openxla/xla
An open-source compiler that optimizes machine learning models from popular frameworks for high-performance execution across diverse hardware platforms.
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XLA (Accelerated Linear Algebra) is a compiler infrastructure that takes models from ML frameworks such as PyTorch, TensorFlow, and JAX and generates optimized code for execution on GPUs, CPUs, and specialized ML accelerators. It performs graph-level optimizations and hardware-specific code generation to improve performance. The project is the core of the OpenXLA ecosystem and is primarily intended for compiler contributors and integrators adding support for new ML frontends or hardware backends.
Frequently asked
- What is openxla/xla?
- An open-source compiler that optimizes machine learning models from popular frameworks for high-performance execution across diverse hardware platforms.
- Is xla open source?
- Yes — openxla/xla is open source, released under the Apache-2.0 license.
- What language is xla written in?
- openxla/xla is primarily written in C++.
- How popular is xla?
- openxla/xla has 4.4k stars on GitHub.
- Where can I find xla?
- openxla/xla is on GitHub at https://github.com/openxla/xla.