aws / aws/sagemaker-python-sdk
Support `experiment_config` in HyperparameterTuner for unified lineage across training and tuning phases
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component: experiments
type: feature request
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描述
Is there a way to pass experiment_config to HyperparameterTuner to have unified lineage tracking across all phases of the model?
What is the recommended way to track end-to-end lineage across training and tuning phases? Should we just manually create a Tracker and log the hyperparameters, objective metrics, and training result metrics?
I'd like to see symmetry between the Estimator and HyperparameterTuner APIs - and pass experiment_config to both.
Any guidance would be appreciated.
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