The original Keras warehouse has 60k+ stars, and the user base is not small. It is somewhat recommended (but not necessarily recommended) to learn and use keras. The reasons are as follows: The design idea of keras api originates from the decoupling and encapsulation of the programming paradigm of classic supervised learning. The official documentation will explain how to use jax for array operations, automatic differentiation, parallel processing, etc. Especially jax quickstart, how to think in jax and tutorial: Learn pytorch, but learn xla and cuda at the same time, and pay attention to the advantages, disadvantages and new features of jax and tensorflow. To add: 1. If you have the ability, learn them all, plus jax. 2. These frameworks can be simply divided as follows.
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Recently, working relationships have also paid more attention to xla/hlo-related developments; overall, it is good for mhlo (stablehlo); the openxla project is Google's attempt to separate compiler-related technologies from tensorflow, so that it can better serve different needs. Not only that, it also supports flexible development tools. Such as hugging face transformers, ollama, jax, keras, pytorch, google ai edge, unsloth, vllm and gemma.cpp. Developers can use Google AI. Jax is a promising high-performance numerical computing library that brings differentiable programming to the Python ecosystem. This article will introduce the differentiable programming technology of jax and how it brings to the fields of deep learning, machine learning and optimization.
Jax launched the kernel-level programming language pallas, which provides a unified programming model that is compatible with both tpu and gpu. This programming model is actually a further encapsulation based on triton and mosaic.
Jax 101。 jax quickstart — jax documentation how to.
