awslabs/gluonts
Auf GitHub ansehenGluonTSFramework Hyper Parameter Optimization Support
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#617 geöffnet am 11. Feb. 2020
enhancementhelp wanted
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Beschreibung
Description
Implement Hyper Parameter Optimisation (HPO) Support in GluonTSFramework. There are already multiple comments in the code of how to go about it:
# HPO implementation sketch:
# > Example HPO of model: MODEL_HPM:Trainer:batch_size:64
# > Now construct nested dict from MODEL_HPM hyperparameters
# > Load the serialized model as a dict
# > Update the model dict with the nested dict from the MODEL_HPMs
# with dict.update(...)
# > Write this new dict back to a s3 as a .json file like before
This is important to support:
- The HyperparameterTuner: https://sagemaker.readthedocs.io/en/stable/tuner.html
- and SageMaker Experiments: https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/