sameli/glearn repository overview

⁠glearn

An Ubuntu 20.04 image with glearn⁠ python package built with NVIDIA CUDA Toolkit 11.4 support.

⁠Download

docker pull sameli/glearn

⁠Usage

• To run the container and open python interpreter directly at startup:

  docker run -it sameli/glearn
  

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  This also imports glearn package automatically.

• To run the container and open ipython interpreter directly at startup:

  docker run -it sameli/glearn ipython
  

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  This also imports glearn package automatically.

• To open bash shell only:

  docker run -it --entrypoint /bin/bash sameli/glearn
  

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• To mount the host directory /home/user/project on the docker's container directory /root, use:

  docker run -it -v /home/user/project:/root sameli/glearn
  

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⁠Using Host's GPU

To access host's GPU device from the container, install Nvidia Container Toolkit as follows.

1. Add the package to the repository:

   distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
   curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
   curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
   

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2. Install nvidia-contaner-toolkit by:

   sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
   

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3. Restart docker to be able to use it:

   sudo systemctl restart docker
   

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To use host's GPU, add --gpus all to any of the docker commands given before, such as:

docker run --gpus all -it sameli/glearn

To check the host's Nvidia driver version, CUDA runtime library version, and list of available GPU devices, run nvida-smi command, such as by:

docker run --gpus all sameli/glearn nvidia-smi

⁠Installed programs

• CUDA 11: /usr/local/cuda
• Python 3.9: /usr/bin/python3
• Python interpreters: ipython, jupyter
• Editor: vim
• LaTeX (texlive)