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Deep Learning Framework
Summary
tinygrad is an end-to-end deep learning stack designed to be intentionally tiny, hackable, and efficient. It combines elements from PyTorch, JAX, and TVM to provide a tensor library, IR-based compiler, and JIT execution.
Highlights
- Provides a complete stack including a tensor library, autograd, IR/compiler, and JIT execution
- Designed to be lightweight and hackable compared to larger frameworks like PyTorch
- Utilizes a lazy execution model to automatically fuse operations into single kernels
- Supports a wide array of accelerators including CUDA, Metal, OpenCL, and WebGPU
auto-generated
Context
Audience
Machine Learning Engineers and Deep Learning Researchers
DomainMachine Learning
Formatopen source repository
Accessopen source
Related
PyTorchJAXTVMAutogradNeural NetworksCUDA