SlowTorch is another personal pet-project of mine where I tried and implemented the basic and bare-bones functionality of PyTorch just using pure Python, similar to what I did with xsNumPy. This project is also a testament to the richness of PyTorch's Tensor-oriented design. By reimplementing its core features in a self-contained and minimalistic fashion, this project aims to:
- Provide an educational tool for those seeking to understand tensor and automatic gradient (backpropagation) mechanics.
- Encourage developers to explore the intricacies of multidimensional array computation.
This project acknowledges the incredible contributions of the PyTorch team and community over decades of development. While this module reimagines PyTorch's functionality, it owes its design, inspiration, and motivation to the pioneering work of the core PyTorch developers. If that's obvious, this module is not a replacement for PyTorch by any stretch but an homage to its brilliance and an opportunity to explore its concepts from the ground up.
SlowTorch is a lightweight, pure-Python library inspired by PyTorch, designed to mimic essential tensor operations and auto-differentiation (backpropagation) features. This project is ideal for learning and experimentation with multidimensional tensor processing.
Install the latest version of SlowTorch using pip:
pip install -U git+https://github.com/xames3/slowtorch.git#egg=slowtorchThe codebase is structured to be intuitive and mirrors the design principles of PyTorch to a significant extent. Comprehensive docstrings are provided for each module and function, ensuring clarity and ease of understanding. Users are encouraged to delve into the code, experiment with it, and modify it to suit their learning curve.
Since, the implementation doesn't rely on any external package, it will work with any CPython build v3.10 and higher. Technically, it should work on 3.9 and below as well but due to some syntactical and type-aliasing changes, it might not support. For instance, the typing module was significantly changed in 3.10, hence some features like types.GenericAlias and using native types like tuple, list, etc. will not work. If you remove all the typing stuff, the code will work just fine, at least that's what I hope.
Note. SlowTorch cannot and should not be used as an alternative to PyTorch.
Contributions to this project are warmly welcomed. Whether it's refining the code, enhancing the documentation, or extending the current feature set, your input is highly valued. Feedback, whether constructive criticism or commendation, is equally appreciated and will be instrumental in the evolution of this educational tool.
This project is inspired by the remarkable work done by the PyTorch Development Team. It is a tribute to their contributions to the field of machine learning and the open-source community at large.
SlowTorch is licensed under the MIT License. See the LICENSE file for more details.