tinygrad is a lightweight deep learning framework designed for simplicity and ease of accelerator integration, targeting researchers and developers who find existing frameworks too complex. It aims to be the easiest framework to add new hardware backends to, supporting both inference and training with a RISC-like philosophy compared to XLA's CISC approach.

How It Works

tinygrad leverages laziness to fuse operations into single kernels, optimizing execution. Its core design prioritizes a minimal set of low-level operations (~25) required for an accelerator to support, making it highly extensible. This approach allows for efficient execution on diverse hardware, from CPUs and GPUs to specialized accelerators.

Quick Start & Requirements

• Install from source: git clone https://github.com/tinygrad/tinygrad.git && cd tinygrad && python3 -m pip install -e .
• Direct install: python3 -m pip install git+https://github.com/tinygrad/tinygrad.git
• Prerequisites: Python 3.x. Supports CPU, OpenCL, LLVM, METAL, CUDA, AMD, NV, QCOM, and WebGPU.
• Documentation: https://docs.tinygrad.org/

Highlighted Details

• Supports LLaMA and Stable Diffusion models.
• Achieves 98% accuracy on MNIST in ~5 seconds with a simple LinearNet example.
• Extensive accelerator support including CPU, GPU (OpenCL, CUDA, Metal), LLVM, and more.
• Designed for easy addition of new hardware backends.

Maintenance & Community

• Maintained by tiny corp.
• Active Discord community: https://discord.gg/ZjZadyC7PK
• Unit tests are run via GitHub Actions.

Licensing & Compatibility

• License: Apache 2.0.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The project is explicitly stated as alpha software. The README warns against code golf, complex or large diffs, and unsolicited documentation/whitespace changes, indicating a strict contribution policy focused on simplicity and clarity.