Running PyTorch

Before running the container, use the docker pull command to ensure an up-to-date image is installed. Once the pull is complete, you can run the container image. This is because nvidia-docker ensures that drivers that match the host are used and configured for the container. Without nvidia-docker, you are likely to get an error when trying to run the container.

  1. Issue the command for the applicable release of the container that you want. The following command assumes you want to pull the latest container.
    docker pull
  2. Open a command prompt and paste the pull command. The pulling of the container image begins. Ensure the pull completes successfully before proceeding to the next step.
  3. Run the container image. To run the container, choose interactive mode or non-interactive mode.
    1. Interactive mode: Open a command prompt and issue:
      nvidia-docker run -it --rm -v local_dir:container_dir<xx.xx>-py3
    2. Non-interactive mode: Open a command prompt and issue:
      nvidia-docker run --rm -v local_dir:container_dir<xx.xx>-py3 <command>

      • -it means interactive
      • --rm means delete the container when finished
      • –v means mount directory
      • local_dir is the directory or file from your host system (absolute path) that you want to access from inside your container. For example, the local_dir in the following path is /home/jsmith/data/mnist.
        -v /home/jsmith/data/mnist:/data/mnist

        If you are inside the container, for example, ls /data/mnist, you will see the same files as if you issued the ls /home/jsmith/data/mnist command from outside the container.

      • container_dir is the target directory when you are inside your container. For example, /data/mnist is the target directory in the example:
        -v /home/jsmith/data/mnist:/data/mnist
      • <xx.xx> is the container version. For example, 19.01.
      • <command> is the command you want to run in the image.
      Note: If you use multiprocessing for multi-threaded data loaders, the default shared memory segment size that the container runs with may not be enough. Therefore, you should increase the shared memory size by issuing either:
      --shm-size=<requested memory size>
      in the command line to
      nvidia-docker run

      You might want to pull in data and model descriptions from locations outside the container for use by PyTorch or save results to locations outside the container. To accomplish this, the easiest method is to mount one or more host directories as Docker® data volumes.

      You have pulled the latest files and run the container image.
  4. See /workspace/ inside the container for information on customizing your PyTorch image.
    For more information about PyTorch, including tutorials, documentation, and examples, see: