artifact-torch

artifact-torch provides Pytorch integration for Artifact-ML.

It offers the tools to build reusable deep learning workflows declaratively.

It handles all aspects of the training loop aside from model architecture and data pipelines, abstracting away engineering complexity to let researchers focus on architectural innovation.

It stands alongside:

• artifact-core: a declarative interface for the computation of validation artifacts in ML experiments.
• artifact-experiment: experiment orchestration extension for building reusable validation workflows with integrated tracking.

Topics

• Getting Started - quick installation instructions.
• User Guide — general user instructions.
• Architecture — high level framework architecture.
• Core Entities — framework core entity specification.
• Development Guide — low-level development guidelines.