artifact-core

artifact-core constitutes the foundation of Artifact-ML.

It provides a declarative interface for the computation of validation artifacts in ML experiments.

Its objective is to enable reusable validation workflows by providing the tools to trigger artifacts by name---with zero adapter code.

In line with our design philosophy, achieving this sets the stage for Artifact-ML’s broader objective: the elimination of imperative glue code in ML experiments at large.

artifact-core stands alongside:

• artifact-experiment: experiment orchestration extension for building reusable validation workflows with integrated tracking.
• artifact-torch: PyTorch integration for building reusable deep-learning workflows declaratively.

Topics

• Getting Started - quick installation instructions.
• User Guide — general user instructions.
• Domain Toolkits
  • Table Comparison Toolkit — guide to the tabular synthesis validation toolkit.
  • Binary Classification Toolkit — guide to the binary classification validation toolkit.
• Architecture — high level framework architecture.
• Core Entities — framework core entity specification.
• Development Guide — low-level development guidelines.
• Contributing Artifacts — development guide for new validation artifacts.