When running the hyperdrive step, I would like to get the hyper parameters that were selected for the best model and export them to use in a subsequent model. How would I got about doing that? I saw a method get_hyperparameters but from what I can tell that just gets all child runs. I am essentially wanting to use the same model but change alpha levels.
Hello.
I think below is what you are looking for. Could you please take a look?
Once all of the hyperparameter tuning runs have completed, identify the best performing configuration and hyperparameter values:
best_run = hyperdrive_run.get_best_run_by_primary_metric()
best_run_metrics = best_run.get_metrics()
parameter_values = best_run.get_details()['runDefinition']['Arguments']
print('Best Run Id: ', best_run.id)
print('\n Accuracy:', best_run_metrics['accuracy'])
print('\n learning rate:',parameter_values[3])
print('\n keep probability:',parameter_values[5])
print('\n batch size:',parameter_values[7])
Regards,
Yutong
Thank you. I will give this a look and report back if any issues arise.
@AdrianCartier-4813 Could you please accept the answer to help the community? Thanks!!!
Regards,
Yutong
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