rocm_jax/docs/index.rst

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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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:link: contributor-guide
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Installation
------------
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.. tab-item:: CPU
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pip install "jax[cpu]"
.. tab-item:: GPU (CUDA)
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pip install "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
.. tab-item:: TPU (Google Cloud)
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pip install "jax[tpu]" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
For more information about supported accelerators and platforms, and for other
installation options, see the `Install Guide`_ in the project README.
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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
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:caption: Further Resources
user_guides
advanced_guide
contributor_guide
notes
jax
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changelog
glossary
.. _Autograd: https://github.com/hips/autograd
.. _XLA: https://www.tensorflow.org/xla
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.. _Install Guide: https://github.com/google/jax#installation