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Description
Feature request
We already provide example shell commands to invoke and test training scripts for all variants of the migration challenge. For example from SKLearn:
!python3 src/main.py \
--train ./data/train \
--test ./data/test \
--model_dir ./data/model \
--class_names {class_names_str} \
--n_estimators=100 \
--min_samples_leaf=3
Since this is the recommended debugging workflow, we should also demonstrate it in the script mode walkthroughs by adding equivalent commands in the 'SageMaker' variants of these notebooks - before the Estimator gets created.
This will help these notebooks illustrate the process/workflow of translating from in-notebook to notebook+job, better than just showing the final result.
Background
Today, we use in-notebook shell commands as the recommended script debugging workflow for the migration challenge - because our options are somewhat constrained for a workshop:
- SageMaker Warm Pools requires a quota increase to enable
- SageMaker Local Mode isn't natively supported in Studio at this time
- SageMaker SSH Helper has a bit of a learning curve for any data scientists not already familiar with online SSH-based debugging (and some setup required - although we could probably automate that).
We talk about these other options in the post-challenge wrap-up, but don't want to confuse the issue by introducing them up-front in the code.