Reproducible machine learning pipelines using mlflow.
Project description
mlf-core
Fully GPU deterministic machine learning project templates using MLflow.
Free software: Apache2.0
Documentation: https://mlf-core.readthedocs.io.
Features
Jumpstart your machine learning project with fully fledged, multi GPU enabled mlflow project templates
Pytorch, Tensorflow, XGBoost supported
mlflow templates are fully GPU deterministic with system-intelligence
Conda and Docker support out of the box
Credits
Primary idea and main development by Lukas Heumos. This package was created with cookietemple based on a modified audreyr/cookiecutter-pypackage project template using Cookiecutter.
Changelog
This project adheres to Semantic Versioning.
1.1.0 (2020-08-14)
Added
Publish Docker workflow. Publishes to Github Packages per default, but can be configured.
Linting function, which checks mlflow-pytorch for any used atomic_add functions.
system-intelligence 1.2.2 -> 1.2.3
Support for both, MLF-CORE TODO: and TODO MLF-CORE: statements
Fixed
Default project version from 0.1.0 to 0.1.0-SNAPSHOT.
Outdated screenshots
Nightly versions now warn instead of wrongly complaining about outdated versions.
Sync actor, but not yet completely for organizations
A LOT of documentation
Now using project_slug_no_hyphen to facilitate the creation of repositories with - characters.
Removed boston dataset from XGBoost and XGBoost_dask
Renamed all parameters to use hyphens instead of underscores
Dependencies
Deprecated
1.0.1 (2020-08-11)
Added
Fixed
Sync workflow now uses the correct secret
Dependencies
Deprecated
1.0.0 (2020-08-11)
Added
Created the project using cookietemple
Added all major commands: create, list, info, lint, sync, bump-version, config, upgrade
Added mlflow-pytorch, mlflow-tensorflow, mlflow-xgboost, mlflow-xgboost_dask templates
Fixed
Dependencies
Deprecated
Release history Release notifications | RSS feed
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