Bump the pip group across 1 directory with 5 updates #23

dependabot · 2025-01-08T20:16:14Z

Bumps the pip group with 5 updates in the / directory:

Package	From	To
numpy	`1.14.5`	`2.2.1`
scikit-learn	`0.19.2`	`1.5.0`
h2o	`3.26.0.9`	`3.46.0.1`
keras	`2.3.1`	`3.8.0`
tensorflow	`1.14.0`	`2.12.1`

Updates numpy from 1.14.5 to 2.2.1

Release notes

Sourced from numpy's releases.

2.2.1 (DEC 21, 2024)

NumPy 2.2.1 Release Notes

NumPy 2.2.1 is a patch release following 2.2.0. It fixes bugs found after the 2.2.0 release and has several maintenance pins to work around upstream changes.

There was some breakage in downstream projects following the 2.2.0 release due to updates to NumPy typing. Because of problems due to MyPy defects, we recommend using basedpyright for type checking, it can be installed from PyPI. The Pylance extension for Visual Studio Code is also based on Pyright. Problems that persist when using basedpyright should be reported as issues on the NumPy github site.

This release supports Python 3.10-3.13.

Contributors

A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris

Joren Hammudoglu

Matti Picus

Nathan Goldbaum

Peter Hawkins

Simon Altrogge

Thomas A Caswell

Warren Weckesser

Yang Wang +

Pull requests merged

A total of 12 pull requests were merged for this release.

#27935: MAINT: Prepare 2.2.x for further development

#27950: TEST: cleanups

#27958: BUG: fix use-after-free error in npy_hashtable.cpp (#27955)

#27959: BLD: add missing include

#27982: BUG:fix compile error libatomic link test to meson.build

#27990: TYP: Fix falsely rejected value types in ndarray.__setitem__

#27991: MAINT: Don't wrap #include <Python.h> with extern "C"

#27993: BUG: Fix segfault in stringdtype lexsort

#28006: MAINT: random: Tweak module code in mtrand.pyx to fix a Cython...

#28007: BUG: Cython API was missing NPY_UINTP.

#28021: CI: pin scipy-doctest to 1.5.1

#28044: TYP: allow None in operand sequence of nditer

Checksums

... (truncated)

Changelog

Sourced from numpy's changelog.

This is a walkthrough of the NumPy 2.1.0 release on Linux, modified for building with GitHub Actions and cibuildwheels and uploading to the anaconda.org staging repository for NumPy <https://anaconda.org/multibuild-wheels-staging/numpy>_. The commands can be copied into the command line, but be sure to replace 2.1.0 by the correct version. This should be read together with the :ref:general release guide <prepare_release>.

Facility preparation

Before beginning to make a release, use the requirements/*_requirements.txt files to ensure that you have the needed software. Most software can be installed with pip, but some will require apt-get, dnf, or whatever your system uses for software. You will also need a GitHub personal access token (PAT) to push the documentation. There are a few ways to streamline things:

Git can be set up to use a keyring to store your GitHub personal access token. Search online for the details.

You can use the keyring app to store the PyPI password for twine. See the online twine documentation for details.

Prior to release

Add/drop Python versions

When adding or dropping Python versions, three files need to be edited:

.github/workflows/wheels.yml # for github cibuildwheel

tools/ci/cirrus_wheels.yml # for cibuildwheel aarch64/arm64 builds

pyproject.toml # for classifier and minimum version check.

Make these changes in an ordinary PR against main and backport if necessary. Add [wheel build] at the end of the title line of the commit summary so that wheel builds will be run to test the changes. We currently release wheels for new Python versions after the first Python rc once manylinux and cibuildwheel support it. For Python 3.11 we were able to release within a week of the rc1 announcement.

Backport pull requests

Changes that have been marked for this release must be backported to the maintenance/2.1.x branch.

Update 2.1.0 milestones

... (truncated)

Commits

7469245 Merge pull request #28047 from charris/prepare-2.2.1
acb051e REL: Prepare for the NumPy 2.2.1 release [wheel build]
28a091a Merge pull request #28044 from charris/backport-28039
723605b TST: Add test for allowing None in operand sequence passed to nditer
554739e TYP: allow None in operand sequence of nditer
31bc4c8 Merge pull request #28021 from charris/backport-28020
32f52a3 CI: pin scipy-doctest to 1.5.1 (#28020)
6219aeb Merge pull request #28007 from charris/backport-28005
eb7071c Merge pull request #28006 from charris/backport-28003
4f82c32 BUG: Cython API was missing NPY_UINTP.
Additional commits viewable in compare view

Updates scikit-learn from 0.19.2 to 1.5.0

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.5.0

We're happy to announce the 1.5.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.5.html

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:
pip install -U scikit-learn
The conda-forge builds can be installed using:
conda install -c conda-forge scikit-learn
Scikit-learn 1.4.2

We're happy to announce the 1.4.2 release.

This release only includes support for numpy 2.

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:
pip install -U scikit-learn
Scikit-learn 1.4.1.post1

We're happy to announce the 1.4.1.post1 release.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.4.html#version-1-4-1-post1

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:
pip install -U scikit-learn
The conda-forge builds can be installed using:
conda install -c conda-forge scikit-learn

... (truncated)

Commits

b51d0c9 trigger whell builder [cd build]
919ae9b MAINT Reoder what's new for 1.5 (#29039)
0ac28ad DOC Release highlights 1.5 (#29007)
729b54d test py3.12 against numpy 2 [cd build]
1e50434 set version
ffbe4ab DOC remove obsolete SVM example (#27108)
4647729 DOC Fix time complexity of MLP (#28592)
9bd7047 FIX convergence criterion of MeanShift (#28951)
b79420f FIX add long long for int32/int64 windows compat in NumPy 2.0 (#29029)
37f544d DOC replace pandas with Polars in examples/gaussian_process/plot_gpr_co2.py (...
Additional commits viewable in compare view

Updates h2o from 3.26.0.9 to 3.46.0.1

Changelog

Sourced from h2o's changelog.

Newer Releases

Yau (3.26.0.11) - 12/05/2019

Download at: http://h2o-release.s3.amazonaws.com/h2o/rel-yau/11/index.html

... (truncated)

Commits

a35cd8a GH-16104: 3.46.0.1 Release Notes (#16105)
294ef49 Update benchmark intervals [nocheck] (#16122)
5927bb1 Revert GH-15857 AutoML pipeline [nocheck] (#16117)
7617ac4 Change gradle.properties for new rel-3.46.0 branch (#16118)
6830131 GH-16113 - config user for all releases (#16114)
b1c9456 GH-15683 - Fix INFO check - remove new SECURITY_WARNING by adding web_ip para...
877efc8 GH-16110 Check if path is local path when checking denied files (#16111)
e9d0563 GH-15684 Extend timeout for LOGGER test (#16107)
deccee9 GH-16102 - upgrade nimbus-jose-jwt to 9.37.2 (#16103)
70f43c9 Merge remote-tracking branch 'origin/rel-3.44.0'
Additional commits viewable in compare view

Updates keras from 2.3.1 to 3.8.0

Release notes

Sourced from keras's releases.

Keras 3.8.0

New: OpenVINO backend

OpenVINO is now available as an infererence-only Keras backend. You can start using it by setting the backend field to "open_vino" in your keras.json config file.

OpenVINO is a deep learning inference-only framework tailored for CPU (x86, ARM), certain GPUs (OpenCL capable, integrated and discrete) and certain AI accelerators (Intel NPU).

Because OpenVINO does not support gradients, you cannot use it for training (e.g. model.fit()) -- only inference. You can train your models with the JAX/TensorFlow/PyTorch backends, and when trained, reload them with the OpenVINO backend for inference on a target device supported by OpenVINO.

New: ONNX model export

You can now export your Keras models to the ONNX format from the JAX, TensorFlow, and PyTorch backends.

Just pass format="onnx" in your model.export() call:
# Export the model as a ONNX artifact
model.export("path/to/location", format="onnx")
Load the artifact in a different process/environment
ort_session = onnxruntime.InferenceSession("path/to/location")
Run inference
ort_inputs = {
k.name: v for k, v in zip(ort_session.get_inputs(), input_data)
}
predictions = ort_session.run(None, ort_inputs)
New: Scikit-Learn API compatibility interface

It's now possible to easily integrate Keras models into Sciki-Learn pipelines! The following wrapper classes are available:

keras.wrappers.SKLearnClassifier: implements the sklearn Classifier API

keras.wrappers.SKLearnRegressor: implements the sklearn Regressor API

keras.wrappers.SKLearnTransformer: implements the sklearn Transformer API

Other feature additions

Add new ops:

Add keras.ops.diagflat

Add keras.ops.unravel_index

Add new activations:

Add sparse_plus activation

Add sparsemax activation

Add new image augmentation and preprocessing layers:

Add keras.layers.RandAugment

Add keras.layers.Equalization

Add keras.layers.MixUp

... (truncated)

Commits

fbf0af7 Fixing batch_dim_name attribute (#20674)
ab3c8f5 Add rand_augment processing layer (#20716)
8f04616 Specify window_length dtype requirement in tf.keras.ops.istft in math.py (#20...
1adaaec Add build method to supress warning (#20729)
881d8da Patch tf2onnx to ensure compatibility with numpy>=2.0.0 (#20725)
94977dd Refactor keras/src/export/export_lib and add export_onnx (#20710)
41c429e Update version number
a91d006 Update BackupAndRestore class example (#20714)
2c95bfe Fix torch GPU CI
5b29974 implement transform_bounding_boxes for random_shear (#20704)
Additional commits viewable in compare view

Updates tensorflow from 1.14.0 to 2.12.1

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.12.1

Release 2.12.1

Bug Fixes and Other Changes

The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance.

TensorFlow 2.12.0

Release 2.12.0

TensorFlow

Breaking Changes

Build, Compilation and Packaging

Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

tf.function:

tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:

Using functools.wraps on a function with different signature

Using functools.partial with an invalid tf.function input

tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.

Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.

tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.

tf.function now traces with placeholder values generated by TraceType instead of the value itself.

Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

Support for Python 3.11 has been added.

Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

tf.lite:

Add 16-bit float type support for built-in op fill.

Transpose now supports 6D tensors.

Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

tf.experimental.dtensor:

Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.

Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

tf.data:

Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().

Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.12.1

Bug Fixes and Other Changes

The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance.

Release 2.12.0

Breaking Changes

Build, Compilation and Packaging

Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

tf.function:

tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:

Using functools.wraps on a function with different signature

Using functools.partial with an invalid tf.function input

tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.

Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.

tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.

tf.function now traces with placeholder values generated by TraceType instead of the value itself.

Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

Support for Python 3.11 has been added.

Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

tf.lite:

Add 16-bit float type support for built-in op fill.

Transpose now supports 6D tensors.

Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

tf.experimental.dtensor:

Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.

Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

tf.data:

Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().

Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.

Added a new rerandomize_each_iteration argument for the tf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If seed is set and rerandomize_each_iteration=True, the sample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.

tf.test:

... (truncated)

Commits

8e2b665 Merge pull request #61094 from tensorflow/venkat-patch-444
02478f0 Fix unit test failure caused by numpy update
2cd9b41 Merge pull request #61082 from tensorflow/venkat-patch-333
7995c95 Updating Simplified retry logic to DNS cache
29479ed Merge pull request #60872 from tensorflow/r2.12-c45a6c0b1cb
e76a933 Simplified retry logic to DNS cache
76addf7 Merge pull request #60850 from elfringham/non_pip_fix
05987a8 [Linaro:ARM_CI] Fix permissions for running nonpip tests
23724d2 Merge pull request #60842 from elfringham/r2.12
496730b Limit typing_extensions to less than 4.6.0 until it works
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Bumps the pip group with 5 updates in the / directory: | Package | From | To | | --- | --- | --- | | [numpy](https://github.com/numpy/numpy) | `1.14.5` | `2.2.1` | | [scikit-learn](https://github.com/scikit-learn/scikit-learn) | `0.19.2` | `1.5.0` | | [h2o](https://github.com/h2oai/h2o-3) | `3.26.0.9` | `3.46.0.1` | | [keras](https://github.com/keras-team/keras) | `2.3.1` | `3.8.0` | | [tensorflow](https://github.com/tensorflow/tensorflow) | `1.14.0` | `2.12.1` | Updates `numpy` from 1.14.5 to 2.2.1 - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](numpy/numpy@v1.14.5...v2.2.1) Updates `scikit-learn` from 0.19.2 to 1.5.0 - [Release notes](https://github.com/scikit-learn/scikit-learn/releases) - [Commits](scikit-learn/scikit-learn@0.19.2...1.5.0) Updates `h2o` from 3.26.0.9 to 3.46.0.1 - [Changelog](https://github.com/h2oai/h2o-3/blob/master/Changes-prior-3.28.0.1.md) - [Commits](h2oai/h2o-3@jenkins-3.26.0.9...jenkins-3.46.0.1) Updates `keras` from 2.3.1 to 3.8.0 - [Release notes](https://github.com/keras-team/keras/releases) - [Commits](keras-team/keras@2.3.1...v3.8.0) Updates `tensorflow` from 1.14.0 to 2.12.1 - [Release notes](https://github.com/tensorflow/tensorflow/releases) - [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md) - [Commits](tensorflow/tensorflow@v1.14.0...v2.12.1) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production dependency-group: pip - dependency-name: scikit-learn dependency-type: direct:production dependency-group: pip - dependency-name: h2o dependency-type: direct:production dependency-group: pip - dependency-name: keras dependency-type: direct:production dependency-group: pip - dependency-name: tensorflow dependency-type: direct:production dependency-group: pip ... Signed-off-by: dependabot[bot] <[email protected]>

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Bump the pip group across 1 directory with 5 updates #23

Bump the pip group across 1 directory with 5 updates #23

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Bump the pip group across 1 directory with 5 updates #23

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2.2.1 (DEC 21, 2024)

NumPy 2.2.1 Release Notes

Contributors

Pull requests merged

Checksums

Facility preparation

Prior to release

Add/drop Python versions

Backport pull requests

Update 2.1.0 milestones

Scikit-learn 1.5.0

Scikit-learn 1.4.2

Scikit-learn 1.4.1.post1

Newer Releases

Yau (3.26.0.11) - 12/05/2019

Keras 3.8.0

New: OpenVINO backend

New: ONNX model export

New: Scikit-Learn API compatibility interface

Other feature additions

TensorFlow 2.12.1

Release 2.12.1

Bug Fixes and Other Changes

TensorFlow 2.12.0

Release 2.12.0

TensorFlow

Breaking Changes

Major Features and Improvements

Release 2.12.1

Bug Fixes and Other Changes

Release 2.12.0

Breaking Changes

Major Features and Improvements