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57 lines
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57 lines
2.2 KiB
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.. meta::
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:description: hipDNN (Deep Neural Network) is a graph-based library providing improved performance for deep learning workloads with AMD GPUs.
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:keywords: hipDNN, ROCm, documentation
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********************
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hipDNN documentation
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********************
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hipDNN (Deep Neural Network) is a graph-based library that improves performance for deep learning workloads on AMD GPUs.
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hipDNN uses operation graphs as an intermediate representation for describing computations, allowing different backend engines to optimize and execute them efficiently.
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hipDNN provides an interface that follows established deep learning conventions and a plugin-based architecture, which allows advanced users to extend hipDNN without modifying the core library.
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.. tip::
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hipDNN provides instructions to :ref:`migrate-cudnn`.
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The public repository for hipDNN is located at `https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipdnn <https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipdnn>`_.
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.. note::
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hipDNN is in beta. Running production workloads is not recommended.
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hipDNN is only supported on ROCm 7.12 or later.
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.. grid:: 2
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:gutter: 3
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.. grid-item-card:: Install
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* :doc:`hipDNN installation <./install/installation>`
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* :doc:`Add hipDNN to your project <./install/add-project>`
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.. grid-item-card:: Conceptual
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* :doc:`High-level architecture <conceptual/architecture>`
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* :doc:`Engine configuration knobs <conceptual/knobs>`
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* :doc:`Backend architecture <conceptual/backend-architecture>`
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.. grid-item-card:: How to
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* :doc:`Build and execute operation graphs <how-to/build-execute-hipdnn>`
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* :doc:`Get and set engine knob configurations <how-to/get-set-engine-knob>`
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* :doc:`Migrate a cuDNN project to hipDNN <how-to/migrate-cudnn>`
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* :doc:`Develop plugins <how-to/develop-plugins>`
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.. grid-item-card:: Reference
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* :doc:`Operation support <reference/plugins>`
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* :doc:`Environment variables <reference/environment-variables>`
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* :doc:`Glossary <reference/glossary>`
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* :doc:`API reference <reference/api-reference>`
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You can find licensing information on the
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`Licensing <https://rocm.docs.amd.com/en/latest/about/license.html>`_ page.
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