ClumsyGrad

ClumsyGrad#

A Simple Automatic Differentiation Library built on top of NumPy

Contents:

Key Features#

  • Lightweight Design

    Minimal dependencies - built primarily on NumPy

  • Minimalist Architecture

    Easy to understand and modify for experimentation

Quick Start Example#

from clumsygrad.tensor import Tensor, TensorType
from clumsygrad.loss import mse_loss

# Create tensors with gradient tracking
x = Tensor([[1.0, 2.0]], tensor_type=TensorType.INPUT)
W = Tensor([[3.0], [4.0]], tensor_type=TensorType.PARAMETER)
b = Tensor([5.0], tensor_type=TensorType.PARAMETER)
target = Tensor([15.0], tensor_type=TensorType.INPUT)

# Forward pass: linear transformation
output = x @ W + b  # Matrix multiplication + bias

# Compute loss
loss = mse_loss(output, target)

# Backward pass: automatic differentiation
loss.backward()

# Gradients are now available
print(f"W gradient: {W.grad}")
print(f"b gradient: {b.grad}")
print(f"Loss: {loss.data}")

Installation#

To install ClumsyGrad, you can use pip:

pip install clumsygrad

Note

ClumsyGrad requires Python 3.9+ and NumPy.

Performance Notes#

Warning

ClumsyGrad is mainly designed by me for educational purposes, and may not be completely optimzed for performance.

Contributing#

ClumsyGrad welcomes contributions! Whether you’re:

  • Reporting bugs

  • Suggesting features

  • Improving documentation

  • Contributing code

Acknowledgments#

  • Inspired by PyTorch (obviously!) and other automatic differentiation frameworks.

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