simplegrade 0.1.5

A lightweight PyTorch-inspired autograd library built from scratch

SimpleGrade

SimpleGrade is a lightweight Python library inspired by PyTorch and Tinygrad. It provides automatic differentiation and the core components required to build and train neural networks from scratch, making it an educational framework for understanding how deep learning libraries work internally.

Installation

Install the latest release from PyPI:

pip install simplegrade

Or install the latest development version from GitHub:

git clone https://github.com/mohamedrxo/simplegrad.git
cd simplegrad
pip install -e .

Features

• Automatic differentiation (Autograd)
• Tensor operations
• Neural network building blocks
• Linear layers
• Loss functions
• SGD optimizer
• DataLoader
• NumPy-based implementation
• Lightweight and easy to understand

Example

from simplegrade import Tensor

a = Tensor([[1, 2, 3]], requires_grad=True)
b = Tensor([[2, 1, 0]], requires_grad=True)

c = a + b
d = a * b
loss = d.sum()

loss.backward()

print("Loss:", loss)
print("Gradient of a:", a.grad)
print("Gradient of b:", b.grad)

Project Links

• PyPI: https://pypi.org/project/simplegrade/
• GitHub: https://github.com/mohamedrxo/simplegrad
• Issues: https://github.com/mohamedrxo/simplegrad/issues

Contributing

Contributions, bug reports, feature requests, and suggestions are welcome. If you find this project useful, consider starring the repository on GitHub.