Skip to main content
HPCI Lab Logo

yProv4ML

A unified interface for logging and tracking provenance information in machine learning experiments, both on distributed as well as large scale experiments.
Explore the docs »

Report Bug · Request Feature


Contributors Forks Stars Issues GPLv3 License

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 3.2.7

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 3.2.7
File Size Uploaded
yprov4ml-3.2.7.tar.gz 75.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for yprov4ml 3.2.7
File Interpreter ABI Platform
yprov4ml-3.2.7-py3-none-any.whl Python 3 none any Details

Total release size: 146.2 kB

Release files / yprov4ml-3.2.7.tar.gz

Download URL yprov4ml-3.2.7.tar.gz
Size 75.3 kB
Tags Source
SHA-256 checksum
How to use checksums
4f784d292c6d354a7d9d5b805cc3f233b43f6c2d3415423f3493220feb578316
BLAKE2b-256 checksum
How to use checksums
84120bdca67b21599478ee100d28cf0db48ff0edc8d53e7e20c3b2c98e935456
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.11

Release files / yprov4ml-3.2.7-py3-none-any.whl

Download URL yprov4ml-3.2.7-py3-none-any.whl
Size 70.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
8635b52fd7628dd8a290e7db4f799ddd4f9480d1fc29f9ddd0266145fd8b4d98
BLAKE2b-256 checksum
How to use checksums
5c12f7fce56e6c7d597ca6d98d2b4f4ac926536b81aa228604e2cc318f9d1139
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.11

Release history Release notifications | RSS feed

This release

3.2.7 This release

2 release files

3.2.6

2 release files

3.2.5

2 release files

3.2.4

2 release files

3.2.3

2 release files

3.2.2

2 release files

3.2.1

2 release files

3.2.0

2 release files

2.0.10

2 release files

2.0.9

2 release files

2.0.8

2 release files

2.0.7

2 release files

2.0.6

2 release files

2.0.5

2 release files

2.0.4

2 release files

2.0.3

2 release files

2.0.2

2 release files

2.0.1

2 release files

2.0.0

2 release files

1.0.0

2 release files

0.1.19

2 release files

0.1.18

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page