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69 lines
2.2 KiB
ReStructuredText
.. meta::
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:description: How to install deep learning frameworks for ROCm
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:keywords: deep learning, frameworks, ROCm, install, PyTorch, TensorFlow, JAX, MAGMA, DeepSpeed, ML, AI
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********************************************
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Installing deep learning frameworks for ROCm
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********************************************
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ROCm provides a comprehensive ecosystem for deep learning development, including
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:ref:`libraries <artificial-intelligence-apis>` for optimized deep learning operations and ROCm-aware versions of popular
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deep learning frameworks and libraries such as PyTorch, TensorFlow, JAX, and MAGMA. ROCm works closely with these
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frameworks to ensure that framework-specific optimizations take advantage of AMD accelerator and GPU architectures.
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The following guides cover installation processes for ROCm-aware deep learning frameworks.
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.. grid::
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.. grid-item::
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:columns: 3
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:doc:`PyTorch for ROCm <rocm-install-on-linux:how-to/3rd-party/pytorch-install>`
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.. grid-item::
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:columns: 3
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:doc:`TensorFlow for ROCm <rocm-install-on-linux:how-to/3rd-party/tensorflow-install>`
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.. grid-item::
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:columns: 3
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.. grid-item::
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:columns: 3
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.. grid-item::
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:columns: 3
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:doc:`JAX for ROCm <rocm-install-on-linux:how-to/3rd-party/jax-install>`
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.. grid-item::
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:columns: 3
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:doc:`MAGMA for ROCm <rocm-install-on-linux:how-to/3rd-party/magma-install>`
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.. grid-item::
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:columns: 3
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.. grid-item::
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:columns: 3
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The following chart steps through typical installation workflows for installing deep learning frameworks for ROCm.
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.. image:: ../data/how-to/framework_install_2024_05_23.png
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:alt: Flowchart for installing ROCm-aware machine learning frameworks
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:align: center
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Find information on version compatibility and framework release notes in :doc:`Third-party support matrix
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<rocm-install-on-linux:reference/3rd-party-support-matrix>`.
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.. note::
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For guidance on installing ROCm itself, refer to :doc:`ROCm installation for Linux <rocm-install-on-linux:index>`.
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Learn how to use your ROCm deep learning environment for training, fine-tuning, inference, and performance optimization
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through the following guides.
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* :doc:`rocm-for-ai/index`
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* :doc:`llm-fine-tuning-optimization/index`
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