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Merge pull request #119 from NVIDIA/dev2.9
Dev2.9
2 parents 406445c + 1b2e055 commit b8625cf

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README.md

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@@ -51,7 +51,7 @@ cd data-science-stack
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```
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On Windows Subsystem for Linux (WSL):
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_Note: This functionality is alpha only until WSL v2 becomes production ready_
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_Note: This functionality is alpha only (and containers only) until WSL v2 becomes production ready_
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Follow the [install instructions](https://docs.nvidia.com/cuda/wsl-user-guide/index.html) to install WSL v2 with CUDA support.
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Then, create a a Ubuntu or RHEL VM, open a terminal, and follow OS-specific instructions above.
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with the Jupyter notebook running at <http://localhost:8888/>
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Control-C to exit.
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To mount data or code into your contianer, see
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To mount data or code into your container, see
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[How do I mount data into containers?](#How-do-i-mount-data-into-containers)
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below.
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<https://access.redhat.com/solutions/253273>
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### Windows Subsystem for Linux (WSL v2)
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_Note: This functionality is alpha only until WSL v2 becomes production ready_
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_Note: This functionality is alpha only (and containers only) until WSL v2 becomes production ready_
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Follow the [install instructions](https://docs.nvidia.com/cuda/wsl-user-guide/index.html) for WSL v2 with CUDA support.
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Then, create a a Ubuntu or RHEL VM, open a terminal, and follow OS-specific instructions above.
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Then, create a Ubuntu or RHEL VM, open a terminal, and follow OS-specific instructions above.
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_Note: WSL v2 currently requires CUDA 11.0, while data science stack 2.9.0 is based on CUDA 11.2. Therefore, WSL v2 is supported via containers only.
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## Installing the NVIDIA GPU Driver
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data-science-stack

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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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# Global Paramaters
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STACK_VERSION=2.8.0
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NOTEBOOKS_VERSION=0.18
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STACK_VERSION=2.9.0
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NOTEBOOKS_VERSION=21.06
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MIN_DRIVER=460.39
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MIN_CUDA=11.0.228
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MIN_DRIVER=465.27
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MIN_CUDA=11.2.152
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MIN_DOCKER=20.10.3
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MIN_CONDA=4.8.3
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sudo add-apt-repository -y ppa:graphics-drivers/ppa
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sudo apt-get -y update
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sudo apt-get -y upgrade
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sudo apt-get -y install nvidia-driver-460
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sudo apt-get -y install nvidia-driver-465
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sudo apt-get -y autoremove
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REBOOT=1
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elif [ $OS_FLAVOR$OS_RELEASE_MAJOR = "rhel8" ]; then
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sudo dnf config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
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sudo dnf install -y kernel-devel-$(uname -r) kernel-headers-$(uname -r)
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sudo dnf -y module install nvidia-driver:460-dkms
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sudo dnf -y module install nvidia-driver:465-dkms
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# REBOOT not necessary
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else
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nvlog "Automated NVIDIA driver install on $OS_FLAVOR $OS_RELEASE_FULL is not supported."
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detect_cuda () {
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if [ -f /usr/local/cuda/version.txt ]; then
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if [ -f /usr/local/cuda/version.json ]; then
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CUDA_VER=$(jq -r ".cuda.version" /usr/local/cuda/version.json 2>/dev/null)
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elif [ -f /usr/local/cuda/version.txt ]; then
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CUDA_VER=$(cat /usr/local/cuda/version.txt | awk '{ print $3 }' 2> /dev/null)
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if [ $? -ne 0 ]; then
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CUDA_VER=0
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sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
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sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
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sudo apt-get update
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sudo apt-get -y install cuda-toolkit-11-0
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sudo apt-get -y install cuda-toolkit-11-2
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else
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curl https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin \
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-o cuda.pin
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sudo mv cuda.pin /etc/apt/preferences.d/cuda-repository-pin-600
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sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub
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sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
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sudo apt-get update
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sudo apt-get -y install cuda-toolkit-11-0
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sudo apt-get -y install cuda-toolkit-11-2
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fi
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else
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if [ $OS_FLAVOR$OS_RELEASE_MAJOR = "rhel7" ]; then
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sudo yum-config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo
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sudo yum clean all
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sudo yum install -y cuda-toolkit-11-0
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sudo yum install -y cuda-toolkit-11-2
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else
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sudo dnf config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
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sudo dnf clean all
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sudo dnf -y install cuda-toolkit-11-0
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sudo dnf -y install cuda-toolkit-11-2
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fi
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fi
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set +e
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echo "export PATH=/usr/local/cuda/bin/:\$PATH # DATA-SCIENCE-STACK-ADDED" >> ${HOME}/.bashrc
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echo "export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:/lib:\$LD_LIBRARY_PATH # DATA-SCIENCE-STACK-ADDED" >> ${HOME}/.bashrc
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echo "export LD_LIBRARY_PATH=/usr/local/cuda-11.2/lib64:/lib:\$LD_LIBRARY_PATH # DATA-SCIENCE-STACK-ADDED" >> ${HOME}/.bashrc
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source ${HOME}/.bashrc
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nvlog "END Installing CUDA"
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sudo yum install -y \
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docker-ce \
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nvidia-container-toolkit
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nvidia-container-toolkit \
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iptables
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sudo systemctl enable docker
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if [ "$WSL" = true ]
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then
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jupyter labextension install -y --clean \
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@jupyter-widgets/jupyterlab-manager \
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jupyter-threejs \
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dask-labextension \
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jupyterlab-nvdashboard
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dask-labextension
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nvlog "END Setup Conda Env"
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nvlog "Next you can run: ${SCRIPT_NAME} run-jupyter"
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run-notebook)
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if [ $# -ne 2 ]; then
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nvlog "Usage: ${SCRIPT_NAME} run-notebook <notebook-file>"
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else
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if [ ! -f "$2" ]; then
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nvlog "Notebook $2 not found"
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else
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if [ $CONDA_VER != "0" ];then
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run_notebook $2
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else
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nvlog "Conda not detected. Please run create-conda-env first"
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fi
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fi
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fi;
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run_notebook $2
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;;
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run-notebook-dir)

data-science-stack.Dockerfile

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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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ARG DOCKER_REPO=nvcr.io/nvidia/cuda
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ARG CUDA_VERSION=11.0
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ARG CUDA_VERSION=11.2.2
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ARG OS_FLAVOR=devel-ubuntu20.04
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FROM ${DOCKER_REPO}:${CUDA_VERSION}-${OS_FLAVOR}
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# Create Base environment
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ARG STACK_VERSION=2.8.0
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ARG STACK_VERSION=2.9.0
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ARG CONDA_VERSION=4.8.3
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ARG NOTEBOOKS_VERSION=0.18
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ARG NOTEBOOKS_VERSION=21.06
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ENV CONDA_ROOT=/conda
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ENV NOTEBOOKS_DIR=/notebooks
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jupyter labextension install -y --clean \
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@jupyter-widgets/jupyterlab-manager \
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jupyter-threejs \
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dask-labextension \
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jupyterlab-nvdashboard' \
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dask-labextension' \
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&& ${CONDA_ROOT}/bin/conda clean -afy \
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&& find ${CONDA_ROOT} -follow -type f -name '*.pyc' -delete \
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&& find ${CONDA_ROOT} -follow -type f -name '*.js.map' -delete

environments/README.md

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## Problems while pinning
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Satisfying the constraings in your .env file may not be possible, in which
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Satisfying the constraints in your .env file may not be possible, in which
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case the pin command will fail.
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To make this (very common) problem easier to solve, the command to generate
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The file is part of the command used by the `data-science-stack` script to
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generate pinned configurations.
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The file should contain only the channels and packages, one per line.
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Packages with version restictions should have double quotes around them.
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Packages with version restrictions should have double quotes around them.
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-c channel-1
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-c channel-2

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