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aws / sagemaker-python-sdk

A library for training and deploying machine learning models on Amazon SageMaker
https://sagemaker.readthedocs.io/
Apache License 2.0
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Support `experiment_config` in HyperparameterTuner for unified lineage across training and tuning phases #1503

Open cfregly opened 4 years ago

cfregly commented 4 years ago

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.

chuyang-deng commented 4 years ago

Hi @cfregly. Thanks for using SageMaker! Unfortunately Hyperparameter Tuning currently does not support ExperimentConfig in the service API yet.

However, once we got an update on the service side, we will update SageMaker Python SDK as well.

cfregly commented 4 years ago

Any update?

leninkumar-sv-tiger commented 4 years ago

Any update on this ?

krishnashanker-amt commented 3 years ago

Would love it if this could be implemented.

paulm-1 commented 3 years ago

Same

pdifranc commented 2 years ago

Would be really useful to have it implemented rather then manually associate the training jobs after the HyperparameterTuner completes its job