awslabs/gluonts
GluonTSFramework Hyper Parameter Optimization Support
Aperta
#617 aperta il 11 feb 2020
enhancementhelp wanted
Metriche repository
- Star
- (3888 stelle)
- Metriche merge PR
- (Merge medio 26g 17h) (4 PR mergiate in 30 g)
Descrizione
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/