Metadata-Version: 2.4
Name: cotorch
Version: 0.0.1
Summary: An experimental, early-stage machine learning framework built from first principles.
License-Expression: MIT
License-File: LICENSE
Classifier: Development Status :: 1 - Planning
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Project-URL: Repository, https://github.com/cotenthusiast/CoTorch
Requires-Python: >=3.12
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: hypothesis; extra == "dev"
Requires-Dist: numpy; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: build; extra == "dev"
Requires-Dist: twine; extra == "dev"
Description-Content-Type: text/markdown

# CoTorch

**A machine learning framework built from first principles.**

CoTorch is an experimental, open-source machine learning framework focused on understanding and implementing the foundations of modern ML.

The project explores the full stack, from tensor operations and automatic differentiation to neural networks, optimisation algorithms, and model training.

Rather than competing with established frameworks, CoTorch serves as a space for hands-on implementation, experimentation, and exploration of machine learning systems.

## Areas of exploration

- **Tensor computation:** Multidimensional arrays, broadcasting, memory layouts, and numerical operations.
- **Automatic differentiation:** Computational graphs, reverse-mode autodiff, and gradient computation.
- **Neural networks:** Layers, activation functions, loss functions, and optimisation algorithms.
- **Machine learning algorithms:** Implementations of classical and modern ML methods.
- **Performance:** CPU/GPU computation, numerical efficiency, and benchmarking.

## Development

CoTorch is in its early stages of development.

The initial focus is on building the core tensor and numerical computation primitives, followed by automatic differentiation and neural network training.

The project is exploratory, and its scope will evolve as new components are implemented.

## Related projects

- [CoInfer](https://github.com/cotenthusiast/CoInfer): An experimental ML inference engine exploring model execution and optimisation.
- [CoHarness](https://github.com/cotenthusiast/CoHarness): An experimental AI coding-agent harness exploring model integration, tool calling, context management, and autonomous execution.

## License

MIT License.
