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To answer your questions:
The CLI approach to defining SageMaker pipelines is a fully supported functionality. Some potential drawbacks compared to the Python SDK are that it may require more manual steps and does not provide programmatic access.
To specify a Python file for each pipeline step, you define the "Container" property for each step in the JSON pipeline definition file. The file path would be specified as part of the container image configuration.
For example:
"Container": { "Image": "myimage:latest", "Command": [ "python_file.py" ] } To create the environment for each step, you define the "Environment" property. This specifies things like the Docker image to use which contains the necessary dependencies and configuration.
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Thanks for the useful information. Regarding the container, does this mean that I have to manually create a Docker image and include whatever libraries my pipeline step requires in addition to the Python file? I was hoping that there's a way to abstract / simplify that step.
Can you please give an example or two of how using the CLI would require more manual steps compared to the Python SDK? One thing I find with using Python for MLOps commands is that it's verbose and also doesn't allow me to easily differentiate between the source Python code and the cloud (MLOps) code.