XLA (Accelerated Linear Algebra) is an open-source machine learning (ML) compiler for GPUs, CPUs, and ML accelerators.

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.

If you want to use XLA to compile your ML project, refer to the corresponding documentation for your ML framework:

• PyTorch
• TensorFlow
• JAX

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.

If you'd like to contribute to XLA, review How to Contribute and then see the developer guide.

• For questions, contact Thea Lamkin - thealamkin at google.com.

• Community Resources

While under TensorFlow governance, all community spaces for SIG OpenXLA are subject to the TensorFlow Code of Conduct.