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Keras原仓库的star 有60k+，用户基数也不算少了。 稍微有点推荐（但不是一定非要推荐）学习和使用keras。原因如下： keras api 设计思想源于对经典监督学习的编程范式的解耦，封装，. 学pytorch，但要同时学习xla， cuda，关注jax和 tensorflow 的优缺点以及新特性 补充一下： 1、如果有能力，全部要学，加上jax。 2、这些框架，可以简单的按如下分. 最近工作关系也比较关注 xla/hlo 相关的动态； 总体来看，利好 mhlo (stablehlo)；openxla 这个项目是 google 想将编译器相关技术从 tensorflow 独立出来，让其能够更好地服务于不.
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Jax 是一个充满潜力的高性能数值计算库，它将可微分编程带入了 python 生态系统。本文将介绍 jax 的可微分编程技术，以及它如何为深度学习、机器学习和优化等领域带来. Jax推出了内核级编程语言 pallas，pallas提供了一种同时兼容tpu与 gpu 的统一编程模型，这种编程模型实际上是建立在triton与 mosaic 上的进一步封装。 Jax 101。 jax quickstart — jax documentation how to.
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不仅如此，它还支持灵活的开发工具。 比如hugging face transformers、ollama、jax、keras、pytorch、google ai edge、unsloth、vllm和gemma.cpp。 开发者们可以在google ai. 官方文档会解释如何使用 jax 来进行数组运算、自动微分、并行处理等。 特别是 jax quickstart 、how to think in jax和 tutorial:
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