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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 benefitsBuild 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.
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.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-12-03 UTC.
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