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MLOps tools for research and development.

Project description

MLOpus

Test Coverage

A collection of MLOps tools for AI/ML/DS research and development.

Main features:

  • Agnostic experiment tracking and model registry:

    • Compatible with any "MLflow-like" provider through plugins.
    • Search entities in MongoDB Query Language with predicate push-down to the MLflow provider.
    • Local cache for artifacts and entity metadata.
    • Offline mode to work with local cache only.
    • Support for nested tags/params/metrics and JSON-encoded tags/params for non-scalar types.
    • Not dependent on env vars, global vars or a single global active run.
  • Artifact Schemas:

    • Packaging framework for models and datasets.
    • Can be used with or without MLflow and/or Kedro.
    • Schemas can be registered by alias at the experiment, run, model or model version.
    • Artifacts catalog for type-safe, configuration-based artifact loading/downloading in serving applications.
  • Extended Kedro support:

    • Dynamic pipeline and hook evaluation with direct access to the Kedro config loader.
    • Artifact Schemas can be used in the Kedro datasets catalog.
    • Extend the Kedro CLI with project-specific options, callbacks and param modifiers.
    • Artifacts hook to set up pipeline inputs and/or collect outputs (optionally schema-aware).
    • Highly customizable MLflow tracker hook for storing any pipeline information in experiment runs.

Check the tutorials for a friendly walkthrough of (almost) everything you can do with MLOpus.

Have a look at the architecture guide for an overview of how these and other features work.

A minimal API reference is also available here.

Installation

Recommended software:

  • Rclone CLI (required for artifact transfer from/to cloud storage)

Optional extras:

  • mlflow: Enables support for the default MLflow plugin, which handles communication with open-source MLflow servers.
  • search: Enables searching entities with MongoDB query syntax
  • kedro: Enables Kedro tools (e.g.: hooks, datasets, CLI extensions, etc)

Using pip:

pip install mlopus[mlflow,kedro,search]

Using Poetry:

poetry add mlopus --extras "mlflow,kedro,search"

Using UV:

uv add mlopus --extra mlflow --extra kedro  --extra search

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