Chapter: index (docs/index.md)
XLA
XLA (Accelerated Linear Algebra) is an open-source compiler for machine
learning. The XLA compiler takes models from popular frameworks such as PyTorch,
TensorFlow, and JAX, and optimizes the models for high-performance execution
across different hardware platforms including GPUs, CPUs, and ML accelerators.
As a part of the OpenXLA project, XLA is built collaboratively by
industry-leading ML hardware and software companies, including
Alibaba, Amazon Web Services, AMD, Apple, Arm, Google, Intel, Meta, and NVIDIA.
Key benefits
- Build anywhere: XLA is already integrated into leading ML frameworks
such as TensorFlow, PyTorch, and JAX.
- Run anywhere: It supports various backends including GPUs, CPUs, and ML
accelerators, and includes a pluggable infrastructure to add support for
more.
- Maximize and scale performance: It optimizes a model's performance with
production-tested optimization passes and automated partitioning for model
parallelism.
- Eliminate complexity: It leverages the power of
MLIR to bring the best capabilities into a single
compiler toolchain, so you don't have to manage a range of domain-specific
compilers.
- Future ready: As an open source project, built through a collaboration
of leading ML hardware and software vendors, XLA is designed to operate at
the cutting-edge of the ML industry.
Documentation
To learn more about XLA, check out the links on the left. If you're a new XLA
developer, you might want to start with XLA architecture and
then read Contributing.