Artifact-ML PyTorch integration: declarative builder toolkit for reusable deep learning workflows.
Project description
⚙️ artifact-torch
Declarative builder toolkit for reusable deep learning workflows.
📋 Overview
artifact-torch provides PyTorch integration for Artifact-ML.
It offers the tools to build reusable deep learning workflows declaratiely.
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.
🚀 Installation
Clone the Artifact-ML monorepo by running:
git clone https://github.com/vasileios-ektor-papoulias/artifact-ml.git
Install the artifact-torch package by running:
cd artifact-ml/artifact-torch
poetry install
📚 Documentation
Documentation for artifact-torch is available at artifact-torch docs.
🤝 Contributing
Contributions are welcome!
Please consult our contribution guidelines document.
📄 License
This project is licensed under the MIT License.
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