[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-523":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":9,"totalLinesOfCode":9,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":9,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":9,"createdAt":9,"pushedAt":9,"updatedAt":24,"readmeContent":25,"aiSummary":26,"trendingCount":16,"starSnapshotCount":16,"syncStatus":27,"lastSyncTime":28,"discoverSource":29},523,"xla","openxla\u002Fxla","openxla","A machine learning compiler for GPUs, CPUs, and ML accelerators",null,"https:\u002F\u002Fgithub.com\u002Fopenxla\u002Fxla","C++",4323,826,52,215,0,10,17,71,30,30.75,false,"main","2026-06-12 02:00:14","# XLA\n\nXLA (Accelerated Linear Algebra) is an open-source machine learning (ML)\ncompiler for GPUs, CPUs, and ML accelerators.\n\n\u003Cpicture>\n  \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\"docs\u002Fimages\u002Fopenxla_dark.svg\">\n  \u003Cimg alt=\"OpenXLA Ecosystem\" src=\"docs\u002Fimages\u002Fopenxla.svg\">\n\u003C\u002Fpicture>\n\nThe XLA compiler takes models from popular ML frameworks such as PyTorch,\nTensorFlow, and JAX, and optimizes them for high-performance execution across\ndifferent hardware platforms including GPUs, CPUs, and ML accelerators.\n\n[openxla.org](https:\u002F\u002Fopenxla.org\u002F) is the project's website.\n\n## Get started\n\nIf you want to use XLA to compile your ML project, refer to the corresponding\ndocumentation for your ML framework:\n\n* [PyTorch](https:\u002F\u002Fpytorch.org\u002Fxla)\n* [TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Fxla)\n* [JAX](https:\u002F\u002Fjax.readthedocs.io\u002Fen\u002Flatest\u002Fnotebooks\u002Fquickstart.html)\n\nIf you're not contributing code to the XLA compiler, you don't need to clone and\nbuild this repo. Everything here is intended for XLA contributors who want to\ndevelop the compiler and XLA integrators who want to debug or add support for ML\nfrontends and hardware backends.\n\n## Contribute\n\nIf you'd like to contribute to XLA, review\n[How to Contribute](docs\u002Fcontributing.md) and then see the\n[developer guide](docs\u002Fdeveloper_guide.md).\n\n## Contacts\n\n*   For questions, contact the maintainers - maintainers at openxla.org\n\n## Resources\n\n*   [Community Resources](https:\u002F\u002Fgithub.com\u002Fopenxla\u002Fcommunity)\n\n## Code of Conduct\n\nWhile under TensorFlow governance, all community spaces for SIG OpenXLA are\nsubject to the\n[TensorFlow Code of Conduct](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Fblob\u002Fmaster\u002FCODE_OF_CONDUCT.md).\n","XLA（Accelerated Linear Algebra）是一个开源的机器学习编译器，专为GPU、CPU和ML加速器设计。它能够从PyTorch、TensorFlow和JAX等流行框架中获取模型，并针对不同硬件平台进行优化以实现高性能执行。XLA的核心功能包括跨多种硬件设备的高效代码生成与优化，支持动态形状操作，以及对现有深度学习模型的无缝集成。适用于需要在异构计算环境中加速机器学习工作负载的应用场景，如数据中心的大规模训练任务或边缘设备上的推理服务。",2,"2026-06-11 02:37:06","trending"]