https://github.com/openxla/xla

A machine learning compiler for GPUs, CPUs, and ML accelerators
https://github.com/openxla/xla

Last synced: 25 days ago
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A machine learning compiler for GPUs, CPUs, and ML accelerators

• Host: GitHub
• URL: https://github.com/openxla/xla
• Owner: openxla
• License: apache-2.0
• Created: 2022-08-09T15:30:01.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-11T22:43:40.000Z (4 months ago)
• Last Synced: 2024-07-11T22:46:03.624Z (4 months ago)
• Language: C++
• Homepage:
• Size: 191 MB
• Stars: 2,422
• Watchers: 39
• Forks: 364
• Open Issues: 1,285
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Codeowners: .github/CODEOWNERS

Awesome Lists containing this project

• awesome-approximate-dnn - OpenXLA - 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. (Tools / Graph Compiler)
• StarryDivineSky - openxla/xla

README

        
# XLA

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.

  

  

## Get started

If you want to use XLA to compile your ML project, refer to the corresponding

documentation for your ML framework:

* [PyTorch](https://pytorch.org/xla)

* [TensorFlow](https://www.tensorflow.org/xla)

* [JAX](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html)

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](CONTRIBUTING.md)

and then see the [developer guide](docs/developer_guide.md).

## Contacts

*   For questions, contact the maintainers - maintainers at openxla.org

## Resources

*   [Community Resources](https://github.com/openxla/community)

## Code of Conduct

While under TensorFlow governance, all community spaces for SIG OpenXLA are

subject to the

[TensorFlow Code of Conduct](https://github.com/tensorflow/tensorflow/blob/master/CODE_OF_CONDUCT.md).