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yProv4ML

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.

Data Model

Data Model

Example

Example

The image shown above has been generated from the example program provided in the example directory.

Metrics Visualization

Loss and GPU Usage

Emission Rate

Experiments and Runs

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).

Documentation

For detailed information, please refer to the Documentation

Contributors

Metadata

Release files for yprov4ml 3.2.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for yprov4ml 3.2.4
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yprov4ml-3.2.4.tar.gz 68.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for yprov4ml 3.2.4
File Interpreter ABI Platform
yprov4ml-3.2.4-py3-none-any.whl Python 3 none any Details

Total release size: 132.8 kB

Release files / yprov4ml-3.2.4.tar.gz

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