teenygrad is a minimalist deep learning framework designed for educational purposes, demonstrating core concepts with a minimal codebase. It's ideal for researchers and developers seeking to understand the fundamentals of automatic differentiation and tensor operations without the complexity of larger libraries.

How It Works

teenygrad achieves its small footprint by sharing 90% of its code with the tinygrad project, focusing on essential components like tensor.py and mlops.py for the frontend. A simplified lazy.py replaces tinygrad's extensive backend, enabling efficient tensor operations and automatic differentiation with a significantly reduced line count.

Quick Start & Requirements

• Install: pip install numpy tqdm
• Usage: PYTHONPATH="." python mnist.py
• Dependencies: NumPy, tqdm

Highlighted Details

• MNIST trainer implemented in under 1000 lines of code.
• Shares 90% of its codebase with the tinygrad project.
• Demonstrates core deep learning concepts including automatic differentiation.

Maintenance & Community

No specific community channels or maintenance details are provided in the README.

Licensing & Compatibility

The README does not specify a license.

Limitations & Caveats

teenygrad intentionally omits the speed optimizations and diverse backend support found in its parent project, tinygrad, making it less suitable for production workloads.