c773986816526e5e3e9c0354f7e757f\n", + "#30 preparing build cache for export 2.5s done\n", + "#30 writing layer sha256:e8acb678f16bc0c369d5cf9c184f2d3a1c773986816526e5e3e9c0354f7e757f done\n", + "#30 writing layer sha256:e9225f7ab6606813ec9acba98a064826ebfd6713a9645a58cd068538af1ecddb done\n", + "#30 writing layer sha256:f33546e75bf1a7d9dc9e21b9a2c54c9d09b24790ad7a4192a8509002ceb14688 done\n", + "#30 writing layer sha256:f608e2fbff86e98627b7e462057e7d2416522096d73fe4664b82fe6ce8a4047d done\n", + "#30 writing layer sha256:f7702077ced42a1ee35e7f5e45f72634328ff3bcfe3f57735ba80baa5ec45daf done\n", + "#30 writing layer sha256:fa66a49172c6e821a1bace57c007c01da10cbc61507c44f8cdfeed8c4e5febab done\n", + "#30 writing config sha256:386c66fca187580548b7c0d95c25fecfb72f326f30df509c9860b977d7e32763 0.0s done\n", + "#30 writing cache manifest sha256:ebd62b21fd87e8654814f8093dffbbf555dced752810b2798c59b2b1a08ce0fd 0.0s done\n", + "#30 DONE 2.5s\n", + "[2024-04-10 15:05:33,378] [INFO] (packager) - Build Summary:\n", "\n", "Platform: x64-workstation/dgpu\n", " Status: Succeeded\n", @@ -1708,7 +1586,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "my_app-x64-workstation-dgpu-linux-amd64 1.0 716356b4f3c0 58 seconds ago 15.4GB\n" + "my_app-x64-workstation-dgpu-linux-amd64 1.0 d6463325d664 About a minute ago 17.5GB\n" ] } ], @@ -1766,7 +1644,7 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1,\n", " \"workingDirectory\": \"/var/holoscan\"\n", @@ -1778,7 +1656,7 @@ " \"applicationRoot\": \"/opt/holoscan/app\",\n", " \"modelRoot\": \"/opt/holoscan/models\",\n", " \"models\": {\n", - " \"model\": \"/opt/holoscan/models\"\n", + " \"model\": \"/opt/holoscan/models/model\"\n", " },\n", " \"resources\": {\n", " \"cpu\": 1,\n", @@ -1786,19 +1664,20 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"6Gi\"\n", " },\n", - " \"version\": 1\n", + " \"version\": 1,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "\n", - "2023-08-30 08:21:57 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app\n", + "2024-04-10 22:05:37 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app\n", "\n", - "2023-08-30 08:21:57 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json\n", - "2023-08-30 08:21:57 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json\n", - "2023-08-30 08:21:57 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml\n", + "2024-04-10 22:05:37 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json\n", + "2024-04-10 22:05:37 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json\n", + "2024-04-10 22:05:37 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml\n", "\n", - "2023-08-30 08:21:57 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models\n", + "2024-04-10 22:05:37 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models\n", "\n", - "2023-08-30 08:21:57 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs\n", - "2023-08-30 08:21:57 [INFO] '/opt/holoscan/docs/' cannot be found.\n", + "2024-04-10 22:05:37 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs\n", + "2024-04-10 22:05:37 [INFO] '/opt/holoscan/docs/' cannot be found.\n", "\n", "app config models\n" ] @@ -1830,20 +1709,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 01:22:01,097] [INFO] (runner) - Checking dependencies...\n", - "[2023-08-30 01:22:01,097] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "[2024-04-10 15:05:39,448] [INFO] (runner) - Checking dependencies...\n", + "[2024-04-10 15:05:39,448] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "\n", + "[2024-04-10 15:05:39,449] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", "\n", - "[2023-08-30 01:22:01,098] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", + "[2024-04-10 15:05:39,449] [INFO] (runner) - --> Verifying if \"my_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", "\n", - "[2023-08-30 01:22:01,098] [INFO] (runner) - --> Verifying if \"my_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", + "[2024-04-10 15:05:39,511] [INFO] (runner) - Reading HAP/MAP manifest...\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmpba1clh6t/app.json\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmpba1clh6t/pkg.json\n", + "[2024-04-10 15:05:39,743] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", "\n", - "[2023-08-30 01:22:01,170] [INFO] (runner) - Reading HAP/MAP manifest...\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmpp_510e7z/app.json\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmpp_510e7z/pkg.json\n", - "[2023-08-30 01:22:01,356] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", + "[2024-04-10 15:05:39,744] [INFO] (runner) - --> Verifying \"nvidia-ctk\" version...\n", "\n", - "[2023-08-30 01:22:01,563] [INFO] (common) - Launching container (03a25b708327) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", - " container name: elated_heyrovsky\n", + "[2024-04-10 15:05:40,086] [INFO] (common) - Launching container (1d77a7676232) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", + " container name: festive_hermann\n", " host name: mingq-dt\n", " network: host\n", " user: 1000:1000\n", @@ -1852,103 +1733,96 @@ " ipc mode: host\n", " shared memory size: 67108864\n", " devices: \n", - "2023-08-30 08:22:02 [INFO] Launching application python3 /opt/holoscan/app ...\n", + " group_add: 44\n", + "2024-04-10 22:05:40 [INFO] Launching application python3 /opt/holoscan/app ...\n", "\n", - "[2023-08-30 08:22:07,078] [INFO] (root) - Parsed args: Namespace(argv=['/opt/holoscan/app'], input=None, log_level=None, model=None, output=None, workdir=None)\n", + "[2024-04-10 22:05:45,192] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['/opt/holoscan/app'])\n", "\n", - "[2023-08-30 08:22:07,081] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", + "[2024-04-10 22:05:45,195] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", "\n", - "[2023-08-30 08:22:07,082] [INFO] (root) - End compose\n", + "[2024-04-10 22:05:45,197] [INFO] (root) - End compose\n", "\n", - "[info] [app_driver.cpp:1025] Launching the driver/health checking service\n", + "[info] [app_driver.cpp:1161] Launching the driver/health checking service\n", "\n", - "[info] [gxf_executor.cpp:210] Creating context\n", + "[info] [gxf_executor.cpp:211] Creating context\n", "\n", - "[info] [server.cpp:73] Health checking server listening on 0.0.0.0:8777\n", + "[info] [server.cpp:87] Health checking server listening on 0.0.0.0:8777\n", "\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", "\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", "\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", "\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "\n", "[info] [greedy_scheduler.cpp:190] Scheduling 8 entities\n", "\n", - "[2023-08-30 08:22:07,211] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 22:05:45,297] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", "\n", - "[2023-08-30 08:22:07,580] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 22:05:46,131] [INFO] (root) - Finding series for Selection named: CT Series\n", "\n", - "[2023-08-30 08:22:07,580] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 22:05:46,131] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", "\n", " # of series: 1\n", "\n", - "[2023-08-30 08:22:07,580] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "\n", - "[2023-08-30 08:22:07,580] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2024-04-10 22:05:46,131] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", "\n", - "[2023-08-30 08:22:07,580] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 22:05:46,131] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", "\n", - "[2023-08-30 08:22:07,581] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 22:05:46,131] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", "\n", - "[2023-08-30 08:22:07,581] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 22:05:46,131] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", "\n", - "[2023-08-30 08:22:07,581] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 22:05:46,131] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", "\n", - "[2023-08-30 08:22:07,581] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 22:05:46,131] [INFO] (root) - Series attribute Modality value: CT\n", "\n", - "[2023-08-30 08:22:07,581] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 22:05:46,132] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", "\n", - "[2023-08-30 08:22:07,581] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 22:05:46,132] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", "\n", - "[2023-08-30 08:22:07,581] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 22:05:46,132] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", "\n", - "[2023-08-30 08:22:07,581] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 22:05:46,132] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", "\n", - "[2023-08-30 08:22:08,032] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/model/model.ts\n", + "[2024-04-10 22:05:46,132] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", + "[2024-04-10 22:05:46,349] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/model/model.ts\n", "\n", - " warn_deprecated(argname, msg, warning_category)\n", + "[2024-04-10 22:05:49,959] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file /var/holoscan/output/stl/spleen.stl.\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary SaveImaged.__init__:resample: Current default value of argument `resample=True` has been deprecated since version 1.1. It will be changed to `resample=False` in version 1.3.\n", + "[2024-04-10 22:05:51,433] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", "\n", - " warn_deprecated(argname, msg, warning_category)\n", + "[2024-04-10 22:05:51,433] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", "\n", - "[2023-08-30 08:22:18,434] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file /var/holoscan/output/stl/spleen.stl.\n", - "\n", - "[2023-08-30 08:22:19,925] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image\n", - "\n", - "[2023-08-30 08:22:19,925] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)\n", - "\n", - "Exception occurred for operator: 'stl_conversion_op'\n", + "Exception occurred in compute method of operator: 'stl_conversion_op'\n", "\n", "Traceback (most recent call last):\n", "\n", - " File \"/home/holoscan/.local/lib/python3.8/site-packages/monai/deploy/operators/stl_conversion_operator.py\", line 118, in compute\n", + " File \"/home/holoscan/.local/lib/python3.10/site-packages/monai/deploy/operators/stl_conversion_operator.py\", line 118, in compute\n", "\n", " stl_bytes = self._convert(input_image, _output_file)\n", "\n", - " File \"/home/holoscan/.local/lib/python3.8/site-packages/monai/deploy/operators/stl_conversion_operator.py\", line 135, in _convert\n", + " File \"/home/holoscan/.local/lib/python3.10/site-packages/monai/deploy/operators/stl_conversion_operator.py\", line 135, in _convert\n", "\n", " return self._converter.convert(\n", "\n", - " File \"/home/holoscan/.local/lib/python3.8/site-packages/monai/deploy/operators/stl_conversion_operator.py\", line 182, in convert\n", + " File \"/home/holoscan/.local/lib/python3.10/site-packages/monai/deploy/operators/stl_conversion_operator.py\", line 182, in convert\n", "\n", " nda = STLConverter.get_largest_cc(nda)\n", "\n", - " File \"/home/holoscan/.local/lib/python3.8/site-packages/monai/deploy/operators/stl_conversion_operator.py\", line 255, in get_largest_cc\n", + " File \"/home/holoscan/.local/lib/python3.10/site-packages/monai/deploy/operators/stl_conversion_operator.py\", line 255, in get_largest_cc\n", "\n", " labels = label(nda)\n", "\n", - " File \"/home/holoscan/.local/lib/python3.8/site-packages/monai/deploy/utils/importutil.py\", line 274, in __call__\n", + " File \"/home/holoscan/.local/lib/python3.10/site-packages/monai/deploy/utils/importutil.py\", line 274, in __call__\n", "\n", " raise self._exception\n", "\n", - " File \"/home/holoscan/.local/lib/python3.8/site-packages/monai/deploy/utils/importutil.py\", line 226, in optional_import\n", + " File \"/home/holoscan/.local/lib/python3.10/site-packages/monai/deploy/utils/importutil.py\", line 226, in optional_import\n", "\n", " pkg = __import__(module) # top level module\n", "\n", @@ -1960,223 +1834,41 @@ "\n", " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "/home/holoscan/.local/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", "\n", " warnings.warn(\n", "\n", - "[2023-08-30 08:22:22,121] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - "\n", - " warnings.warn(msg)\n", - "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - "\n", - " warnings.warn(msg)\n", - "\n", - "[2023-08-30 08:22:22,124] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,125] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,126] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,127] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,127] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,128] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,129] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,129] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,130] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,131] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,131] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,132] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,133] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,133] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,134] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,134] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,135] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,136] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,137] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,137] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,138] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,139] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,140] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,141] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,141] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,142] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,143] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,144] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,145] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,146] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,147] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,148] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,148] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,149] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,150] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,151] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,152] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,152] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", + "[2024-04-10 22:05:52,708] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:22:22,153] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", + "[2024-04-10 22:05:52,708] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", "\n", - "[2023-08-30 08:22:22,153] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", + "[2024-04-10 22:05:52,708] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:22:22,154] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", + "[2024-04-10 22:05:52,708] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", "\n", - "[2023-08-30 08:22:22,155] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", + "[2024-04-10 22:05:52,709] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", "\n", - "[2023-08-30 08:22:22,155] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", + "[2024-04-10 22:05:52,709] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:22:22,156] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", + "[2024-04-10 22:05:52,709] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", "\n", - "[2023-08-30 08:22:22,157] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", + "[2024-04-10 22:05:52,709] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", "\n", - "[2023-08-30 08:22:22,158] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,158] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,159] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,160] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,160] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,161] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,162] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,162] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,163] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,163] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,164] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,165] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,165] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,166] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,167] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,167] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,168] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,168] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,169] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,170] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,170] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,171] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,172] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,172] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,173] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,173] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,174] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,175] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,175] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,176] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,177] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,178] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,178] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,179] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,180] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,180] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,181] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,181] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,182] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,183] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,183] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "\n", - "[2023-08-30 08:22:22,223] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "\n", - "[2023-08-30 08:22:22,224] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "\n", - "[2023-08-30 08:22:22,224] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "\n", - "[2023-08-30 08:22:22,224] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "\n", - "[2023-08-30 08:22:22,224] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "\n", - "[2023-08-30 08:22:22,224] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "\n", - "[2023-08-30 08:22:22,224] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "\n", - "[2023-08-30 08:22:22,224] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "\n", - "[2023-08-30 08:22:22,225] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 22:05:52,709] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "\n", "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", "\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", "\n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", "\n", - "[2023-08-30 08:22:22,346] [INFO] (app.AISpleenSegApp) - End run\n", + "[2024-04-10 22:05:52,805] [INFO] (app.AISpleenSegApp) - End run\n", "\n", - "[2023-08-30 01:22:23,709] [INFO] (common) - Container 'elated_heyrovsky'(03a25b708327) exited.\n" + "[2024-04-10 15:05:54,069] [INFO] (common) - Container 'festive_hermann'(1d77a7676232) exited.\n" ] } ], @@ -2195,15 +1887,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.2.826.0.1.3680043.10.511.3.39359760221330773075218270807121109.dcm stl\n" - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + "1.2.826.0.1.3680043.10.511.3.33232544284800485207819891596585914.dcm stl\n" ] } ], @@ -2228,7 +1912,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" }, "vscode": { "interpreter": { diff --git a/notebooks/tutorials/05_multi_model_app.ipynb b/notebooks/tutorials/05_multi_model_app.ipynb index e773fafd..2b505888 100644 --- a/notebooks/tutorials/05_multi_model_app.ipynb +++ b/notebooks/tutorials/05_multi_model_app.ipynb @@ -175,23 +175,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: gdown in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (4.7.1)\n", - "Requirement already satisfied: filelock in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (3.12.2)\n", - "Requirement already satisfied: requests[socks] in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (2.31.0)\n", - "Requirement already satisfied: six in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (1.16.0)\n", - "Requirement already satisfied: tqdm in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (4.66.1)\n", - "Requirement already satisfied: beautifulsoup4 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from gdown) (4.12.2)\n", - "Requirement already satisfied: soupsieve>1.2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from beautifulsoup4->gdown) (2.4.1)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (3.2.0)\n", - "Requirement already satisfied: idna<4,>=2.5 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (3.4)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (2.0.4)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (2023.7.22)\n", - "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages (from requests[socks]->gdown) (1.7.1)\n", + "Requirement already satisfied: gdown in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (5.1.0)\n", + "Requirement already satisfied: beautifulsoup4 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (4.12.3)\n", + "Requirement already satisfied: filelock in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (3.13.3)\n", + "Requirement already satisfied: requests[socks] in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (2.28.2)\n", + "Requirement already satisfied: tqdm in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from gdown) (4.66.2)\n", + "Requirement already satisfied: soupsieve>1.2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from beautifulsoup4->gdown) (2.5)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (3.6)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (1.26.18)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (2024.2.2)\n", + "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages (from requests[socks]->gdown) (1.7.1)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Downloading...\n", - "From (uriginal): https://drive.google.com/uc?id=1llJ4NGNTjY187RLX4MtlmHYhfGxBNWmd\n", - "From (redirected): https://drive.google.com/uc?id=1llJ4NGNTjY187RLX4MtlmHYhfGxBNWmd&confirm=t&uuid=3866f09d-9a59-46f1-a71a-1270d4eeb6fe\n", + "From (original): https://drive.google.com/uc?id=1llJ4NGNTjY187RLX4MtlmHYhfGxBNWmd\n", + "From (redirected): https://drive.google.com/uc?id=1llJ4NGNTjY187RLX4MtlmHYhfGxBNWmd&confirm=t&uuid=63a55326-90b2-463a-9bdc-a115a7336f1c\n", "To: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/ai_multi_model_bundle_data.zip\n", - "100%|████████████████████████████████████████| 647M/647M [00:08<00:00, 73.4MB/s]\n", + "100%|█████████████████████████████████████████| 647M/647M [00:06<00:00, 105MB/s]\n", "Archive: ai_multi_model_bundle_data.zip\n", " inflating: dcm/1-001.dcm \n", " inflating: dcm/1-002.dcm \n", @@ -397,9 +399,7 @@ " inflating: dcm/1-202.dcm \n", " inflating: dcm/1-203.dcm \n", " inflating: dcm/1-204.dcm \n", - " creating: multi_models/pancreas_ct_dints/\n", " inflating: multi_models/pancreas_ct_dints/model.ts \n", - " creating: multi_models/spleen_ct/\n", " inflating: multi_models/spleen_ct/model.ts \n" ] } @@ -747,290 +747,59 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-08-30 01:28:16,680] [INFO] (root) - Parsed args: Namespace(argv=[], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-08-30 01:28:16,697] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=multi_models, workdir=)\n", - "[2023-08-30 01:28:16,702] [INFO] (root) - End compose\n", - "[info] [gxf_executor.cpp:210] Creating context\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 15:07:47,589] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])\n", + "[2024-04-10 15:07:47,599] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=multi_models, workdir=)\n", + "[2024-04-10 15:07:47,604] [INFO] (root) - End compose\n", + "[info] [gxf_executor.cpp:211] Creating context\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[info] [greedy_scheduler.cpp:190] Scheduling 9 entities\n", - "[2023-08-30 01:28:16,783] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", - "[2023-08-30 01:28:17,108] [INFO] (root) - Finding series for Selection named: CT Series\n", - "[2023-08-30 01:28:17,109] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 15:07:47,665] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 15:07:48,220] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 15:07:48,221] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", " # of series: 1\n", - "[2023-08-30 01:28:17,110] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:28:17,111] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:28:17,111] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", - "[2023-08-30 01:28:17,112] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:28:17,112] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", - "[2023-08-30 01:28:17,113] [INFO] (root) - Series attribute Modality value: CT\n", - "[2023-08-30 01:28:17,114] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:28:17,114] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:28:17,115] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", - "[2023-08-30 01:28:17,116] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:28:17,116] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:28:17,327] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/pancreas_ct_dints/model.ts\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary SaveImaged.__init__:resample: Current default value of argument `resample=True` has been deprecated since version 1.1. It will be changed to `resample=False` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "[2023-08-30 01:29:46,021] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/spleen_ct/model.ts\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "[2024-04-10 15:07:48,222] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 15:07:48,223] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2024-04-10 15:07:48,224] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 15:07:48,227] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:07:48,228] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 15:07:48,228] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 15:07:48,229] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:07:48,230] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 15:07:48,231] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 15:07:48,232] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:07:48,233] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 15:07:48,452] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/pancreas_ct_dints/model.ts\n", + "[2024-04-10 15:09:28,757] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/spleen_ct/model.ts\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", " warnings.warn(\n", - "[2023-08-30 01:29:52,325] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - " warnings.warn(msg)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - " warnings.warn(msg)\n", - "[2023-08-30 01:29:52,328] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 01:29:52,329] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 01:29:52,331] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 01:29:52,333] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 01:29:52,336] [INFO] (highdicom.seg.sop) - 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"[2023-08-30 01:29:52,462] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 01:29:52,464] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 01:29:52,466] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 01:29:52,469] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 01:29:52,471] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 01:29:52,473] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 01:29:52,475] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 01:29:52,515] [INFO] (highdicom.seg.sop) - skip empty plane 0 of segment #2\n", - "[2023-08-30 01:29:52,516] [INFO] (highdicom.seg.sop) - skip empty plane 1 of segment #2\n", - "[2023-08-30 01:29:52,517] [INFO] (highdicom.seg.sop) - skip empty plane 2 of segment #2\n", - "[2023-08-30 01:29:52,518] [INFO] (highdicom.seg.sop) - skip empty plane 3 of segment #2\n", - "[2023-08-30 01:29:52,519] [INFO] (highdicom.seg.sop) - skip empty plane 4 of segment #2\n", - "[2023-08-30 01:29:52,520] [INFO] (highdicom.seg.sop) - skip empty plane 5 of segment #2\n", - "[2023-08-30 01:29:52,521] [INFO] (highdicom.seg.sop) - skip empty plane 6 of segment #2\n", - "[2023-08-30 01:29:52,521] [INFO] (highdicom.seg.sop) - skip empty plane 7 of segment #2\n", - "[2023-08-30 01:29:52,522] [INFO] (highdicom.seg.sop) - skip empty plane 8 of segment #2\n", - "[2023-08-30 01:29:52,523] [INFO] (highdicom.seg.sop) - skip empty plane 9 of segment #2\n", - "[2023-08-30 01:29:52,524] [INFO] (highdicom.seg.sop) - skip empty plane 10 of segment #2\n", - "[2023-08-30 01:29:52,525] [INFO] (highdicom.seg.sop) - skip empty plane 11 of segment #2\n", - "[2023-08-30 01:29:52,526] [INFO] (highdicom.seg.sop) - skip empty plane 12 of segment #2\n", - "[2023-08-30 01:29:52,526] [INFO] (highdicom.seg.sop) - skip empty plane 13 of segment #2\n", - "[2023-08-30 01:29:52,527] [INFO] (highdicom.seg.sop) - skip empty plane 14 of segment #2\n", - "[2023-08-30 01:29:52,528] [INFO] (highdicom.seg.sop) - skip empty plane 15 of segment #2\n", - "[2023-08-30 01:29:52,529] [INFO] (highdicom.seg.sop) - skip empty plane 16 of segment #2\n", - "[2023-08-30 01:29:52,532] [INFO] (highdicom.seg.sop) - skip empty plane 17 of segment #2\n", - "[2023-08-30 01:29:52,532] [INFO] (highdicom.seg.sop) - skip empty plane 18 of segment #2\n", - "[2023-08-30 01:29:52,533] [INFO] (highdicom.seg.sop) - skip empty plane 19 of segment #2\n", - "[2023-08-30 01:29:52,534] [INFO] (highdicom.seg.sop) - skip empty plane 20 of segment #2\n", - "[2023-08-30 01:29:52,534] [INFO] (highdicom.seg.sop) - skip empty plane 21 of segment #2\n", - "[2023-08-30 01:29:52,536] [INFO] (highdicom.seg.sop) - skip empty plane 22 of segment #2\n", - "[2023-08-30 01:29:52,537] [INFO] (highdicom.seg.sop) - skip empty plane 23 of segment #2\n", - "[2023-08-30 01:29:52,538] [INFO] (highdicom.seg.sop) - skip empty plane 24 of segment #2\n", - "[2023-08-30 01:29:52,539] [INFO] (highdicom.seg.sop) - skip empty plane 25 of segment #2\n", - "[2023-08-30 01:29:52,540] [INFO] (highdicom.seg.sop) - skip empty plane 26 of segment #2\n", - "[2023-08-30 01:29:52,541] [INFO] (highdicom.seg.sop) - skip empty plane 27 of segment #2\n", - "[2023-08-30 01:29:52,542] [INFO] (highdicom.seg.sop) - skip empty plane 28 of segment #2\n", - "[2023-08-30 01:29:52,543] [INFO] (highdicom.seg.sop) - skip empty plane 29 of segment #2\n", - "[2023-08-30 01:29:52,544] [INFO] (highdicom.seg.sop) - skip empty plane 30 of segment #2\n", - "[2023-08-30 01:29:52,545] [INFO] (highdicom.seg.sop) - skip empty plane 31 of segment #2\n", - "[2023-08-30 01:29:52,546] [INFO] (highdicom.seg.sop) - skip empty plane 32 of segment #2\n", - "[2023-08-30 01:29:52,547] [INFO] (highdicom.seg.sop) - skip empty plane 33 of segment #2\n", - "[2023-08-30 01:29:52,549] [INFO] (highdicom.seg.sop) - skip empty plane 34 of segment #2\n", - "[2023-08-30 01:29:52,550] [INFO] (highdicom.seg.sop) - skip empty plane 35 of segment #2\n", - "[2023-08-30 01:29:52,551] [INFO] (highdicom.seg.sop) - skip empty plane 36 of segment #2\n", - "[2023-08-30 01:29:52,552] [INFO] (highdicom.seg.sop) - skip empty plane 37 of segment #2\n", - "[2023-08-30 01:29:52,553] [INFO] (highdicom.seg.sop) - skip empty plane 38 of segment #2\n", - "[2023-08-30 01:29:52,554] [INFO] (highdicom.seg.sop) - skip empty plane 39 of segment #2\n", - "[2023-08-30 01:29:52,555] [INFO] (highdicom.seg.sop) - skip empty plane 40 of segment #2\n", - "[2023-08-30 01:29:52,556] [INFO] (highdicom.seg.sop) - skip empty plane 41 of segment #2\n", - "[2023-08-30 01:29:52,557] [INFO] (highdicom.seg.sop) - skip empty plane 42 of segment #2\n", - "[2023-08-30 01:29:52,558] [INFO] (highdicom.seg.sop) - skip empty plane 43 of segment #2\n", - "[2023-08-30 01:29:52,559] [INFO] (highdicom.seg.sop) - skip empty plane 44 of segment #2\n", - "[2023-08-30 01:29:52,560] [INFO] (highdicom.seg.sop) - skip empty plane 45 of segment #2\n", - "[2023-08-30 01:29:52,561] [INFO] (highdicom.seg.sop) - skip empty plane 46 of segment #2\n", - "[2023-08-30 01:29:52,562] [INFO] (highdicom.seg.sop) - skip empty plane 47 of segment #2\n", - "[2023-08-30 01:29:52,563] [INFO] (highdicom.seg.sop) - skip empty plane 48 of segment #2\n", - "[2023-08-30 01:29:52,564] [INFO] (highdicom.seg.sop) - skip empty plane 49 of segment #2\n", - "[2023-08-30 01:29:52,565] [INFO] (highdicom.seg.sop) - skip empty plane 50 of segment #2\n", - "[2023-08-30 01:29:52,566] [INFO] (highdicom.seg.sop) - skip empty plane 51 of segment #2\n", - "[2023-08-30 01:29:52,567] [INFO] (highdicom.seg.sop) - skip empty plane 52 of segment #2\n", - "[2023-08-30 01:29:52,568] [INFO] (highdicom.seg.sop) - skip empty plane 53 of segment #2\n", - "[2023-08-30 01:29:52,569] [INFO] (highdicom.seg.sop) - skip empty plane 54 of segment #2\n", - "[2023-08-30 01:29:52,570] [INFO] (highdicom.seg.sop) - skip empty plane 55 of segment #2\n", - "[2023-08-30 01:29:52,572] [INFO] (highdicom.seg.sop) - skip empty plane 56 of segment #2\n", - "[2023-08-30 01:29:52,573] [INFO] (highdicom.seg.sop) - skip empty plane 57 of segment #2\n", - "[2023-08-30 01:29:52,574] [INFO] (highdicom.seg.sop) - skip empty plane 58 of segment #2\n", - "[2023-08-30 01:29:52,575] [INFO] (highdicom.seg.sop) - skip empty plane 59 of segment #2\n", - "[2023-08-30 01:29:52,576] [INFO] (highdicom.seg.sop) - skip empty plane 60 of segment #2\n", - "[2023-08-30 01:29:52,577] [INFO] (highdicom.seg.sop) - skip empty plane 61 of segment #2\n", - "[2023-08-30 01:29:52,578] [INFO] (highdicom.seg.sop) - skip empty plane 62 of segment #2\n", - "[2023-08-30 01:29:52,579] [INFO] (highdicom.seg.sop) - skip empty plane 63 of segment #2\n", - "[2023-08-30 01:29:52,580] [INFO] (highdicom.seg.sop) - skip empty plane 64 of segment #2\n", - "[2023-08-30 01:29:52,581] [INFO] (highdicom.seg.sop) - skip empty plane 65 of segment #2\n", - "[2023-08-30 01:29:52,582] [INFO] (highdicom.seg.sop) - skip empty plane 66 of segment #2\n", - "[2023-08-30 01:29:52,583] [INFO] (highdicom.seg.sop) - skip empty plane 67 of segment #2\n", - "[2023-08-30 01:29:52,620] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:29:52,621] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 01:29:52,623] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:29:52,623] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 01:29:52,624] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 01:29:52,625] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:29:52,626] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 01:29:52,627] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 01:29:52,627] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", - "[2023-08-30 01:29:54,671] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "[2023-08-30 01:29:54,673] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 01:29:54,675] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 01:29:54,676] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 01:29:54,677] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 01:29:54,678] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "[2023-08-30 01:29:54,680] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "[2023-08-30 01:29:54,681] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "[2023-08-30 01:29:54,682] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "[2023-08-30 01:29:54,684] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "[2023-08-30 01:29:54,686] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "[2023-08-30 01:29:54,689] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "[2023-08-30 01:29:54,692] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "[2023-08-30 01:29:54,694] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "[2023-08-30 01:29:54,697] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "[2023-08-30 01:29:54,700] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "[2023-08-30 01:29:54,702] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "[2023-08-30 01:29:54,705] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "[2023-08-30 01:29:54,707] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "[2023-08-30 01:29:54,710] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "[2023-08-30 01:29:54,712] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "[2023-08-30 01:29:54,714] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "[2023-08-30 01:29:54,716] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "[2023-08-30 01:29:54,718] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "[2023-08-30 01:29:54,720] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "[2023-08-30 01:29:54,722] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "[2023-08-30 01:29:54,724] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "[2023-08-30 01:29:54,726] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "[2023-08-30 01:29:54,729] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "[2023-08-30 01:29:54,730] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "[2023-08-30 01:29:54,732] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "[2023-08-30 01:29:54,734] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "[2023-08-30 01:29:54,736] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "[2023-08-30 01:29:54,739] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "[2023-08-30 01:29:54,742] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "[2023-08-30 01:29:54,745] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "[2023-08-30 01:29:54,747] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "[2023-08-30 01:29:54,750] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "[2023-08-30 01:29:54,752] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "[2023-08-30 01:29:54,755] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "[2023-08-30 01:29:54,757] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "[2023-08-30 01:29:54,760] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "[2023-08-30 01:29:54,762] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "[2023-08-30 01:29:54,764] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "[2023-08-30 01:29:54,767] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "[2023-08-30 01:29:54,769] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "[2023-08-30 01:29:54,771] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "[2023-08-30 01:29:54,773] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "[2023-08-30 01:29:54,775] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "[2023-08-30 01:29:54,777] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "[2023-08-30 01:29:54,779] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "[2023-08-30 01:29:54,781] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "[2023-08-30 01:29:54,783] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "[2023-08-30 01:29:54,785] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "[2023-08-30 01:29:54,788] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "[2023-08-30 01:29:54,790] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "[2023-08-30 01:29:54,793] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "[2023-08-30 01:29:54,796] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "[2023-08-30 01:29:54,799] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "[2023-08-30 01:29:54,801] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "[2023-08-30 01:29:54,804] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "[2023-08-30 01:29:54,806] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 01:29:54,809] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 01:29:54,811] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 01:29:54,813] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 01:29:54,817] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 01:29:54,819] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 01:29:54,822] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 01:29:54,824] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "[2023-08-30 01:29:54,826] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "[2023-08-30 01:29:54,828] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "[2023-08-30 01:29:54,830] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "[2023-08-30 01:29:54,832] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "[2023-08-30 01:29:54,834] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "[2023-08-30 01:29:54,837] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "[2023-08-30 01:29:54,839] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "[2023-08-30 01:29:54,842] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "[2023-08-30 01:29:54,844] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "[2023-08-30 01:29:54,846] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "[2023-08-30 01:29:54,848] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "[2023-08-30 01:29:54,850] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "[2023-08-30 01:29:54,852] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "[2023-08-30 01:29:54,854] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "[2023-08-30 01:29:54,856] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "[2023-08-30 01:29:54,858] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "[2023-08-30 01:29:54,860] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "[2023-08-30 01:29:54,863] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "[2023-08-30 01:29:54,922] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:29:54,923] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 01:29:54,924] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:29:54,925] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 01:29:54,926] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 01:29:54,926] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:29:54,927] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 01:29:54,928] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 01:29:54,928] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 15:09:34,244] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:09:34,246] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 15:09:34,247] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:09:34,248] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 15:09:34,249] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 15:09:34,250] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:09:34,251] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 15:09:34,252] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 15:09:34,253] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 15:09:35,375] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:09:35,377] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 15:09:35,377] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:09:35,378] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 15:09:35,380] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 15:09:35,381] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:09:35,382] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 15:09:35,383] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 15:09:35,384] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[2023-08-30 01:29:55,020] [INFO] (__main__.App) - End run\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n" + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[2024-04-10 15:09:35,488] [INFO] (__main__.App) - End run\n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n" ] } ], @@ -1402,290 +1171,59 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 01:30:01,466] [INFO] (root) - Parsed args: Namespace(argv=['my_app'], input=None, log_level=None, model=None, output=None, workdir=None)\n", - "[2023-08-30 01:30:01,471] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=multi_models, workdir=)\n", - "[2023-08-30 01:30:01,473] [INFO] (root) - End compose\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:210] Creating context\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1595] Loading extensions from configs...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1741] Activating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1771] Running Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1773] Waiting for completion...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[2024-04-10 15:09:40,274] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['my_app'])\n", + "[2024-04-10 15:09:40,282] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=multi_models, workdir=)\n", + "[2024-04-10 15:09:40,284] [INFO] (root) - End compose\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:211] Creating context\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1674] Loading extensions from configs...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1864] Activating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1894] Running Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1896] Waiting for completion...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:190] Scheduling 9 entities\n", - "[2023-08-30 01:30:01,532] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", - "[2023-08-30 01:30:02,120] [INFO] (root) - Finding series for Selection named: CT Series\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 15:09:40,321] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", " # of series: 1\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - Series attribute Modality value: CT\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", - "[2023-08-30 01:30:02,121] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "[2023-08-30 01:30:02,541] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/pancreas_ct_dints/model.ts\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary SaveImaged.__init__:resample: Current default value of argument `resample=True` has been deprecated since version 1.1. It will be changed to `resample=False` in version 1.3.\n", - " warn_deprecated(argname, msg, warning_category)\n", - "[2023-08-30 01:31:30,380] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/spleen_ct/model.ts\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 15:09:40,657] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 15:09:41,061] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/pancreas_ct_dints/model.ts\n", + "[2024-04-10 15:11:19,819] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/spleen_ct/model.ts\n", + "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", " warnings.warn(\n", - "[2023-08-30 01:31:37,123] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - " warnings.warn(msg)\n", - "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - " warnings.warn(msg)\n", - "[2023-08-30 01:31:37,125] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 01:31:37,125] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 01:31:37,126] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 01:31:37,127] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 01:31:37,127] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "[2023-08-30 01:31:37,128] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "[2023-08-30 01:31:37,129] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "[2023-08-30 01:31:37,129] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "[2023-08-30 01:31:37,130] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "[2023-08-30 01:31:37,131] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "[2023-08-30 01:31:37,132] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "[2023-08-30 01:31:37,133] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "[2023-08-30 01:31:37,133] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "[2023-08-30 01:31:37,134] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "[2023-08-30 01:31:37,134] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "[2023-08-30 01:31:37,135] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "[2023-08-30 01:31:37,136] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "[2023-08-30 01:31:37,136] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "[2023-08-30 01:31:37,137] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "[2023-08-30 01:31:37,137] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "[2023-08-30 01:31:37,138] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "[2023-08-30 01:31:37,139] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "[2023-08-30 01:31:37,139] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "[2023-08-30 01:31:37,140] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "[2023-08-30 01:31:37,140] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "[2023-08-30 01:31:37,141] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "[2023-08-30 01:31:37,141] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "[2023-08-30 01:31:37,142] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "[2023-08-30 01:31:37,143] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "[2023-08-30 01:31:37,143] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "[2023-08-30 01:31:37,144] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "[2023-08-30 01:31:37,144] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "[2023-08-30 01:31:37,145] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "[2023-08-30 01:31:37,146] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "[2023-08-30 01:31:37,146] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "[2023-08-30 01:31:37,147] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "[2023-08-30 01:31:37,147] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "[2023-08-30 01:31:37,148] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "[2023-08-30 01:31:37,149] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "[2023-08-30 01:31:37,149] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "[2023-08-30 01:31:37,150] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "[2023-08-30 01:31:37,150] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "[2023-08-30 01:31:37,151] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "[2023-08-30 01:31:37,152] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "[2023-08-30 01:31:37,152] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "[2023-08-30 01:31:37,153] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "[2023-08-30 01:31:37,153] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "[2023-08-30 01:31:37,154] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "[2023-08-30 01:31:37,154] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "[2023-08-30 01:31:37,155] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "[2023-08-30 01:31:37,156] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "[2023-08-30 01:31:37,156] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "[2023-08-30 01:31:37,157] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "[2023-08-30 01:31:37,157] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "[2023-08-30 01:31:37,158] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "[2023-08-30 01:31:37,159] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "[2023-08-30 01:31:37,159] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "[2023-08-30 01:31:37,160] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "[2023-08-30 01:31:37,160] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "[2023-08-30 01:31:37,161] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "[2023-08-30 01:31:37,162] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 01:31:37,162] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 01:31:37,163] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 01:31:37,163] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 01:31:37,164] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 01:31:37,165] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 01:31:37,165] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 01:31:37,194] [INFO] (highdicom.seg.sop) - skip empty plane 0 of segment #2\n", - "[2023-08-30 01:31:37,195] [INFO] (highdicom.seg.sop) - skip empty plane 1 of segment #2\n", - "[2023-08-30 01:31:37,195] [INFO] (highdicom.seg.sop) - skip empty plane 2 of segment #2\n", - "[2023-08-30 01:31:37,195] [INFO] (highdicom.seg.sop) - skip empty plane 3 of segment #2\n", - "[2023-08-30 01:31:37,195] [INFO] (highdicom.seg.sop) - skip empty plane 4 of segment #2\n", - "[2023-08-30 01:31:37,195] [INFO] (highdicom.seg.sop) - skip empty plane 5 of segment #2\n", - "[2023-08-30 01:31:37,196] [INFO] (highdicom.seg.sop) - skip empty plane 6 of segment #2\n", - "[2023-08-30 01:31:37,196] [INFO] (highdicom.seg.sop) - skip empty plane 7 of segment #2\n", - "[2023-08-30 01:31:37,196] [INFO] (highdicom.seg.sop) - skip empty plane 8 of segment #2\n", - "[2023-08-30 01:31:37,196] [INFO] (highdicom.seg.sop) - skip empty plane 9 of segment #2\n", - "[2023-08-30 01:31:37,196] [INFO] (highdicom.seg.sop) - skip empty plane 10 of segment #2\n", - "[2023-08-30 01:31:37,196] [INFO] (highdicom.seg.sop) - skip empty plane 11 of segment #2\n", - "[2023-08-30 01:31:37,197] [INFO] (highdicom.seg.sop) - skip empty plane 12 of segment #2\n", - "[2023-08-30 01:31:37,197] [INFO] (highdicom.seg.sop) - skip empty plane 13 of segment #2\n", - "[2023-08-30 01:31:37,197] [INFO] (highdicom.seg.sop) - skip empty plane 14 of segment #2\n", - "[2023-08-30 01:31:37,197] [INFO] (highdicom.seg.sop) - skip empty plane 15 of segment #2\n", - "[2023-08-30 01:31:37,197] [INFO] (highdicom.seg.sop) - skip empty plane 16 of segment #2\n", - "[2023-08-30 01:31:37,197] [INFO] (highdicom.seg.sop) - skip empty plane 17 of segment #2\n", - "[2023-08-30 01:31:37,197] [INFO] (highdicom.seg.sop) - skip empty plane 18 of segment #2\n", - "[2023-08-30 01:31:37,198] [INFO] (highdicom.seg.sop) - skip empty plane 19 of segment #2\n", - "[2023-08-30 01:31:37,198] [INFO] (highdicom.seg.sop) - skip empty plane 20 of segment #2\n", - "[2023-08-30 01:31:37,198] [INFO] (highdicom.seg.sop) - skip empty plane 21 of segment #2\n", - "[2023-08-30 01:31:37,198] [INFO] (highdicom.seg.sop) - skip empty plane 22 of segment #2\n", - "[2023-08-30 01:31:37,198] [INFO] (highdicom.seg.sop) - skip empty plane 23 of segment #2\n", - "[2023-08-30 01:31:37,198] [INFO] (highdicom.seg.sop) - skip empty plane 24 of segment #2\n", - "[2023-08-30 01:31:37,199] [INFO] (highdicom.seg.sop) - skip empty plane 25 of segment #2\n", - "[2023-08-30 01:31:37,199] [INFO] (highdicom.seg.sop) - skip empty plane 26 of segment #2\n", - "[2023-08-30 01:31:37,199] [INFO] (highdicom.seg.sop) - skip empty plane 27 of segment #2\n", - "[2023-08-30 01:31:37,199] [INFO] (highdicom.seg.sop) - skip empty plane 28 of segment #2\n", - "[2023-08-30 01:31:37,199] [INFO] (highdicom.seg.sop) - skip empty plane 29 of segment #2\n", - "[2023-08-30 01:31:37,199] [INFO] (highdicom.seg.sop) - skip empty plane 30 of segment #2\n", - "[2023-08-30 01:31:37,199] [INFO] (highdicom.seg.sop) - skip empty plane 31 of segment #2\n", - "[2023-08-30 01:31:37,200] [INFO] (highdicom.seg.sop) - skip empty plane 32 of segment #2\n", - "[2023-08-30 01:31:37,200] [INFO] (highdicom.seg.sop) - skip empty plane 33 of segment #2\n", - "[2023-08-30 01:31:37,200] [INFO] (highdicom.seg.sop) - skip empty plane 34 of segment #2\n", - "[2023-08-30 01:31:37,200] [INFO] (highdicom.seg.sop) - skip empty plane 35 of segment #2\n", - "[2023-08-30 01:31:37,200] [INFO] (highdicom.seg.sop) - skip empty plane 36 of segment #2\n", - "[2023-08-30 01:31:37,200] [INFO] (highdicom.seg.sop) - skip empty plane 37 of segment #2\n", - "[2023-08-30 01:31:37,201] [INFO] (highdicom.seg.sop) - skip empty plane 38 of segment #2\n", - "[2023-08-30 01:31:37,201] [INFO] (highdicom.seg.sop) - skip empty plane 39 of segment #2\n", - "[2023-08-30 01:31:37,201] [INFO] (highdicom.seg.sop) - skip empty plane 40 of segment #2\n", - "[2023-08-30 01:31:37,201] [INFO] (highdicom.seg.sop) - skip empty plane 41 of segment #2\n", - "[2023-08-30 01:31:37,201] [INFO] (highdicom.seg.sop) - skip empty plane 42 of segment #2\n", - "[2023-08-30 01:31:37,201] [INFO] (highdicom.seg.sop) - skip empty plane 43 of segment #2\n", - "[2023-08-30 01:31:37,201] [INFO] (highdicom.seg.sop) - skip empty plane 44 of segment #2\n", - "[2023-08-30 01:31:37,202] [INFO] (highdicom.seg.sop) - skip empty plane 45 of segment #2\n", - "[2023-08-30 01:31:37,202] [INFO] (highdicom.seg.sop) - skip empty plane 46 of segment #2\n", - "[2023-08-30 01:31:37,202] [INFO] (highdicom.seg.sop) - skip empty plane 47 of segment #2\n", - "[2023-08-30 01:31:37,202] [INFO] (highdicom.seg.sop) - skip empty plane 48 of segment #2\n", - "[2023-08-30 01:31:37,202] [INFO] (highdicom.seg.sop) - skip empty plane 49 of segment #2\n", - "[2023-08-30 01:31:37,202] [INFO] (highdicom.seg.sop) - skip empty plane 50 of segment #2\n", - "[2023-08-30 01:31:37,203] [INFO] (highdicom.seg.sop) - skip empty plane 51 of segment #2\n", - "[2023-08-30 01:31:37,203] [INFO] (highdicom.seg.sop) - skip empty plane 52 of segment #2\n", - "[2023-08-30 01:31:37,203] [INFO] (highdicom.seg.sop) - skip empty plane 53 of segment #2\n", - "[2023-08-30 01:31:37,203] [INFO] (highdicom.seg.sop) - skip empty plane 54 of segment #2\n", - "[2023-08-30 01:31:37,203] [INFO] (highdicom.seg.sop) - skip empty plane 55 of segment #2\n", - "[2023-08-30 01:31:37,203] [INFO] (highdicom.seg.sop) - skip empty plane 56 of segment #2\n", - "[2023-08-30 01:31:37,203] [INFO] (highdicom.seg.sop) - skip empty plane 57 of segment #2\n", - "[2023-08-30 01:31:37,204] [INFO] (highdicom.seg.sop) - skip empty plane 58 of segment #2\n", - "[2023-08-30 01:31:37,204] [INFO] (highdicom.seg.sop) - skip empty plane 59 of segment #2\n", - "[2023-08-30 01:31:37,204] [INFO] (highdicom.seg.sop) - skip empty plane 60 of segment #2\n", - "[2023-08-30 01:31:37,204] [INFO] (highdicom.seg.sop) - skip empty plane 61 of segment #2\n", - "[2023-08-30 01:31:37,204] [INFO] (highdicom.seg.sop) - skip empty plane 62 of segment #2\n", - "[2023-08-30 01:31:37,204] [INFO] (highdicom.seg.sop) - skip empty plane 63 of segment #2\n", - "[2023-08-30 01:31:37,205] [INFO] (highdicom.seg.sop) - skip empty plane 64 of segment #2\n", - "[2023-08-30 01:31:37,205] [INFO] (highdicom.seg.sop) - skip empty plane 65 of segment #2\n", - "[2023-08-30 01:31:37,205] [INFO] (highdicom.seg.sop) - skip empty plane 66 of segment #2\n", - "[2023-08-30 01:31:37,205] [INFO] (highdicom.seg.sop) - skip empty plane 67 of segment #2\n", - "[2023-08-30 01:31:37,228] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:31:37,228] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 01:31:37,228] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:31:37,228] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 01:31:37,228] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 01:31:37,228] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:31:37,228] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 01:31:37,229] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 01:31:37,229] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", - "[2023-08-30 01:31:39,286] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "[2023-08-30 01:31:39,286] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "[2023-08-30 01:31:39,287] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "[2023-08-30 01:31:39,288] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "[2023-08-30 01:31:39,288] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "[2023-08-30 01:31:39,289] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "[2023-08-30 01:31:39,289] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "[2023-08-30 01:31:39,290] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "[2023-08-30 01:31:39,291] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "[2023-08-30 01:31:39,291] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "[2023-08-30 01:31:39,292] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "[2023-08-30 01:31:39,292] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "[2023-08-30 01:31:39,293] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "[2023-08-30 01:31:39,293] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "[2023-08-30 01:31:39,294] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "[2023-08-30 01:31:39,294] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "[2023-08-30 01:31:39,295] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "[2023-08-30 01:31:39,296] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "[2023-08-30 01:31:39,296] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "[2023-08-30 01:31:39,297] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "[2023-08-30 01:31:39,297] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "[2023-08-30 01:31:39,298] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "[2023-08-30 01:31:39,299] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "[2023-08-30 01:31:39,300] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "[2023-08-30 01:31:39,300] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "[2023-08-30 01:31:39,301] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "[2023-08-30 01:31:39,301] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "[2023-08-30 01:31:39,302] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "[2023-08-30 01:31:39,302] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "[2023-08-30 01:31:39,303] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "[2023-08-30 01:31:39,303] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "[2023-08-30 01:31:39,304] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "[2023-08-30 01:31:39,304] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "[2023-08-30 01:31:39,305] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "[2023-08-30 01:31:39,306] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "[2023-08-30 01:31:39,306] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "[2023-08-30 01:31:39,307] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "[2023-08-30 01:31:39,307] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "[2023-08-30 01:31:39,308] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "[2023-08-30 01:31:39,308] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "[2023-08-30 01:31:39,309] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "[2023-08-30 01:31:39,309] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "[2023-08-30 01:31:39,310] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "[2023-08-30 01:31:39,311] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "[2023-08-30 01:31:39,311] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "[2023-08-30 01:31:39,312] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "[2023-08-30 01:31:39,312] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "[2023-08-30 01:31:39,313] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "[2023-08-30 01:31:39,313] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "[2023-08-30 01:31:39,314] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "[2023-08-30 01:31:39,315] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "[2023-08-30 01:31:39,315] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "[2023-08-30 01:31:39,316] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "[2023-08-30 01:31:39,316] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "[2023-08-30 01:31:39,317] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "[2023-08-30 01:31:39,317] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "[2023-08-30 01:31:39,318] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "[2023-08-30 01:31:39,318] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "[2023-08-30 01:31:39,319] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "[2023-08-30 01:31:39,320] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "[2023-08-30 01:31:39,320] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "[2023-08-30 01:31:39,321] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "[2023-08-30 01:31:39,321] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "[2023-08-30 01:31:39,322] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "[2023-08-30 01:31:39,322] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "[2023-08-30 01:31:39,323] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "[2023-08-30 01:31:39,323] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "[2023-08-30 01:31:39,324] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "[2023-08-30 01:31:39,325] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "[2023-08-30 01:31:39,325] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "[2023-08-30 01:31:39,326] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "[2023-08-30 01:31:39,326] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "[2023-08-30 01:31:39,327] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "[2023-08-30 01:31:39,327] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "[2023-08-30 01:31:39,328] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "[2023-08-30 01:31:39,329] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "[2023-08-30 01:31:39,329] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "[2023-08-30 01:31:39,330] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "[2023-08-30 01:31:39,330] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "[2023-08-30 01:31:39,331] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "[2023-08-30 01:31:39,331] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "[2023-08-30 01:31:39,332] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "[2023-08-30 01:31:39,332] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "[2023-08-30 01:31:39,333] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "[2023-08-30 01:31:39,334] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "[2023-08-30 01:31:39,334] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "[2023-08-30 01:31:39,335] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "[2023-08-30 01:31:39,375] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:31:39,375] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "[2023-08-30 01:31:39,375] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:31:39,375] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "[2023-08-30 01:31:39,375] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "[2023-08-30 01:31:39,375] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "[2023-08-30 01:31:39,375] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "[2023-08-30 01:31:39,375] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "[2023-08-30 01:31:39,375] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 15:11:25,386] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:11:25,386] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 15:11:25,386] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:11:25,386] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 15:11:25,386] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 15:11:25,386] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:11:25,386] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 15:11:25,387] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 15:11:25,387] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 15:11:26,671] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:11:26,671] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", + "[2024-04-10 15:11:26,671] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:11:26,671] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", + "[2024-04-10 15:11:26,671] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", + "[2024-04-10 15:11:26,671] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", + "[2024-04-10 15:11:26,672] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", + "[2024-04-10 15:11:26,672] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", + "[2024-04-10 15:11:26,672] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:398] Scheduler finished.\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1784] Deactivating Graph...\n", - "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", - "[2023-08-30 01:31:39,463] [INFO] (app.App) - End run\n" + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1907] Deactivating Graph...\n", + "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", + "[2024-04-10 15:11:26,772] [INFO] (app.App) - End run\n" ] } ], @@ -1703,8 +1241,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.2.826.0.1.3680043.10.511.3.12607789968000921484298196664438779.dcm\n", - "1.2.826.0.1.3680043.10.511.3.13304883178132083951047262313172068.dcm\n" + "1.2.826.0.1.3680043.10.511.3.15046048574363662193759299900270199.dcm\n", + "1.2.826.0.1.3680043.10.511.3.98214024482046978965335342071467925.dcm\n" ] } ], @@ -1804,15 +1342,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 01:31:42,861] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app\n", - "[2023-08-30 01:31:42,861] [INFO] (packager.parameters) - Detected application type: Python Module\n", - "[2023-08-30 01:31:42,861] [INFO] (packager) - Scanning for models in {models_path}...\n", - "[2023-08-30 01:31:42,861] [DEBUG] (packager) - Model spleen_ct=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/spleen_ct added.\n", - "[2023-08-30 01:31:42,861] [DEBUG] (packager) - Model pancreas_ct_dints=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/pancreas_ct_dints added.\n", - "[2023-08-30 01:31:42,861] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...\n", - "[2023-08-30 01:31:42,862] [INFO] (packager) - Generating app.json...\n", - "[2023-08-30 01:31:42,863] [INFO] (packager) - Generating pkg.json...\n", - "[2023-08-30 01:31:42,863] [DEBUG] (common) - \n", + "[2024-04-10 15:11:29,127] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app\n", + "[2024-04-10 15:11:29,127] [INFO] (packager.parameters) - Detected application type: Python Module\n", + "[2024-04-10 15:11:29,127] [INFO] (packager) - Scanning for models in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models...\n", + "[2024-04-10 15:11:29,127] [DEBUG] (packager) - Model spleen_ct=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/spleen_ct added.\n", + "[2024-04-10 15:11:29,127] [DEBUG] (packager) - Model pancreas_ct_dints=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/pancreas_ct_dints added.\n", + "[2024-04-10 15:11:29,127] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...\n", + "[2024-04-10 15:11:29,128] [INFO] (packager) - Generating app.json...\n", + "[2024-04-10 15:11:29,129] [INFO] (packager) - Generating pkg.json...\n", + "[2024-04-10 15:11:29,133] [DEBUG] (common) - \n", "=============== Begin app.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", @@ -1840,14 +1378,14 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1.0,\n", " \"workingDirectory\": \"/var/holoscan\"\n", "}\n", "================ End app.json ================\n", " \n", - "[2023-08-30 01:31:42,863] [DEBUG] (common) - \n", + "[2024-04-10 15:11:29,134] [DEBUG] (common) - \n", "=============== Begin pkg.json ===============\n", "{\n", " \"apiVersion\": \"1.0.0\",\n", @@ -1863,15 +1401,16 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"10Gi\"\n", " },\n", - " \"version\": 1.0\n", + " \"version\": 1.0,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "================ End pkg.json ================\n", " \n", - "[2023-08-30 01:31:43,285] [DEBUG] (packager.builder) - \n", + "[2024-04-10 15:11:29,632] [DEBUG] (packager.builder) - \n", "========== Begin Dockerfile ==========\n", "\n", "\n", - "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", + "FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", "\n", "ENV DEBIAN_FRONTEND=noninteractive\n", "ENV TERM=xterm-256color\n", @@ -1887,11 +1426,13 @@ " && mkdir -p /var/holoscan/input \\\n", " && mkdir -p /var/holoscan/output\n", "\n", - "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\"\n", + "LABEL base=\"nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\"\n", "LABEL tag=\"my_app:1.0\"\n", "LABEL org.opencontainers.image.title=\"MONAI Deploy App Package - Multi Model App\"\n", "LABEL org.opencontainers.image.version=\"1.0\"\n", - "LABEL org.nvidia.holoscan=\"0.6.0\"\n", + "LABEL org.nvidia.holoscan=\"1.0.3\"\n", + "LABEL org.monai.deploy.app-sdk=\"0.5.1\"\n", + "\n", "\n", "ENV HOLOSCAN_ENABLE_HEALTH_CHECK=true\n", "ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input\n", @@ -1916,7 +1457,7 @@ "\n", "\n", "\n", - "RUN groupadd -g $GID $UNAME\n", + "RUN groupadd -f -g $GID $UNAME\n", "RUN useradd -rm -d /home/$UNAME -s /bin/bash -g $GID -G sudo -u $UID $UNAME\n", "RUN chown -R holoscan /var/holoscan \n", "RUN chown -R holoscan /var/holoscan/input \n", @@ -1941,13 +1482,12 @@ "RUN pip install --upgrade pip\n", "RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "\n", - "# Install Holoscan from PyPI org\n", - "RUN pip install holoscan==0.6.0\n", - "\n", + "# Install Holoscan from PyPI only when sdk_type is Holoscan. \n", + "# For MONAI Deploy, the APP SDK will install it unless user specifies the Holoscan SDK file.\n", "\n", "# Copy user-specified MONAI Deploy SDK file\n", - "COPY ./monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", - "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + "COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", + "RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", "\n", "\n", "\n", @@ -1963,211 +1503,223 @@ "ENTRYPOINT [\"/var/holoscan/tools\"]\n", "=========== End Dockerfile ===========\n", "\n", - "[2023-08-30 01:31:43,285] [INFO] (packager.builder) - \n", + "[2024-04-10 15:11:29,632] [INFO] (packager.builder) - \n", "===============================================================================\n", "Building image for: x64-workstation\n", " Architecture: linux/amd64\n", - " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - " Build Image: N/A \n", + " Base Image: nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + " Build Image: N/A\n", " Cache: Enabled\n", " Configuration: dgpu\n", - " Holoiscan SDK Package: pypi.org\n", - " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + " Holoscan SDK Package: pypi.org\n", + " MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", " gRPC Health Probe: N/A\n", - " SDK Version: 0.6.0\n", + " SDK Version: 1.0.3\n", " SDK: monai-deploy\n", " Tag: my_app-x64-workstation-dgpu-linux-amd64:1.0\n", " \n", - "[2023-08-30 01:31:43,573] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", - "[2023-08-30 01:31:43,574] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=my_app-x64-workstation-dgpu-linux-amd64:1.0\n", - "#1 [internal] load .dockerignore\n", - "#1 transferring context:\n", - "#1 transferring context: 1.79kB 0.0s done\n", + "[2024-04-10 15:11:29,935] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n", + "[2024-04-10 15:11:29,936] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=my_app-x64-workstation-dgpu-linux-amd64:1.0\n", + "#0 building with \"holoscan_app_builder\" instance using docker-container driver\n", + "\n", + "#1 [internal] load build definition from Dockerfile\n", + "#1 transferring dockerfile: 2.79kB done\n", "#1 DONE 0.1s\n", "\n", - "#2 [internal] load build definition from Dockerfile\n", - "#2 transferring dockerfile: 2.66kB done\n", + "#2 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", "#2 DONE 0.1s\n", "\n", - "#3 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#3 DONE 0.7s\n", + "#3 [internal] load .dockerignore\n", + "#3 transferring context: 1.79kB done\n", + "#3 DONE 0.1s\n", "\n", "#4 [internal] load build context\n", "#4 DONE 0.0s\n", "\n", - "#5 importing cache manifest from local:5671991582744023691\n", + "#5 importing cache manifest from local:3229340695933661696\n", + "#5 inferred cache manifest type: application/vnd.oci.image.index.v1+json done\n", "#5 DONE 0.0s\n", "\n", - "#6 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu\n", - "#6 DONE 0.7s\n", + "#6 [ 1/21] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155\n", + "#6 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu@sha256:50343c616bf910e2a7651abb59db7833933e82cce64c3c4885f938d7e4af6155 0.0s done\n", + "#6 DONE 0.1s\n", "\n", - "#7 [ 1/22] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc\n", - "#7 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc\n", - "#7 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v0.6.0-dgpu@sha256:9653f80f241fd542f25afbcbcf7a0d02ed7e5941c79763e69def5b1e6d9fb7bc 0.1s done\n", - "#7 DONE 0.1s\n", + "#7 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v1.0.3-dgpu\n", + "#7 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done\n", + "#7 DONE 0.6s\n", "\n", "#4 [internal] load build context\n", - "#4 transferring context: 585.41MB 3.4s\n", - "#4 transferring context: 636.06MB 3.7s done\n", - "#4 DONE 3.8s\n", + "#4 transferring context: 636.05MB 3.2s done\n", + "#4 DONE 3.2s\n", "\n", - "#8 [ 8/22] RUN chown -R holoscan /var/holoscan/output\n", + "#8 [ 2/21] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", "#8 CACHED\n", "\n", - "#9 [ 2/22] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output\n", + "#9 [ 6/21] RUN chown -R holoscan /var/holoscan\n", "#9 CACHED\n", "\n", - "#10 [ 9/22] WORKDIR /var/holoscan\n", + "#10 [15/21] COPY ./monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", "#10 CACHED\n", "\n", - "#11 [13/22] RUN pip install --upgrade pip\n", + "#11 [10/21] COPY ./tools /var/holoscan/tools\n", "#11 CACHED\n", "\n", - "#12 [ 7/22] RUN chown -R holoscan /var/holoscan/input\n", + "#12 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt\n", "#12 CACHED\n", "\n", - "#13 [14/22] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", + "#13 [ 9/21] WORKDIR /var/holoscan\n", "#13 CACHED\n", "\n", - "#14 [ 4/22] RUN groupadd -g 1000 holoscan\n", + "#14 [13/21] RUN pip install --upgrade pip\n", "#14 CACHED\n", "\n", - "#15 [11/22] RUN chmod +x /var/holoscan/tools\n", + "#15 [ 3/21] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", "#15 CACHED\n", "\n", - "#16 [ 3/22] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*\n", + "#16 [ 4/21] RUN groupadd -f -g 1000 holoscan\n", "#16 CACHED\n", "\n", - "#17 [ 5/22] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", + "#17 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n", "#17 CACHED\n", "\n", - "#18 [ 6/22] RUN chown -R holoscan /var/holoscan\n", + "#18 [ 7/21] RUN chown -R holoscan /var/holoscan/input\n", "#18 CACHED\n", "\n", - "#19 [12/22] COPY ./pip/requirements.txt /tmp/requirements.txt\n", + "#19 [14/21] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n", "#19 CACHED\n", "\n", - "#20 [15/22] RUN pip install holoscan==0.6.0\n", + "#20 [11/21] RUN chmod +x /var/holoscan/tools\n", "#20 CACHED\n", "\n", - "#21 [10/22] COPY ./tools /var/holoscan/tools\n", + "#21 [ 8/21] RUN chown -R holoscan /var/holoscan/output\n", "#21 CACHED\n", "\n", - "#22 [16/22] COPY ./monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", + "#22 [16/21] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+25.g31e4165.dirty-py3-none-any.whl\n", "#22 CACHED\n", "\n", - "#23 [17/22] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+22.g029f8bc.dirty-py3-none-any.whl\n", - "#23 CACHED\n", + "#23 [17/21] COPY ./models /opt/holoscan/models\n", + "#23 DONE 6.7s\n", "\n", - "#24 [18/22] COPY ./models /opt/holoscan/models\n", - "#24 DONE 3.5s\n", + "#24 [18/21] COPY ./map/app.json /etc/holoscan/app.json\n", + "#24 DONE 0.1s\n", "\n", - "#25 [19/22] COPY ./map/app.json /etc/holoscan/app.json\n", + "#25 [19/21] COPY ./app.config /var/holoscan/app.yaml\n", "#25 DONE 0.1s\n", "\n", - "#26 [20/22] COPY ./app.config /var/holoscan/app.yaml\n", + "#26 [20/21] COPY ./map/pkg.json /etc/holoscan/pkg.json\n", "#26 DONE 0.1s\n", "\n", - "#27 [21/22] COPY ./map/pkg.json /etc/holoscan/pkg.json\n", - "#27 DONE 0.1s\n", - "\n", - "#28 [22/22] COPY ./app /opt/holoscan/app\n", - "#28 DONE 0.1s\n", - "\n", - "#29 exporting to docker image format\n", - "#29 exporting layers\n", - "#29 exporting layers 17.6s done\n", - "#29 exporting manifest sha256:711ffdb84ecd0fb87425a0acbb896443eb5e0f1943d40b46402b2b08142ea130 0.0s done\n", - "#29 exporting config sha256:21983532749ea654bbd28d0091fa6a69e2e00340df6398d4fc008d2a50a70acb 0.0s done\n", - "#29 sending tarball\n", - "#29 ...\n", - "\n", - "#30 importing to docker\n", - "#30 DONE 8.4s\n", - "\n", - "#29 exporting to docker image format\n", - "#29 sending tarball 67.5s done\n", - "#29 DONE 85.1s\n", - "\n", - "#31 exporting content cache\n", - "#31 preparing build cache for export\n", - "#31 writing layer sha256:0709800848b4584780b40e7e81200689870e890c38b54e96b65cd0a3b1942f2d done\n", - "#31 writing layer sha256:0ce020987cfa5cd1654085af3bb40779634eb3d792c4a4d6059036463ae0040d done\n", - "#31 writing layer sha256:0f65089b284381bf795d15b1a186e2a8739ea957106fa526edef0d738e7cda70 done\n", - "#31 writing layer sha256:124e6b96b81690cf8ea2488c3755d3b87bd3e28f8ce6d68cfa8d13fafd1adc7b 0.0s done\n", - "#31 writing layer sha256:12a47450a9f9cc5d4edab65d0f600dbbe8b23a1663b0b3bb2c481d40e074b580 done\n", - "#31 writing layer sha256:1de965777e2e37c7fabe00bdbf3d0203ca83ed30a71a5479c3113fe4fc48c4bb done\n", - "#31 writing layer sha256:24b5aa2448e920814dd67d7d3c0169b2cdacb13c4048d74ded3b4317843b13ff done\n", - "#31 writing layer sha256:2789e1f0e19719b047679b4b490cab1edb9e151cd286aed22df08022c249f040 done\n", - "#31 writing layer sha256:2d42104dbf0a7cc962b791f6ab4f45a803f8a36d296f996aca180cfb2f3e30d0 done\n", - "#31 writing layer sha256:2fa1ce4fa3fec6f9723380dc0536b7c361d874add0baaddc4bbf2accac82d2ff done\n", - "#31 writing layer sha256:38794be1b5dc99645feabf89b22cd34fb5bdffb5164ad920e7df94f353efe9c0 done\n", - "#31 writing layer sha256:38f963dc57c1e7b68a738fe39ed9f9345df7188111a047e2163a46648d7f1d88 done\n", - "#31 writing layer sha256:3e7e4c9bc2b136814c20c04feb4eea2b2ecf972e20182d88759931130cfb4181 done\n", - "#31 writing layer sha256:3fd77037ad585442cd82d64e337f49a38ddba50432b2a1e563a48401d25c79e6 done\n", - "#31 writing layer sha256:41814ed91034b30ac9c44dfc604a4bade6138005ccf682372c02e0bead66dbc0 done\n", - "#31 writing layer sha256:45893188359aca643d5918c9932da995364dc62013dfa40c075298b1baabece3 done\n", - "#31 writing layer sha256:49bc651b19d9e46715c15c41b7c0daa007e8e25f7d9518f04f0f06592799875a done\n", - "#31 writing layer sha256:4c12db5118d8a7d909e4926d69a2192d2b3cd8b110d49c7504a4f701258c1ccc done\n", - "#31 writing layer sha256:4cc43a803109d6e9d1fd35495cef9b1257035f5341a2db54f7a1940815b6cc65 done\n", - "#31 writing layer sha256:4d32b49e2995210e8937f0898327f196d3fcc52486f0be920e8b2d65f150a7ab done\n", - "#31 writing layer sha256:4d6fe980bad9cd7b2c85a478c8033cae3d098a81f7934322fb64658b0c8f9854 done\n", - "#31 writing layer sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 done\n", - "#31 writing layer sha256:50b2500ad4a5ad2f73d71f4dedecabff852c74ea78a97dab0fc86b2ed44ddc77 done\n", - "#31 writing layer sha256:5150182f1ff123399b300ca469e00f6c4d82e1b9b72652fb8ee7eab370245236 done\n", - "#31 writing layer sha256:595c38fa102c61c3dda19bdab70dcd26a0e50465b986d022a84fa69023a05d0f done\n", - "#31 writing layer sha256:59d451175f6950740e26d38c322da0ef67cb59da63181eb32996f752ba8a2f17 done\n", - "#31 writing layer sha256:5ad1f2004580e415b998124ea394e9d4072a35d70968118c779f307204d6bd17 done\n", - "#31 writing layer sha256:5e2c1cbc09286c26c04d5b4257b11940ecdb161330319d54feadc7ef9a8dc8f6 done\n", - "#31 writing layer sha256:5f9c46ad5b8701de87fd765a52f3202c1c36bf038dec8112e9df659111c94442 0.0s done\n", - "#31 writing layer sha256:62598eafddf023e7f22643485f4321cbd51ff7eee743b970db12454fd3c8c675 done\n", - "#31 writing layer sha256:63d7e616a46987136f4cc9eba95db6f6327b4854cfe3c7e20fed6db0c966e380 done\n", - "#31 writing layer sha256:6939d591a6b09b14a437e5cd2d6082a52b6d76bec4f72d960440f097721da34f\n", - "#31 writing layer sha256:6939d591a6b09b14a437e5cd2d6082a52b6d76bec4f72d960440f097721da34f done\n", - "#31 writing layer sha256:698318e5a60e5e0d48c45bf992f205a9532da567fdfe94bd59be2e192975dd6f done\n", - "#31 writing layer sha256:6ddc1d0f91833b36aac1c6f0c8cea005c87d94bab132d46cc06d9b060a81cca3 done\n", - "#31 writing layer sha256:6de966d13ad3e40ec7320152bba8dc4ffbbe04de44488c5e001560900cafeff8 0.0s 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sha256:eb411bef39c013c9853651e68f00965dbd826d829c4e478884a2886976e9c989 done\n", - "#31 writing layer sha256:edfe4a95eb6bd3142aeda941ab871ffcc8c19cf50c33561c210ba8ead2424759\n", - "#31 preparing build cache for export 12.8s done\n", - "#31 writing layer sha256:edfe4a95eb6bd3142aeda941ab871ffcc8c19cf50c33561c210ba8ead2424759 done\n", - "#31 writing layer sha256:ef4466d6f927d29d404df9c5af3ef5733c86fa14e008762c90110b963978b1e7 done\n", - "#31 writing layer sha256:f346e3ecdf0bee048fa1e3baf1d3128ff0283b903f03e97524944949bd8882e5 done\n", - "#31 writing layer sha256:f3f9a00a1ce9aadda250aacb3e66a932676badc5d8519c41517fdf7ea14c13ed done\n", - "#31 writing layer sha256:f7a50dafd51c2bcaad0ede31fbf29c38fe66776ade008a7fbdb07dba39de7f97 done\n", - "#31 writing layer sha256:fd849d9bd8889edd43ae38e9f21a912430c8526b2c18f3057a3b2cd74eb27b31 done\n", - "#31 writing config sha256:28c3423e13c001faf1998faab90290315a1801c71bb4889c6841352c9e2726f5 0.0s done\n", - "#31 writing manifest sha256:9435a6ae42ed4f35173de90fdae7292f942efcc10b547b7918aa8ee048295d40 0.0s done\n", - "#31 DONE 12.8s\n", - "[2023-08-30 01:33:32,218] [INFO] (packager) - Build Summary:\n", + "#27 [21/21] COPY ./app /opt/holoscan/app\n", + "#27 DONE 0.2s\n", + "\n", + "#28 exporting to docker image format\n", + "#28 exporting layers\n", + "#28 exporting layers 20.5s done\n", + "#28 exporting manifest sha256:e77308187dcf97394b99101aa895bea8c1e7dc92572bedddb310476c65f828ab 0.0s done\n", + "#28 exporting config sha256:f820210cdcfe0c41a2791dcced8072f2413aa1f6f2b1076134249d6abc400141 0.0s done\n", + "#28 sending tarball\n", + "#28 ...\n", + "\n", + "#29 importing to docker\n", + "#29 loading layer 0b7c827957f8 557.06kB / 584.49MB\n", + "#29 loading layer 0b7c827957f8 150.96MB / 584.49MB 2.1s\n", + "#29 loading layer 0b7c827957f8 305.82MB / 584.49MB 4.2s\n", + "#29 loading layer 0b7c827957f8 492.99MB / 584.49MB 6.3s\n", + "#29 loading layer 0f7d49f89e9c 492B / 492B\n", + "#29 loading layer 98aed7487fb0 312B / 312B\n", + "#29 loading layer 972f8aa128ec 322B / 322B\n", + "#29 loading layer db9d1b5a3f36 4.00kB / 4.00kB\n", + "#29 loading layer db9d1b5a3f36 4.00kB / 4.00kB 1.2s done\n", + "#29 loading layer 0b7c827957f8 492.99MB / 584.49MB 9.4s done\n", + "#29 loading layer 0f7d49f89e9c 492B / 492B 1.4s done\n", + "#29 loading layer 98aed7487fb0 312B / 312B 1.3s done\n", + "#29 loading layer 972f8aa128ec 322B / 322B 1.3s done\n", + "#29 DONE 9.4s\n", + "\n", + "#28 exporting to docker image format\n", + "#28 sending tarball 79.2s done\n", + "#28 DONE 99.9s\n", + "\n", + "#30 exporting cache to client directory\n", + "#30 preparing build cache for export\n", + "#30 writing layer sha256:00bb4c1319ba1a33ac3edcb3aa1240d8abcb8d0383c6267ed8028d3b6228a8a4\n", + "#30 writing layer sha256:00bb4c1319ba1a33ac3edcb3aa1240d8abcb8d0383c6267ed8028d3b6228a8a4 done\n", + "#30 writing layer sha256:014cff740c9ec6e9a30d0b859219a700ae880eb385d62095d348f5ea136d6015 done\n", + "#30 writing layer sha256:021b9bc7b766e946a42d4bf0d3f88658998d36cf2fa5f182af98d925b3d44f4f 0.0s done\n", + "#30 writing layer sha256:0a1756432df4a4350712d8ae5c003f1526bd2180800b3ae6301cfc9ccf370254 done\n", + "#30 writing layer sha256:0a77dcbd0e648ddc4f8e5230ade8fdb781d99e24fa4f13ca96a360c7f7e6751f done\n", + "#30 writing layer sha256:0bf3a16e4f3f9ec99796b99e331a5c62472bc9377925e1fdc05f64709ed09895 done\n", + "#30 writing layer sha256:0ec682bf99715a9f88631226f3749e2271b8b9f254528ef61f65ed829984821c done\n", + "#30 writing layer sha256:1133dfcee0e851b490d17b3567f50c4b25ba5750da02ba4b3f3630655d0b1a7b done\n", + "#30 writing layer sha256:1294b2835667d633f938174d9fecb18a60bbbebb6fb49788a1f939893a25d1af done\n", + "#30 writing layer sha256:16a03c6e0373b62f9713416da0229bb7ce2585183141081d3ea8427ad2e84408 done\n", + "#30 writing layer sha256:20d331454f5fb557f2692dfbdbe092c718fd2cb55d5db9d661b62228dacca5c2 done\n", + "#30 writing layer sha256:2232aeb26b5b7ea57227e9a5b84da4fb229624d7bc976a5f7ce86d9c8653d277 done\n", + "#30 writing layer sha256:238f69a43816e481f0295995fcf5fe74d59facf0f9f99734c8d0a2fb140630e0 done\n", + "#30 writing layer sha256:2ad84487f9d4d31cd1e0a92697a5447dd241935253d036b272ef16d31620c1e7 done\n", + "#30 writing layer sha256:2bb73464628bd4a136c4937f42d522c847bea86b2215ae734949e24c1caf450e done\n", + "#30 writing layer sha256:3a663fdf00962d807df49af4b54ad7382e7d0b0c65355a78d706ee221758e691\n", + "#30 writing layer sha256:3a663fdf00962d807df49af4b54ad7382e7d0b0c65355a78d706ee221758e691 11.2s done\n", + "#30 writing layer sha256:3e3e04011ebdba380ab129f0ee390626cb2a600623815ca756340c18bedb9517\n", + "#30 writing layer sha256:3e3e04011ebdba380ab129f0ee390626cb2a600623815ca756340c18bedb9517 done\n", + "#30 writing layer sha256:42619ce4a0c9e54cfd0ee41a8e5f27d58b3f51becabd1ac6de725fbe6c42b14a done\n", + "#30 writing layer sha256:43a21fb6c76bd2b3715cc09d9f8c3865dc61c51dd9e2327b429f5bec8fff85d1 done\n", + "#30 writing layer 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"#30 writing layer sha256:5ea668ffc2fc267d241dbf17ca283bc879643a189be4f7e3d9034a82fc64a1ea done\n", + "#30 writing layer sha256:62452179df7c18e292f141d4aec29e6aba9ff8270c893731169fc6f41dc07631 done\n", + "#30 writing layer sha256:6399aeba5e066098b3fac85e23e402cd10f0c5d0f06107595840b9f7259f9b40 0.0s done\n", + "#30 writing layer sha256:6630c387f5f2115bca2e646fd0c2f64e1f3d5431c2e050abe607633883eda230 done\n", + "#30 writing layer sha256:69af4b756272a77f683a8d118fd5ca55c03ad5f1bacc673b463f54d16b833da5 done\n", + "#30 writing layer sha256:6ae1f1fb92c0cb2b6e219f687b08c8e511501a7af696c943ca20d119eba7cd02 done\n", + "#30 writing layer sha256:6deb3d550b15a5e099c0b3d0cbc242e351722ca16c058d3a6c28ba1a02824d0f done\n", + "#30 writing layer sha256:6e80a527af94a864094c4f9116c2d29d3d7548ec8388579d9cf3f8a39a4b8178 done\n", + "#30 writing layer sha256:7386814d57100e2c7389fbf4e16f140f5c549d31434c62c3884a85a3ee5cd2a7 done\n", + "#30 writing layer 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"#30 writing layer sha256:a68f4e0ec09ec3b78cb4cf8e4511d658e34e7b6f676d7806ad9703194ff17604 done\n", + "#30 writing layer sha256:a8e4decc8f7289623b8fd7b9ba1ca555b5a755ebdbf81328d68209f148d9e602 done\n", + "#30 writing layer sha256:afde1c269453ce68a0f2b54c1ba8c5ecddeb18a19e5618a4acdef1f0fe3921af done\n", + "#30 writing layer sha256:b48a5fafcaba74eb5d7e7665601509e2889285b50a04b5b639a23f8adc818157 done\n", + "#30 writing layer sha256:b49326ff73acef905ddca0e7c2734fb9fa6d21d55c9b25feb30dd3a4aa99a9d9 0.0s done\n", + "#30 writing layer sha256:ba9f7c75e4dd7942b944679995365aab766d3677da2e69e1d74472f471a484dd done\n", + "#30 writing layer sha256:bc42865e1c27a9b1bee751f3c99ad2c12a906d32aca396ace7a07231c9cafbd1 done\n", + "#30 writing layer sha256:bdfc73b2a0fa11b4086677e117a2f9feb6b4ffeccb23a3d58a30543339607e31 done\n", + "#30 writing layer sha256:c175bb235295e50de2961fa1e1a2235c57e6eba723a914287dfc26d3be0eac11 done\n", + "#30 writing layer sha256:c98533d2908f36a5e9b52faae83809b3b6865b50e90e2817308acfc64cd3655f done\n", + "#30 writing layer sha256:cb6c95b33bc30dd285c5b3cf99a05281b8f12decae1c932ab64bd58f56354021 done\n", + "#30 writing layer sha256:d57848e1e8b61049c64df4a786ec67b44ae3ffc2554b13b92ea4ce57b8686ab9 0.0s done\n", + "#30 writing layer sha256:d6b5d6e098aacb316146a428c6b5aef9692011c6dce0932e3bbfbf27a514b7ed done\n", + "#30 writing layer sha256:d7da5c5e9a40c476c4b3188a845e3276dedfd752e015ea5113df5af64d4d43f7 done\n", + "#30 writing layer sha256:e4aedc686433c0ec5e676e6cc54a164345f7016aa0eb714f00c07e11664a1168\n", + "#30 preparing build cache for export 11.8s done\n", + "#30 writing layer sha256:e4aedc686433c0ec5e676e6cc54a164345f7016aa0eb714f00c07e11664a1168 done\n", + "#30 writing layer sha256:e8acb678f16bc0c369d5cf9c184f2d3a1c773986816526e5e3e9c0354f7e757f done\n", + "#30 writing layer sha256:e9225f7ab6606813ec9acba98a064826ebfd6713a9645a58cd068538af1ecddb done\n", + "#30 writing layer sha256:f33546e75bf1a7d9dc9e21b9a2c54c9d09b24790ad7a4192a8509002ceb14688 done\n", + "#30 writing layer sha256:f608e2fbff86e98627b7e462057e7d2416522096d73fe4664b82fe6ce8a4047d done\n", + "#30 writing layer sha256:f7702077ced42a1ee35e7f5e45f72634328ff3bcfe3f57735ba80baa5ec45daf done\n", + "#30 writing layer sha256:fa66a49172c6e821a1bace57c007c01da10cbc61507c44f8cdfeed8c4e5febab done\n", + "#30 writing config sha256:7d69b41abed153db28e437a954cc449f6c78804af5fd43d91d5b0b2b7d7e2b64 0.0s done\n", + "#30 writing cache manifest sha256:bf627b702e1d3cfae1b53eb29f8b0367eca39a3a4e756a966b4b5ee9054360fe 0.0s done\n", + "#30 DONE 11.8s\n", + "[2024-04-10 15:13:34,407] [INFO] (packager) - Build Summary:\n", "\n", "Platform: x64-workstation/dgpu\n", " Status: Succeeded\n", @@ -2198,7 +1750,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "my_app-x64-workstation-dgpu-linux-amd64 1.0 21983532749e About a minute ago 16GB\n" + "my_app-x64-workstation-dgpu-linux-amd64 1.0 f820210cdcfe About a minute ago 18.1GB\n" ] } ], @@ -2256,7 +1808,7 @@ " },\n", " \"readiness\": null,\n", " \"sdk\": \"monai-deploy\",\n", - " \"sdkVersion\": \"0.6.0\",\n", + " \"sdkVersion\": \"0.5.1\",\n", " \"timeout\": 0,\n", " \"version\": 1,\n", " \"workingDirectory\": \"/var/holoscan\"\n", @@ -2277,19 +1829,20 @@ " \"memory\": \"1Gi\",\n", " \"gpuMemory\": \"10Gi\"\n", " },\n", - " \"version\": 1\n", + " \"version\": 1,\n", + " \"platformConfig\": \"dgpu\"\n", "}\n", "\n", - "2023-08-30 08:33:39 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app\n", + "2024-04-10 22:13:38 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app\n", "\n", - "2023-08-30 08:33:39 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json\n", - "2023-08-30 08:33:39 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json\n", - "2023-08-30 08:33:39 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml\n", + "2024-04-10 22:13:38 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json\n", + "2024-04-10 22:13:38 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json\n", + "2024-04-10 22:13:38 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml\n", "\n", - "2023-08-30 08:33:39 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models\n", + "2024-04-10 22:13:38 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models\n", "\n", - "2023-08-30 08:33:39 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs\n", - "2023-08-30 08:33:39 [INFO] '/opt/holoscan/docs/' cannot be found.\n", + "2024-04-10 22:13:39 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs\n", + "2024-04-10 22:13:39 [INFO] '/opt/holoscan/docs/' cannot be found.\n", "\n", "app config models\n" ] @@ -2321,20 +1874,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2023-08-30 01:33:44,364] [INFO] (runner) - Checking dependencies...\n", - "[2023-08-30 01:33:44,364] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "[2024-04-10 15:13:42,036] [INFO] (runner) - Checking dependencies...\n", + "[2024-04-10 15:13:42,036] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n", + "\n", + "[2024-04-10 15:13:42,036] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", "\n", - "[2023-08-30 01:33:44,364] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n", + "[2024-04-10 15:13:42,036] [INFO] (runner) - --> Verifying if \"my_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", "\n", - "[2023-08-30 01:33:44,364] [INFO] (runner) - --> Verifying if \"my_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n", + "[2024-04-10 15:13:42,107] [INFO] (runner) - Reading HAP/MAP manifest...\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmpor1kfks8/app.json\n", + "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmpor1kfks8/pkg.json\n", + "[2024-04-10 15:13:42,350] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", "\n", - "[2023-08-30 01:33:44,440] [INFO] (runner) - Reading HAP/MAP manifest...\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.56kB to /tmp/tmpdiaab_y5/app.json\n", - "\u001b[sPreparing to copy...\u001b[?25l\u001b[u\u001b[2KCopying from container - 0B\u001b[?25h\u001b[u\u001b[2KSuccessfully copied 2.05kB to /tmp/tmpdiaab_y5/pkg.json\n", - "[2023-08-30 01:33:44,635] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n", + "[2024-04-10 15:13:42,351] [INFO] (runner) - --> Verifying \"nvidia-ctk\" version...\n", "\n", - "[2023-08-30 01:33:44,838] [INFO] (common) - Launching container (fe5fd1aac7e5) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", - " container name: practical_swirles\n", + "[2024-04-10 15:13:42,710] [INFO] (common) - Launching container (60a9bd0111e6) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...\n", + " container name: hungry_bassi\n", " host name: mingq-dt\n", " network: host\n", " user: 1000:1000\n", @@ -2343,581 +1898,120 @@ " ipc mode: host\n", " shared memory size: 67108864\n", " devices: \n", - "2023-08-30 08:33:45 [INFO] Launching application python3 /opt/holoscan/app ...\n", + " group_add: 44\n", + "2024-04-10 22:13:43 [INFO] Launching application python3 /opt/holoscan/app ...\n", "\n", - "[2023-08-30 08:33:49,660] [INFO] (root) - Parsed args: Namespace(argv=['/opt/holoscan/app'], input=None, log_level=None, model=None, output=None, workdir=None)\n", + "[2024-04-10 22:13:46,276] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['/opt/holoscan/app'])\n", "\n", - "[2023-08-30 08:33:49,668] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", + "[2024-04-10 22:13:46,382] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n", "\n", - "[2023-08-30 08:33:49,671] [INFO] (root) - End compose\n", + "[2024-04-10 22:13:46,384] [INFO] (root) - End compose\n", "\n", - "[info] [app_driver.cpp:1025] Launching the driver/health checking service\n", + "[info] [app_driver.cpp:1161] Launching the driver/health checking service\n", "\n", - "[info] [gxf_executor.cpp:210] Creating context\n", + "[info] [gxf_executor.cpp:211] Creating context\n", "\n", - "[info] [server.cpp:73] Health checking server listening on 0.0.0.0:8777\n", + "[info] [server.cpp:87] Health checking server listening on 0.0.0.0:8777\n", "\n", - "[info] [gxf_executor.cpp:1595] Loading extensions from configs...\n", + "[info] [gxf_executor.cpp:1674] Loading extensions from configs...\n", "\n", - "[info] [gxf_executor.cpp:1741] Activating Graph...\n", + "[info] [gxf_executor.cpp:1864] Activating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1771] Running Graph...\n", + "[info] [gxf_executor.cpp:1894] Running Graph...\n", "\n", - "[info] [gxf_executor.cpp:1773] Waiting for completion...\n", + "[info] [gxf_executor.cpp:1896] Waiting for completion...\n", "\n", - "[info] [gxf_executor.cpp:1774] Graph execution waiting. Fragment: \n", + "[info] [gxf_executor.cpp:1897] Graph execution waiting. Fragment: \n", "\n", "[info] [greedy_scheduler.cpp:190] Scheduling 9 entities\n", "\n", - "[2023-08-30 08:33:49,794] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", + "[2024-04-10 22:13:46,439] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n", "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2024-04-10 22:13:47,210] [INFO] (root) - Finding series for Selection named: CT Series\n", "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n", "\n", " # of series: 1\n", "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", - "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", - "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n", "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - Series attribute Modality value: CT\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", "\n", - "[2023-08-30 08:33:50,169] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - Series attribute Modality value: CT\n", "\n", - "[2023-08-30 08:33:50,170] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", "\n", - "[2023-08-30 08:33:50,170] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", "\n", - "[2023-08-30 08:33:50,170] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n", "\n", - "[2023-08-30 08:33:50,725] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/pancreas_ct_dints/model.ts\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", + "[2024-04-10 22:13:47,211] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n", "\n", - " warn_deprecated(argname, msg, warning_category)\n", + "[2024-04-10 22:13:47,444] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/pancreas_ct_dints/model.ts\n", "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary SaveImaged.__init__:resample: Current default value of argument `resample=True` has been deprecated since version 1.1. It will be changed to `resample=False` in version 1.3.\n", + "[2024-04-10 22:15:33,613] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/spleen_ct/model.ts\n", "\n", - " warn_deprecated(argname, msg, warning_category)\n", - "\n", - "[2023-08-30 08:35:23,643] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/spleen_ct/model.ts\n", - "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", + "/home/holoscan/.local/lib/python3.10/site-packages/highdicom/valuerep.py:54: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n", "\n", " warnings.warn(\n", "\n", - "[2023-08-30 08:35:30,423] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR UL.\n", - "\n", - " warnings.warn(msg)\n", - "\n", - "/home/holoscan/.local/lib/python3.8/site-packages/pydicom/valuerep.py:443: UserWarning: A value of type 'int64' cannot be assigned to a tag with VR US.\n", - "\n", - " warnings.warn(msg)\n", - "\n", - "[2023-08-30 08:35:30,427] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,428] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,428] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,429] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,430] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,430] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,431] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,432] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,432] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,433] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,434] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,435] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,436] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,436] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,437] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,437] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,438] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,439] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,440] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,440] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,441] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,442] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,443] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,444] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,445] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,445] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,446] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,447] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,448] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,448] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,449] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,450] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,620] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,621] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,622] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,622] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,623] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,624] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,625] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,625] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,626] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,627] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,627] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,628] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,629] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,630] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,630] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,631] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,632] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,633] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,633] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,634] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,635] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,636] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,636] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,637] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,638] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,639] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,639] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,640] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,641] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,641] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,642] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,643] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,644] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,645] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,645] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "\n", - "[2023-08-30 08:35:30,663] [INFO] (highdicom.seg.sop) - skip empty plane 0 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,664] [INFO] (highdicom.seg.sop) - skip empty plane 1 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,664] [INFO] (highdicom.seg.sop) - skip empty plane 2 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,664] [INFO] (highdicom.seg.sop) - skip empty plane 3 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,664] [INFO] (highdicom.seg.sop) - skip empty plane 4 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,665] [INFO] (highdicom.seg.sop) - skip empty plane 5 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,665] [INFO] (highdicom.seg.sop) - skip empty plane 6 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,665] [INFO] (highdicom.seg.sop) - skip empty plane 7 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,665] [INFO] (highdicom.seg.sop) - skip empty plane 8 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,665] [INFO] (highdicom.seg.sop) - skip empty plane 9 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,666] [INFO] (highdicom.seg.sop) - skip empty plane 10 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,666] [INFO] (highdicom.seg.sop) - skip empty plane 11 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,666] [INFO] (highdicom.seg.sop) - skip empty plane 12 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,666] [INFO] (highdicom.seg.sop) - skip empty plane 13 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,666] [INFO] (highdicom.seg.sop) - skip empty plane 14 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,667] [INFO] (highdicom.seg.sop) - skip empty plane 15 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,667] [INFO] (highdicom.seg.sop) - skip empty plane 16 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,667] [INFO] (highdicom.seg.sop) - skip empty plane 17 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,667] [INFO] (highdicom.seg.sop) - skip empty plane 18 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,667] [INFO] (highdicom.seg.sop) - skip empty plane 19 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,667] [INFO] (highdicom.seg.sop) - skip empty plane 20 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,668] [INFO] (highdicom.seg.sop) - skip empty plane 21 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,668] [INFO] (highdicom.seg.sop) - skip empty plane 22 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,668] [INFO] (highdicom.seg.sop) - skip empty plane 23 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,668] [INFO] (highdicom.seg.sop) - skip empty plane 24 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,668] [INFO] (highdicom.seg.sop) - skip empty plane 25 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,669] [INFO] (highdicom.seg.sop) - skip empty plane 26 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,669] [INFO] (highdicom.seg.sop) - skip empty plane 27 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,669] [INFO] (highdicom.seg.sop) - skip empty plane 28 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,669] [INFO] (highdicom.seg.sop) - skip empty plane 29 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,669] [INFO] (highdicom.seg.sop) - skip empty plane 30 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,670] [INFO] (highdicom.seg.sop) - skip empty plane 31 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,670] [INFO] (highdicom.seg.sop) - skip empty plane 32 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,670] [INFO] (highdicom.seg.sop) - skip empty plane 33 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,670] [INFO] (highdicom.seg.sop) - skip empty plane 34 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,670] [INFO] (highdicom.seg.sop) - skip empty plane 35 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,671] [INFO] (highdicom.seg.sop) - skip empty plane 36 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,671] [INFO] (highdicom.seg.sop) - skip empty plane 37 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,671] [INFO] (highdicom.seg.sop) - skip empty plane 38 of segment #2\n", + "[2024-04-10 22:15:37,078] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:35:30,671] [INFO] (highdicom.seg.sop) - skip empty plane 39 of segment #2\n", + "[2024-04-10 22:15:37,078] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", "\n", - "[2023-08-30 08:35:30,671] [INFO] (highdicom.seg.sop) - skip empty plane 40 of segment #2\n", + "[2024-04-10 22:15:37,078] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:35:30,671] [INFO] (highdicom.seg.sop) - skip empty plane 41 of segment #2\n", + "[2024-04-10 22:15:37,078] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", "\n", - "[2023-08-30 08:35:30,672] [INFO] (highdicom.seg.sop) - skip empty plane 42 of segment #2\n", + "[2024-04-10 22:15:37,079] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", "\n", - "[2023-08-30 08:35:30,672] [INFO] (highdicom.seg.sop) - skip empty plane 43 of segment #2\n", + "[2024-04-10 22:15:37,079] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:35:30,672] [INFO] (highdicom.seg.sop) - skip empty plane 44 of segment #2\n", + "[2024-04-10 22:15:37,079] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", "\n", - "[2023-08-30 08:35:30,672] [INFO] (highdicom.seg.sop) - skip empty plane 45 of segment #2\n", + "[2024-04-10 22:15:37,079] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", "\n", - "[2023-08-30 08:35:30,672] [INFO] (highdicom.seg.sop) - skip empty plane 46 of segment #2\n", + "[2024-04-10 22:15:37,079] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "\n", - "[2023-08-30 08:35:30,673] [INFO] (highdicom.seg.sop) - skip empty plane 47 of segment #2\n", + "[2024-04-10 22:15:38,307] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:35:30,673] [INFO] (highdicom.seg.sop) - skip empty plane 48 of segment #2\n", + "[2024-04-10 22:15:38,307] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", "\n", - "[2023-08-30 08:35:30,673] [INFO] (highdicom.seg.sop) - skip empty plane 49 of segment #2\n", + "[2024-04-10 22:15:38,307] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:35:30,673] [INFO] (highdicom.seg.sop) - skip empty plane 50 of segment #2\n", + "[2024-04-10 22:15:38,307] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", "\n", - "[2023-08-30 08:35:30,673] [INFO] (highdicom.seg.sop) - skip empty plane 51 of segment #2\n", + "[2024-04-10 22:15:38,308] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", "\n", - "[2023-08-30 08:35:30,674] [INFO] (highdicom.seg.sop) - skip empty plane 52 of segment #2\n", + "[2024-04-10 22:15:38,308] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", "\n", - "[2023-08-30 08:35:30,674] [INFO] (highdicom.seg.sop) - skip empty plane 53 of segment #2\n", + "[2024-04-10 22:15:38,308] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", "\n", - "[2023-08-30 08:35:30,674] [INFO] (highdicom.seg.sop) - skip empty plane 54 of segment #2\n", + "[2024-04-10 22:15:38,308] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", "\n", - "[2023-08-30 08:35:30,674] [INFO] (highdicom.seg.sop) - skip empty plane 55 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,674] [INFO] (highdicom.seg.sop) - skip empty plane 56 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,674] [INFO] (highdicom.seg.sop) - skip empty plane 57 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,675] [INFO] (highdicom.seg.sop) - skip empty plane 58 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,675] [INFO] (highdicom.seg.sop) - skip empty plane 59 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,675] [INFO] (highdicom.seg.sop) - skip empty plane 60 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,675] [INFO] (highdicom.seg.sop) - skip empty plane 61 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,675] [INFO] (highdicom.seg.sop) - skip empty plane 62 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,676] [INFO] (highdicom.seg.sop) - skip empty plane 63 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,676] [INFO] (highdicom.seg.sop) - skip empty plane 64 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,676] [INFO] (highdicom.seg.sop) - skip empty plane 65 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,676] [INFO] (highdicom.seg.sop) - skip empty plane 66 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,676] [INFO] (highdicom.seg.sop) - skip empty plane 67 of segment #2\n", - "\n", - "[2023-08-30 08:35:30,701] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "\n", - "[2023-08-30 08:35:30,701] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "\n", - "[2023-08-30 08:35:30,702] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "\n", - "[2023-08-30 08:35:30,702] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "\n", - "[2023-08-30 08:35:30,702] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "\n", - "[2023-08-30 08:35:30,702] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "\n", - "[2023-08-30 08:35:30,702] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "\n", - "[2023-08-30 08:35:30,702] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "\n", - "[2023-08-30 08:35:30,703] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", - "\n", - "[2023-08-30 08:35:32,827] [INFO] (highdicom.seg.sop) - add plane #0 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,828] [INFO] (highdicom.seg.sop) - add plane #1 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,829] [INFO] (highdicom.seg.sop) - add plane #2 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,830] [INFO] (highdicom.seg.sop) - add plane #3 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,831] [INFO] (highdicom.seg.sop) - add plane #4 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,832] [INFO] (highdicom.seg.sop) - add plane #5 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,832] [INFO] (highdicom.seg.sop) - add plane #6 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,833] [INFO] (highdicom.seg.sop) - add plane #7 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,834] [INFO] (highdicom.seg.sop) - add plane #8 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,835] [INFO] (highdicom.seg.sop) - add plane #9 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,835] [INFO] (highdicom.seg.sop) - add plane #10 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,836] [INFO] (highdicom.seg.sop) - add plane #11 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,837] [INFO] (highdicom.seg.sop) - add plane #12 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,837] [INFO] (highdicom.seg.sop) - add plane #13 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,838] [INFO] (highdicom.seg.sop) - add plane #14 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,839] [INFO] (highdicom.seg.sop) - add plane #15 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,840] [INFO] (highdicom.seg.sop) - add plane #16 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,840] [INFO] (highdicom.seg.sop) - add plane #17 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,841] [INFO] (highdicom.seg.sop) - add plane #18 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,842] [INFO] (highdicom.seg.sop) - add plane #19 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,842] [INFO] (highdicom.seg.sop) - add plane #20 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,843] [INFO] (highdicom.seg.sop) - add plane #21 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,844] [INFO] (highdicom.seg.sop) - add plane #22 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,844] [INFO] (highdicom.seg.sop) - add plane #23 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,845] [INFO] (highdicom.seg.sop) - add plane #24 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,846] [INFO] (highdicom.seg.sop) - add plane #25 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,846] [INFO] (highdicom.seg.sop) - add plane #26 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,847] [INFO] (highdicom.seg.sop) - add plane #27 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,848] [INFO] (highdicom.seg.sop) - add plane #28 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,849] [INFO] (highdicom.seg.sop) - add plane #29 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,849] [INFO] (highdicom.seg.sop) - add plane #30 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,850] [INFO] (highdicom.seg.sop) - add plane #31 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,851] [INFO] (highdicom.seg.sop) - add plane #32 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,852] [INFO] (highdicom.seg.sop) - add plane #33 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,852] [INFO] (highdicom.seg.sop) - add plane #34 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,853] [INFO] (highdicom.seg.sop) - add plane #35 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,854] [INFO] (highdicom.seg.sop) - add plane #36 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,854] [INFO] (highdicom.seg.sop) - add plane #37 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,855] [INFO] (highdicom.seg.sop) - add plane #38 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,856] [INFO] (highdicom.seg.sop) - add plane #39 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,857] [INFO] (highdicom.seg.sop) - add plane #40 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,857] [INFO] (highdicom.seg.sop) - add plane #41 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,858] [INFO] (highdicom.seg.sop) - add plane #42 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,859] [INFO] (highdicom.seg.sop) - add plane #43 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,859] [INFO] (highdicom.seg.sop) - add plane #44 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,861] [INFO] (highdicom.seg.sop) - add plane #45 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,861] [INFO] (highdicom.seg.sop) - add plane #46 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,862] [INFO] (highdicom.seg.sop) - add plane #47 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,863] [INFO] (highdicom.seg.sop) - add plane #48 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,863] [INFO] (highdicom.seg.sop) - add plane #49 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,864] [INFO] (highdicom.seg.sop) - add plane #50 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,865] [INFO] (highdicom.seg.sop) - add plane #51 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,865] [INFO] (highdicom.seg.sop) - add plane #52 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,866] [INFO] (highdicom.seg.sop) - add plane #53 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,867] [INFO] (highdicom.seg.sop) - add plane #54 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,867] [INFO] (highdicom.seg.sop) - add plane #55 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,868] [INFO] (highdicom.seg.sop) - add plane #56 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,869] [INFO] (highdicom.seg.sop) - add plane #57 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,869] [INFO] (highdicom.seg.sop) - add plane #58 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,870] [INFO] (highdicom.seg.sop) - add plane #59 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,871] [INFO] (highdicom.seg.sop) - add plane #60 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,872] [INFO] (highdicom.seg.sop) - add plane #61 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,873] [INFO] (highdicom.seg.sop) - add plane #62 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,873] [INFO] (highdicom.seg.sop) - add plane #63 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,874] [INFO] (highdicom.seg.sop) - add plane #64 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,875] [INFO] (highdicom.seg.sop) - add plane #65 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,875] [INFO] (highdicom.seg.sop) - add plane #66 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,876] [INFO] (highdicom.seg.sop) - add plane #67 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,877] [INFO] (highdicom.seg.sop) - add plane #68 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,878] [INFO] (highdicom.seg.sop) - add plane #69 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,879] [INFO] (highdicom.seg.sop) - add plane #70 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,880] [INFO] (highdicom.seg.sop) - add plane #71 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,880] [INFO] (highdicom.seg.sop) - add plane #72 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,881] [INFO] (highdicom.seg.sop) - add plane #73 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,882] [INFO] (highdicom.seg.sop) - add plane #74 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,882] [INFO] (highdicom.seg.sop) - add plane #75 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,883] [INFO] (highdicom.seg.sop) - add plane #76 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,884] [INFO] (highdicom.seg.sop) - add plane #77 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,884] [INFO] (highdicom.seg.sop) - add plane #78 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,885] [INFO] (highdicom.seg.sop) - add plane #79 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,886] [INFO] (highdicom.seg.sop) - add plane #80 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,886] [INFO] (highdicom.seg.sop) - add plane #81 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,887] [INFO] (highdicom.seg.sop) - add plane #82 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,888] [INFO] (highdicom.seg.sop) - add plane #83 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,889] [INFO] (highdicom.seg.sop) - add plane #84 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,889] [INFO] (highdicom.seg.sop) - add plane #85 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,890] [INFO] (highdicom.seg.sop) - add plane #86 for segment #1\n", - "\n", - "[2023-08-30 08:35:32,931] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "\n", - "[2023-08-30 08:35:32,932] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n", - "\n", - "[2023-08-30 08:35:32,932] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "\n", - "[2023-08-30 08:35:32,932] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n", - "\n", - "[2023-08-30 08:35:32,932] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n", - "\n", - "[2023-08-30 08:35:32,932] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n", - "\n", - "[2023-08-30 08:35:32,932] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n", - "\n", - "[2023-08-30 08:35:32,933] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n", - "\n", - "[2023-08-30 08:35:32,933] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", + "[2024-04-10 22:15:38,308] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n", "\n", "[info] [greedy_scheduler.cpp:369] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n", "\n", "[info] [greedy_scheduler.cpp:398] Scheduler finished.\n", "\n", - "[info] [gxf_executor.cpp:1783] Graph execution deactivating. Fragment: \n", + "[info] [gxf_executor.cpp:1906] Graph execution deactivating. Fragment: \n", "\n", - "[info] [gxf_executor.cpp:1784] Deactivating Graph...\n", + "[info] [gxf_executor.cpp:1907] Deactivating Graph...\n", "\n", - "[info] [gxf_executor.cpp:1787] Graph execution finished. Fragment: \n", + "[info] [gxf_executor.cpp:1910] Graph execution finished. Fragment: \n", "\n", - "[2023-08-30 08:35:33,048] [INFO] (app.App) - End run\n", + "[2024-04-10 22:15:38,401] [INFO] (app.App) - End run\n", "\n", - "[2023-08-30 01:35:34,796] [INFO] (common) - Container 'practical_swirles'(fe5fd1aac7e5) exited.\n" + "[2024-04-10 15:15:39,461] [INFO] (common) - Container 'hungry_bassi'(60a9bd0111e6) exited.\n" ] } ], @@ -2943,16 +2037,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.2.826.0.1.3680043.10.511.3.42782193787457037272655949212805965.dcm\n", - "1.2.826.0.1.3680043.10.511.3.96201497877957065691838845939203299.dcm\n" - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + "1.2.826.0.1.3680043.10.511.3.77817234108119246236647417839296398.dcm\n", + "1.2.826.0.1.3680043.10.511.3.83680469536583357494244170492806366.dcm\n" ] } ], @@ -2977,7 +2063,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" }, "vscode": { "interpreter": { diff --git a/pyproject.toml b/pyproject.toml index a7aefad4..10fcfc02 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ build-backend = "setuptools.build_meta" [tool.black] line-length = 120 -target-version = ['py37', 'py38', 'py39'] +target-version = ['py38', 'py39', 'py310', 'py311', 'py312'] include = '\.pyi?$' exclude = ''' ( diff --git a/requirements.txt b/requirements.txt index 2f03f20d..9034567c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -holoscan~=0.6.0 +holoscan~=1.0 numpy>=1.21.6 colorama>=0.4.1 typeguard>=3.0.0 diff --git a/run b/run index 493e7f6e..bcfdc8eb 100755 --- a/run +++ b/run @@ -326,7 +326,7 @@ install_python_dev_deps() { fi # Adding temp fix to address the issue of holoscan sdk dragging in low level dependencies, e.g. libcuda.so - fix_holoscan_import + # fix_holoscan_import install_edit_mode } diff --git a/setup.cfg b/setup.cfg index 58248242..c7e5e227 100644 --- a/setup.cfg +++ b/setup.cfg @@ -24,7 +24,7 @@ python_requires = >= 3.8 # cucim install_requires = numpy>=1.21.6 - holoscan~=0.6.0 + holoscan~=1.0 colorama>=0.4.1 typeguard>=3.0.0 @@ -48,7 +48,9 @@ ignore = # B027, #method in base class with no implementation B027, # B905 `zip()` without an explicit `strict=` parameter, but conflicting with pytype - B905 + B905, + # B026 Star-arg unpacking after a keyword argument is strongly discouraged + B026 per_file_ignores = # e.g. F403 'from holoscan.conditions import *' used; unable to detect undefined names