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mlf-core: a framework for deterministic machine learning

mlf-core provides CPU and GPU deterministic machine learning templates based on MLflow, Conda, Docker and a strong Github integration. Templates are available for PyTorch, TensorFlow and XGBoost. A custom linter ensures that projects stay deterministic in all phases of development and deployment.

Start your journey with mlf-core by installing it via $ pip install mlf-core.

See Installation.

See a mlf-core project in action.

Configure mlf-core to get started.

See Configuring mlf-core

List all available mlf-core templates.

See Listing all templates.

Get detailed information on a mlf-core template.

See Get detailed template information.

Kickstart your deterministic machine laerning project with one of mlf-core's templates in no time.

See Create a project.

Use advanced linting to ensure your project always adheres to mlf-core's standards and stays deterministic.

See Linting your project

Bump your project version across several files.

See Bumping the version of an existing project.

Sync your project with the latest mlf-core release to get the latest template features.

See Syncing a project.

Check whether you are using the latest mlf-core version and update automatically to benefit from the latest features.

See https://mlf_core.readthedocs.io/en/latest/upgrade.html.

Primary idea and main development by Lukas Heumos. mlf-core is inspired by nf-core. This package was created with cookietemple based on a modified audreyr/cookiecutter-pypackage project template using cookiecutter.