TinyAutoGrad

TinyAutoGrad is an automatic differentiation engine and neural network library inspired by Micrograd, with optional CUDA support.

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🚀 Features

• ⚙️ Automatic differentiation (forward & backward)
• 🧱 Simple Tensor class with NumPy-style operations
• 🧠 Neural network support with MLP example
• 💻 CPU and ⚡ CUDA backend support
• 🧪 Built-in testing with pytest

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🛠️ Getting Started

CPU (Default)

Run the MNIST example on CPU:

python -m samples.mnist.mnist

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CUDA (Experimental)

Ensure CUDA toolkit (nvcc) is installed:

# Compile CUDA backend
nvcc -shared -o libops.so tinyautograd/ops.cu -Xcompiler -fPIC -lcublas

# Run test.py
python test.py

# Run MNIST with CUDA backend
python -m samples.mnist.mnist_cuda

⚠️ Make sure libops.so is in the current directory or Python load path.

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🧪 Running Tests

Run all unit tests:

pytest tinyautograd/ -s -v --cache-clear

Or a specific test:

pytest tinyautograd/test_rawtensor.py -s -v --cache-clear

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📂 Project Structure

.
├── README.md
├── libops.so
├── test.py
├── samples
│   └── mnist
│       ├── mnist.py
│       ├── mnist_cuda.py
│       ├── train-images-idx3-ubyte.gz
│       └── train-labels-idx1-ubyte.gz
├── tinyautograd
│   ├── __init__.py
│   ├── functional.py
│   ├── nn.py
│   ├── ops.cu
│   ├── optim.py
│   ├── rawtensor.py
│   ├── tensor.py
│   ├── test_nn.py
│   ├── test_rawtensor.py
│   └── test_tensor.py

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📄 License

MIT