artifact-torch 0.1.4
Artifact-ML PyTorch integration: declarative builder toolkit for reusable deep learning workflows.
License: MIT
Requires Python: <3.14,>=3.11

# ⚙️ artifact-torch

> Declarative builder toolkit for reusable deep learning workflows.

<p align="center">
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</p>

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---

## 📋 Overview

`artifact-torch` provides PyTorch integration for [Artifact-ML](https://github.com/vasileios-ektor-papoulias/artifact-ml).

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

It stands alongside:

- [`artifact-core`](https://github.com/vasileios-ektor-papoulias/artifact-ml/tree/main/artifact-core): a declarative interface for the computation of validation artifacts in ML experiments.
- [`artifact-experiment`](https://github.com/vasileios-ektor-papoulias/artifact-ml/tree/main/artifact-experiment): experiment orchestration extension for building reusable validation workflows with integrated tracking.

## 🚀 Installation

Clone the [**Artifact-ML monorepo**](https://github.com/vasileios-ektor-papoulias/artifact-ml/tree/main) by running:

```bash
git clone https://github.com/vasileios-ektor-papoulias/artifact-ml.git
```

Install the `artifact-torch` package by running:

```bash
cd artifact-ml/artifact-torch

poetry install
```

## 📚 Documentation

Documentation for `artifact-torch` is available at [**artifact-torch docs**](https://artifact-ml.readthedocs.io/en/latest/artifact-torch).

## 🤝 Contributing

Contributions are welcome!

Please consult our [**contribution guidelines document**](https://artifact-ml.readthedocs.io/en/latest/Development/contributing).

## 📄 License

This project is licensed under the [MIT License](https://img.shields.io/github/license/vasileios-ektor-papoulias/artifact-ml).