Amazon SageMaker
Developer Guide

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How Amazon SageMaker Runs Your Training Image

To configure a Docker container to run as an executable, use an ENTRYPOINT instruction in a Dockerfile. Note the following:

  • For model training, Amazon SageMaker runs the container as follows:

    docker run image train

    Amazon SageMaker overrides any default CMD statement in a container by specifying the train argument after the image name. The train argument also overrides arguments that you provide using CMD in the Dockerfile.


  • Use the exec form of the ENTRYPOINT instruction:

    ENTRYPOINT ["executable", "param1", "param2", ...]

    For example:

    ENTRYPOINT ["python", ""]

    The exec form of the ENTRYPOINT instruction starts the executable directly, not as a child of /bin/sh. This enables it to receive signals like SIGTERM and SIGKILL from Amazon SageMaker APIs. Note the following:


    • The CreateTrainingJob API has a stopping condition that directs Amazon SageMaker to stop model training after a specific time.


    • The StopTrainingJob API issues the equivalent of the docker stop, with a 2 minute timeout, command to gracefully stop the specified container:

      docker stop -t120

      The command attempts to stop the running container by sending a SIGTERM signal. After the 2 minute timeout, SIGKILL is sent and the containers are forcibly stopped. If the container handles the SIGTERM gracefully and exits within 120 seconds from receiving it, no SIGKILL is sent.


    If you want access to the intermediate model artifacts after Amazon SageMaker stops the training, add code to handle saving artifacts in your SIGTERM handler.

  • If you plan to use GPU devices for model training, make sure that your containers are nvidia-docker compatible. Only the CUDA toolkit should be included on containers; don't bundle NVIDIA drivers with the image. For more information about nvidia-docker, see NVIDIA/nvidia-docker.

  • You can't use the tini initializer as your entry point in Amazon SageMaker containers because it gets confused by the train and serve arguments.

  • /opt/ml and all sub-directories are reserved by Amazon SageMaker training. When building your algorithm’s docker image, please ensure you don't place any data required by your algorithm under them as the data may no longer be visible during training.