A unified interface for logging and tracking provenance information in machine learning experiments, both on distributed as well as large scale experiments.
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This library is part of the yProv suite, and provides a unified interface for logging and tracking provenance information in machine learning experiments, both on distributed as well as large scale experiments.
It allows users to create provenance graphs from the logged information, and save all metrics and parameters to json format.
The image shown above has been generated from the example program provided in the example directory.
An experiment is a collection of runs. Each run is a single execution of a machine learning model.
By changing the experiment_name parameter in the start_run function, the user can create a new experiment.
All artifacts and metrics logged during the execution of the experiment will be saved in the directory specified by the experiment ID.
Several runs can be executed in the same experiment. All runs will be saved in the same directory (according to the specific experiment name and ID).
For detailed information, please refer to the Documentation