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Ray Environments

Ray Compute Environment

This is the environment for a workspace that can connect to the Ray Cluster that can connect to the On-Demand Ray cluster attached to the Workspace

(Custom) Base Image

quay.io/domino/compute-environment-images:ubuntu20-py3.9-r4.3-ray2.4.0-domino5.7

Pluggable Workspace Tools

jupyter:
  title: "Jupyter (Python, R, Julia)"
  iconUrl: "/assets/images/workspace-logos/Jupyter.svg"
  start: [ "/opt/domino/workspaces/jupyter/start" ]
  supportedFileExtensions: [ ".ipynb" ]
  httpProxy:
    port: 8888
    rewrite: false
    internalPath: "/{{ownerUsername}}/{{projectName}}/{{sessionPathComponent}}/{{runId}}/{{#if pathToOpen}}tree/{{pathToOpen}}{{/if}}"
    requireSubdomain: false
jupyterlab:
  title: "JupyterLab"
  iconUrl: "/assets/images/workspace-logos/jupyterlab.svg"
  start: [  "/opt/domino/workspaces/jupyterlab/start" ]
  httpProxy:
    internalPath: "/{{ownerUsername}}/{{projectName}}/{{sessionPathComponent}}/{{runId}}/{{#if pathToOpen}}tree/{{pathToOpen}}{{/if}}"
    port: 8888
    rewrite: false
    requireSubdomain: false
vscode:
  title: "vscode"
  iconUrl: "/assets/images/workspace-logos/vscode.svg"
  start: [ "/opt/domino/workspaces/vscode/start" ]
  httpProxy:
    port: 8888
    requireSubdomain: false
rstudio:
  title: "RStudio"
  iconUrl: "/assets/images/workspace-logos/Rstudio.svg"
  start: [ "/opt/domino/workspaces/rstudio/start" ]
  httpProxy:
    port: 8888
    requireSubdomain: false

Ray Cluster Environment

(Custom) Base Image

quay.io/domino/cluster-environment-images:ray2.4.0-py3.9-domino5.7

In the "Supported Cluster Settings" select Ray Enable Ray Cluster

GPU Support

Add the following to the Dockerfile instruction set for both compute and cluster environment definitions where GPU support is needed

USER root
RUN pip install torchmetrics 
RUN pip install pytorch_lightning 
RUN pip install install lightning-bolts 

#FROM quay.io/domino/compute-environment-images:ubuntu20-py3.9-r4.3-domino5.7-standard

ARG DEBIAN_FRONTEND=noninteractive

#######################################################
#         Install CUDA and GPU dependencies           #
#######################################################
# Based off https://gitlab.com/nvidia/container-images/cuda/-/tree/master/dist/11.7.0/ubuntu2004

# Install CUDA Base
ENV NVARCH x86_64

ENV NVIDIA_REQUIRE_CUDA "cuda>=11.7 brand=tesla,driver>=450,driver<451 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511"
ENV NV_CUDA_CUDART_VERSION 11.7.60-1
ENV NV_CUDA_COMPAT_PACKAGE cuda-compat-11-7

RUN curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add - && \
    echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list && \
    apt-get update && apt-get install -y --no-install-recommends \
    gnupg2 curl ca-certificates && \
    curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/${NVARCH}/3bf863cc.pub | apt-key add - && \
    echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/${NVARCH} /" > /etc/apt/sources.list.d/cuda.list && \
    apt-get purge --autoremove -y curl \
    && rm -rf /var/lib/apt/lists/*

ENV CUDA_VERSION 11.7.0

# For libraries in the cuda-compat-* package: https://docs.nvidia.com/cuda/eula/index.html#attachment-a
RUN apt-get update && apt-get install -y --no-install-recommends \
    cuda-cudart-11-7=${NV_CUDA_CUDART_VERSION} \
    ${NV_CUDA_COMPAT_PACKAGE} \
    && rm -rf /var/lib/apt/lists/*

# Required for nvidia-docker v1
RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf \
    && echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf

ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64


# nvidia-container-runtime
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility



# CUDANN8
ENV NV_CUDNN_VERSION 8.5.0.96
ENV NV_CUDNN_PACKAGE_NAME "libcudnn8"

ENV NV_CUDNN_PACKAGE "libcudnn8=$NV_CUDNN_VERSION-1+cuda11.7"
ENV NV_CUDNN_PACKAGE_DEV "libcudnn8-dev=$NV_CUDNN_VERSION-1+cuda11.7"


RUN apt-get update && apt-get install -y --no-install-recommends \
    ${NV_CUDNN_PACKAGE} \
    && apt-mark hold ${NV_CUDNN_PACKAGE_NAME} \
    && rm -rf /var/lib/apt/lists/*


# Install CUDA Runtime
ENV NV_CUDA_LIB_VERSION 11.7.0-1
ENV NV_NVTX_VERSION 11.7.50-1
ENV NV_LIBNPP_VERSION 11.7.3.21-1
ENV NV_LIBNPP_PACKAGE libnpp-11-7=${NV_LIBNPP_VERSION}
ENV NV_LIBCUSPARSE_VERSION 11.7.3.50-1

ENV NV_LIBCUBLAS_PACKAGE_NAME libcublas-11-7
ENV NV_LIBCUBLAS_VERSION 11.10.1.25-1
ENV NV_LIBCUBLAS_PACKAGE ${NV_LIBCUBLAS_PACKAGE_NAME}=${NV_LIBCUBLAS_VERSION}

ENV NV_LIBNCCL_PACKAGE_NAME libnccl2
ENV NV_LIBNCCL_PACKAGE_VERSION 2.13.4-1
ENV NCCL_VERSION 2.13.4-1
ENV NV_LIBNCCL_PACKAGE ${NV_LIBNCCL_PACKAGE_NAME}=${NV_LIBNCCL_PACKAGE_VERSION}+cuda11.7

RUN apt-get update && apt-get install -y --no-install-recommends \
    cuda-libraries-11-7=${NV_CUDA_LIB_VERSION} \
    ${NV_LIBNPP_PACKAGE} \
    cuda-nvtx-11-7=${NV_NVTX_VERSION} \
    libcusparse-11-7=${NV_LIBCUSPARSE_VERSION} \
    ${NV_LIBCUBLAS_PACKAGE} \
    ${NV_LIBNCCL_PACKAGE} \
    && rm -rf /var/lib/apt/lists/*

# Keep apt from auto upgrading the cublas and nccl packages. See https://gitlab.com/nvidia/container-images/cuda/-/issues/88
RUN apt-mark hold ${NV_LIBCUBLAS_PACKAGE_NAME} ${NV_LIBNCCL_PACKAGE_NAME}

# Install Tensorflow
RUN pip install tensorflow --user

# Install Jupyter-ai 
RUN pip install 'jupyter-ai>=1.0,<2.0' --user


#python libraries
RUN pip install pytorch-lightning
RUN pip install torchmetrics
RUN pip install pytorch-lightning-bolts
RUN pip install git+https://github.com/PytorchLightning/lightning-bolts.git@master --upgrade
RUN pip install torchvision
RUN pip install GPUtil
RUN pip install torch torchvision tensortrade
#RUN pip install wandb
RUN pip install tblib
RUN pip install pandas==1.4.1
RUN pip install nvidia-ml-py3
RUN pip install pystan numpyro[cuda] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html 
USER ubuntu

Non GPU Environments

For the non-gpu environments use the following Dockerfile

USER root
RUN pip install torchmetrics 
RUN pip install pytorch_lightning 
RUN pip install install lightning-bolts 
# Install Tensorflow
RUN pip install tensorflow --user

# Install Jupyter-ai 
RUN pip install 'jupyter-ai>=1.0,<2.0' --user

#python libraries
RUN pip install pytorch-lightning
RUN pip install torchmetrics
RUN pip install pytorch-lightning-bolts
RUN pip install git+https://github.com/PytorchLightning/lightning-bolts.git@master --upgrade
RUN pip install torchvision
RUN pip install GPUtil
RUN pip install torch torchvision tensortrade
#RUN pip install wandb
RUN pip install tblib
RUN pip install pandas==1.4.1
RUN pip install nvidia-ml-py3
RUN pip install numpyro pystan
USER ubuntu