JAX: High-Performance Array Computing
=====================================

JAX is Autograd_ and XLA_, brought together for high-performance numerical computing.

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   .. grid-item-card:: Familiar API
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      JAX provides a familiar NumPy-style API for ease of adoption by researchers and engineers.

   .. grid-item-card:: Transformations
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      JAX includes composable function transformations for compilation, batching, automatic differentiation, and parallelization.

   .. grid-item-card:: Run Anywhere
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      The same code executes on multiple backends, including CPU, GPU, & TPU

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    .. grid-item-card:: :material-regular:`rocket_launch;2em` Getting Started
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      :link: beginner-guide
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      :class-card: getting-started

    .. grid-item-card:: :material-regular:`library_books;2em` User Guides
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      :link: user-guides
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    .. grid-item-card:: :material-regular:`laptop_chromebook;2em` Developer Docs
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      :link: contributor-guide
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.. toctree::
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   :caption: Getting Started

   installation
   notebooks/quickstart
   notebooks/thinking_in_jax
   notebooks/Common_Gotchas_in_JAX
   faq

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   jax-101/index


.. toctree::
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   :caption: Further Resources

   user_guides
   advanced_guide
   contributor_guide
   building_on_jax
   notes
   jax


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   :maxdepth: 1

   changelog
   glossary


.. _Autograd: https://github.com/hips/autograd
.. _XLA: https://www.tensorflow.org/xla