Metadata-Version: 2.4
Name: torch-batteries
Version: 0.3.0
Summary: A lightweight Python package that supplies batteries-included abstractions for PyTorch workflows
Author: Michal Szczygiel, Arkadiusz Paterak, Antoni Zięciak
License-Expression: MIT
Project-URL: Homepage, https://github.com/michalszc/torch-batteries
Project-URL: Repository, https://github.com/michalszc/torch-batteries
Project-URL: Issues, https://github.com/michalszc/torch-batteries/issues
Keywords: pytorch,machine learning,deep learning,training,evaluation
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.9.0
Requires-Dist: tqdm>=4.67.1
Provides-Extra: example
Requires-Dist: matplotlib>=3.10.7; extra == "example"
Requires-Dist: numpy>=2.3.4; extra == "example"
Requires-Dist: scikit-learn>=1.7.2; extra == "example"
Dynamic: license-file

# torch-batteries

<p align="center">
   <a href="https://pypi.org/project/torch-batteries/"><img src="https://img.shields.io/pypi/v/torch-batteries" alt="PyPI version"></a>
   <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License: MIT"></a>
</p>

<p align="center">
   <img src="https://raw.githubusercontent.com/michalszc/torch-batteries/refs/heads/master/assets/logo.png" alt="torch-batteries" style="max-height:512px; height:auto;" />
   <br/>
   <em>Image generated by AI</em>
</p>

A lightweight Python package that supplies batteries-included abstractions for:
- Data loading pipelines
- Model training loops
- Evaluation workflows
- Metrics computation
- Seamless Weights & Biases tracking

Designed to reduce boilerplate and standardize experiment code.

## Installation

```bash
pip install torch-batteries
```

## Examples

Explore practical examples demonstrating torch-batteries features:

| Example | Description | Notebook | Colab |
|---------|-------------|----------|-------|
| **Function Fitting with MLP** | Train a neural network to approximate a polynomial function using the event-driven training approach | [function_fitting.ipynb](notebooks/function_fitting.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/michalszc/torch-batteries/blob/master/notebooks/function_fitting.ipynb) |
