Minimalist deep learning framework for education and exploration
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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
git clone https://github.com/tinygrad/tinygrad.git && cd tinygrad && python3 -m pip install -e .
python3 -m pip install git+https://github.com/tinygrad/tinygrad.git
Highlighted Details
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Licensing & Compatibility
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.
17 hours ago
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