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XLA
XLA (Accelerated Linear Algebra) is an open-source machine learning (ML) compiler for GPUs, CPUs, and ML accelerators.
<picture> <source media="(prefers-color-scheme: dark)" srcset="docs/images/openxla_dark.svg"> <img alt="OpenXLA Ecosystem" src="docs/images/openxla.svg"> </picture>The XLA compiler takes models from popular ML frameworks such as PyTorch, TensorFlow, and JAX, and optimizes them for high-performance execution across different hardware platforms including GPUs, CPUs, and ML accelerators.
Get started
If you want to use XLA to compile your ML project, refer to the corresponding documentation for your ML framework:
If you're not contributing code to the XLA compiler, you don't need to clone and build this repo. Everything here is intended for XLA contributors who want to develop the compiler and XLA integrators who want to debug or add support for ML frontends and hardware backends.
Contribute
If you'd like to contribute to XLA, review How to Contribute and then see the developer guide.
Contacts
- For questions, contact the maintainers - maintainers at openxla.org
Resources
Code of Conduct
While under TensorFlow governance, all community spaces for SIG OpenXLA are subject to the TensorFlow Code of Conduct.