google/torchax
torchax is a PyTorch frontend for JAX. It gives JAX the ability to author JAX programs using familiar PyTorch syntax. It also provides JAX-Pytorch interoperability, meaning, one can mix JAX & Pytorch syntax together when authoring ML programs, and run it in every hardware JAX can run.
This tool helps machine learning engineers and researchers accelerate their PyTorch models, especially when working with high-performance hardware like Google Cloud TPUs. It takes existing PyTorch code and allows it to run on JAX, providing the ability to seamlessly use JAX's advanced features. The output is a PyTorch model that benefits from JAX's performance optimizations and hardware support.
196 stars. Used by 1 other package. Available on PyPI.
Use this if you are a machine learning engineer or researcher using PyTorch and want to leverage Google Cloud TPUs or JAX's performance features without rewriting your entire codebase.
Not ideal if your primary workflow does not involve high-performance computing on JAX-supported accelerators or if you are not familiar with PyTorch or JAX concepts.
Stars
196
Forks
25
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
Reverse dependents
1
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