JAX (software)
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Machine-learning framework
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.[1] [2] [3]
It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra). It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks such as TensorFlow and PyTorch.[4] [5] The primary features of JAX are:[6]
- Providing a unified NumPy-like interface to computations that run on CPU, GPU, or TPU, in local or distributed settings.
- Built-in Just-In-Time (JIT) compilation via Open XLA, an open-source machine learning compiler ecosystem.
- Efficient evaluation of gradients via its automatic differentiation transformations.
- Automatic vectorization to efficiently map functions over arrays representing batches of inputs.
See also
[edit ]External links
[edit ]- Documentationː jax
.readthedocs .io - Colab (Jupyter/iPython) Quickstart Guideː colab
.research .google .com /github /google /jax /blob /main /docs /notebooks /quickstart .ipynb - TensorFlow's XLAː www
.tensorflow .org /xla (Accelerated Linear Algebra) - YouTube TensorFlow Channel "Intro to JAX: Accelerating Machine Learning research": www
.youtube .com /watch?v=WdTeDXsOSj4 - Original paperː mlsys
.org /Conferences /doc /2018 /146 .pdf
References
[edit ]- ^ Bradbury, James; Frostig, Roy; Hawkins, Peter; Johnson, Matthew James; Leary, Chris; MacLaurin, Dougal; Necula, George; Paszke, Adam; Vanderplas, Jake; Wanderman-Milne, Skye; Zhang, Qiao (2022年06月18日), "JAX: Autograd and XLA", Astrophysics Source Code Library, Google, Bibcode:2021ascl.soft11002B, archived from the original on 2022年06月18日, retrieved 2022年06月18日
- ^ Frostig, Roy; Johnson, Matthew James; Leary, Chris (2018年02月02日). "Compiling machine learning programs via high-level tracing" (PDF). MLsys: 1–3. Archived (PDF) from the original on 2022年06月21日.
- ^ "Using JAX to accelerate our research". www.deepmind.com. Archived from the original on 2022年06月18日. Retrieved 2022年06月18日.
- ^ Lynley, Matthew. "Google is quietly replacing the backbone of its AI product strategy after its last big push for dominance got overshadowed by Meta". Business Insider. Archived from the original on 2022年06月21日. Retrieved 2022年06月21日.
- ^ "Why is Google's JAX so popular?". Analytics India Magazine. 2022年04月25日. Archived from the original on 2022年06月18日. Retrieved 2022年06月18日.
- ^ "Quickstart — JAX documentation".
