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JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. It is developed by Google with contributions from Nvidia and other community contributors.
Measurement & Overview
Explore the main themes, entities and connections around JAX (software). Start with the topic map, then use the sections below for research and deeper semantic analysis.
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jax machine learning automatic google numerical python designed differentiation via gpu tpu numpy tensorflow pytorch computing system xla cuda accelerated
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| JAX (software) | Developers | Google and JAX developers | 1.00 | infobox |
| JAX (software) | License | Apache 2.0 | 1.00 | infobox |
| JAX (software) | Operating system | Linux, macOS, Windows | 1.00 | infobox |
| JAX (software) | Original author | 1.00 | infobox | |
| JAX (software) | Platform | x86-64, ARM, GPU, TPU | 1.00 | infobox |
| JAX (software) | Repository | github.com/jax-ml/jax | 1.00 | infobox |
| JAX (software) | Type | Numerical computing, machine learning | 1.00 | infobox |
| JAX (software) | Website | jax.dev | 1.00 | infobox |
| JAX (software) | Written in | Python, C++, CUDA | 1.00 | infobox |
| TensorFlow | instance of | It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks | 0.80 | text |
| PyTorch | instance of | It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks | 0.80 | text |
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