How should the ML specialist package the Docker container so that SageMaker can launch the training correctly?
Specify the server argument in the ENTRYPOINT instruction in the Dockerfile.
Specify the training program in the ENTRYPOINT instruction in the Dockerfile.
Include the path to the training data in the docker build command when packaging the container.
Use a COPY instruction in the Dockerfile to copy the training program to the /opt/ml/train directory.
Explanations:
Specifying the server argument in the ENTRYPOINT instruction is not required for SageMaker to launch training correctly. SageMaker expects a specific format for the training command rather than a server argument.
Specifying the training program in the ENTRYPOINT instruction allows SageMaker to know which script to execute when the container starts. This is necessary for SageMaker to run the training process.
Including the path to the training data in the docker build command is not relevant. SageMaker handles data input and environment configuration separately, and the training data path should be managed by SageMaker when the training job is launched.
While using a COPY instruction to copy the training program to the container is necessary, it should be copied to the correct directory (typically /opt/ml/code for SageMaker) and not /opt/ml/train. The training script needs to be accessible in the right location for SageMaker to execute it properly.
From what I’ve seen, the answer is:
Specify the training program in the ENTRYPOINT instruction in the Dockerfile.