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* Create stanford-megatron-lm-compatibility.rst * toc and wordlist * Update deep-learning-rocm.rst * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst * fixes and adding to main compat matrix * formatting fix * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst * Update docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst * Update stanford-megatron-lm-compatibility.rst --------- Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com>
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2.6 KiB
ReStructuredText
54 lines
2.6 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, and JAX. 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 provide information on compatibility and supported
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features for these ROCm-enabled deep learning frameworks.
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* :doc:`PyTorch compatibility <../compatibility/ml-compatibility/pytorch-compatibility>`
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* :doc:`TensorFlow compatibility <../compatibility/ml-compatibility/tensorflow-compatibility>`
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* :doc:`JAX compatibility <../compatibility/ml-compatibility/jax-compatibility>`
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* :doc:`Stanford Megatron-LM compatibility <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`
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* :doc:`DGL compatibility <../compatibility/ml-compatibility/dgl-compatibility>`
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This chart steps through typical installation workflows for installing deep learning frameworks for ROCm.
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.. image:: ../data/how-to/framework_install_2024_07_04.png
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:alt: Flowchart for installing ROCm-aware machine learning frameworks
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:align: center
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See the installation instructions to get started.
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* :doc:`PyTorch for ROCm <rocm-install-on-linux:install/3rd-party/pytorch-install>`
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* :doc:`TensorFlow for ROCm <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
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* :doc:`JAX for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
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* :doc:`Stanford Megatron-LM for ROCm <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>`
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* :doc:`DGL for ROCm <rocm-install-on-linux:install/3rd-party/dgl-install>`
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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:`Training <rocm-for-ai/training/index>`
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* :doc:`Fine-tuning LLMs <rocm-for-ai/fine-tuning/index>`
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* :doc:`Inference <rocm-for-ai/inference/index>`
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* :doc:`Inference optimization <rocm-for-ai/inference-optimization/index>`
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