Automation of Python Scripts in AWS Sagemaker

There are several ways to automate Python code execution within Amazon SageMaker, including the scenario you described with notebook instances and lifecycle configurations. Let's address your questions and provide some clarity on automation options:

1. Regarding StepFunctions and "StartNotebookInstance": While there is a "StartNotebookInstance" action, you're correct that it doesn't directly run notebook code. This action is typically used to ensure a notebook instance is running before other operations are performed, but it doesn't execute the notebook content itself.

2. Automating Python code in notebook instances: Yes, you can automate the execution of Python code that you've written within a notebook instance, even with lifecycle configurations for additional package installation. Here are a few approaches:

   a. SageMaker Pipelines: This is a purpose-built solution for ML workflows. You can use the @step decorator to easily integrate your existing ML code into a SageMaker Pipeline. This allows you to automate the execution of your ETL processes and DeepAR model training without significant code changes.

   b. Notebook Jobs: SageMaker provides a feature called "Notebook Jobs" which allows you to schedule and run notebooks as jobs. This can be useful if your code is already in notebook format.

   c. Processing Jobs: If your code is in script format, you can use SageMaker Processing Jobs to run your Python scripts in a managed environment.

3. Lifecycle Configurations: These are indeed useful for installing additional packages and customizing your notebook environment. You can combine lifecycle configurations with the above automation methods to ensure your required packages are available.

4. Custom Docker Images: While you can use custom Docker images for more complex environments or dependencies, it's not strictly necessary for most automation tasks. SageMaker provides pre-built containers for many common ML frameworks, and you can extend these with lifecycle configurations in many cases.

5. Code Pipelines: You mentioned Code Pipelines, which are being deprecated. Instead, consider using SageMaker Pipelines, which is specifically designed for ML workflows and offers better integration with SageMaker features.

In summary, SageMaker Pipelines is likely the most straightforward and purpose-built solution for automating ML workflows, including ETL processes and model training. It allows you to reuse your existing code with minimal changes and provides a serverless, scalable execution environment. Combined with lifecycle configurations for package management, this approach should meet your automation needs without requiring you to build custom Docker images in most cases.
Sources
Workflows for Machine Learning - Amazon SageMaker Pipelines
Customization of a SageMaker notebook instance using an LCC script - Amazon SageMaker AI
Step 2. Create the runtime scripts - AWS Prescriptive Guidance