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https://github.com/ROCm/ROCm.git
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Deep learning frameworks edits for scale (#5189)
* Deep learning frameworks edits for scale Based on https://ontrack-internal.amd.com/browse/ROCDOC-1809 * update table table * leo comments * formatting * format * update table based on feedback * header * Update machine learning page * headers * Apply suggestions from code review Co-authored-by: anisha-amd <anisha.sankar@amd.com> * Update .wordlist.txt * formatting * Update docs/how-to/deep-learning-rocm.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> --------- Co-authored-by: Matt Williams <Matt.Williams+amdeng@amd.com> Co-authored-by: anisha-amd <anisha.sankar@amd.com> Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com>
This commit is contained in:
Matt Williams
@@ -124,6 +124,7 @@ ENDPGM
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EPYC
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ESXi
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EoS
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fas
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FBGEMM
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FFT
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FFTs
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@@ -196,6 +197,7 @@ HWE
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HWS
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Haswell
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Higgs
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href
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Hyperparameters
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Huggingface
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ICD
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@@ -2,58 +2,132 @@
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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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**********************************
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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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Deep learning frameworks provide environments for machine learning, training, fine-tuning, inference, and performance optimization.
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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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ROCm offers a complete ecosystem for developing and running deep learning applications efficiently. It also provides ROCm-compatible versions of popular frameworks and libraries, such as PyTorch, TensorFlow, JAX, and others.
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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:`verl compatibility <../compatibility/ml-compatibility/verl-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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* :doc:`Megablocks compatibility <../compatibility/ml-compatibility/megablocks-compatibility>`
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* :doc:`Taichi compatibility <../compatibility/ml-compatibility/taichi-compatibility>`
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The AMD ROCm organization actively contributes to open-source development and collaborates closely with framework organizations. This collaboration ensures that framework-specific optimizations effectively leverage AMD GPUs and accelerators.
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This chart steps through typical installation workflows for installing deep learning frameworks for ROCm.
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The table below summarizes information about ROCm-enabled deep learning frameworks. It includes details on ROCm compatibility and third-party tool support, installation steps and options, and links to GitHub resources. For a complete list of supported framework versions on ROCm, see the :doc:`Compatibility matrix <../compatibility/compatibility-matrix>` topic.
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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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.. list-table::
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:header-rows: 1
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:widths: 5 3 6 3
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See the installation instructions to get started.
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* - Framework
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- Installation
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- Installation options
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- GitHub
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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:`verl for ROCm <rocm-install-on-linux:install/3rd-party/verl-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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* :doc:`Megablocks for ROCm <rocm-install-on-linux:install/3rd-party/megablocks-install>`
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* :doc:`Taichi for ROCm <rocm-install-on-linux:install/3rd-party/taichi-install>`
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* - `PyTorch <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html>`_
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- .. raw:: html
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<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html"><i class="fas fa-link fa-lg"></i></a>
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-
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- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-docker-image-with-pytorch-pre-installed>`_
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- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-wheels-package>`_
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- `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-rocm-base-docker-image>`_
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- `Upstream Docker file <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-upstream-dockerfile>`_
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- .. raw:: html
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<a href="https://github.com/ROCm/pytorch"><i class="fab fa-github fa-lg"></i></a>
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* - `TensorFlow <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/tensorflow-compatibility.html>`_
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- .. raw:: html
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<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html"><i class="fas fa-link fa-lg"></i></a>
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-
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- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-docker-image-with-tensorflow-pre-installed>`_
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- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-wheels-package>`_
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.. note::
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- .. raw:: html
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<a href="https://github.com/ROCm/tensorflow-upstream"><i class="fab fa-github fa-lg"></i></a>
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* - `JAX <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/jax-compatibility.html>`_
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- .. raw:: html
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<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html"><i class="fas fa-link fa-lg"></i></a>
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-
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- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html#using-a-prebuilt-docker-image>`_
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- .. raw:: html
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<a href="https://github.com/ROCm/jax"><i class="fab fa-github fa-lg"></i></a>
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* - `verl <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/verl-compatibility.html>`_
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<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html"><i class="fas fa-link fa-lg"></i></a>
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-
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- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html#use-a-prebuilt-docker-image-with-verl-pre-installed>`_
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- .. raw:: html
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<a href="https://github.com/ROCm/verl"><i class="fab fa-github fa-lg"></i></a>
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* - `Stanford Megatron-LM <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.html>`_
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- .. raw:: html
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<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html"><i class="fas fa-link fa-lg"></i></a>
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-
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- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html#use-a-prebuilt-docker-image-with-stanford-megatron-lm-pre-installed>`_
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<a href="https://github.com/ROCm/Stanford-Megatron-LM"><i class="fab fa-github fa-lg"></i></a>
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* - `DGL <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/dgl-compatibility.html>`_
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- .. raw:: html
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<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html"><i class="fas fa-link fa-lg"></i></a>
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-
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- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html#use-a-prebuilt-docker-image-with-dgl-pre-installed>`_
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- .. raw:: html
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<a href="https://github.com/ROCm/dgl"><i class="fab fa-github fa-lg"></i></a>
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* - `Megablocks <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/megablocks-compatibility.html>`_
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- .. raw:: html
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<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html"><i class="fas fa-link fa-lg"></i></a>
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-
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- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html#using-a-prebuilt-docker-image-with-megablocks-pre-installed>`_
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<a href="https://github.com/ROCm/megablocks"><i class="fab fa-github fa-lg"></i></a>
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* - `Taichi <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/taichi-compatibility.html>`_
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- .. raw:: html
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<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html"><i class="fas fa-link fa-lg"></i></a>
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-
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- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-prebuilt-docker-image-with-taichi-pre-installed>`_
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- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-wheels-package>`_
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- .. raw:: html
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<a href="https://github.com/ROCm/taichi"><i class="fab fa-github fa-lg"></i></a>
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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:`Use ROCm for training <rocm-for-ai/training/index>`
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* :doc:`Use ROCm for fine-tuning LLMs <rocm-for-ai/fine-tuning/index>`
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* :doc:`Use ROCm for AI inference <rocm-for-ai/inference/index>`
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* :doc:`Use ROCm for AI inference optimization <rocm-for-ai/inference-optimization/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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@@ -1,14 +1,14 @@
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.. meta::
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:description: How to install ROCm and popular machine learning frameworks.
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:description: How to install ROCm and popular deep learning frameworks.
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:keywords: ROCm, AI, LLM, train, fine-tune, FSDP, DeepSpeed, LLaMA, tutorial
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.. _rocm-for-ai-install:
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***********************************************
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Installing ROCm and machine learning frameworks
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***********************************************
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********************************************
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Installing ROCm and deep learning frameworks
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********************************************
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Before getting started, install ROCm and supported machine learning frameworks.
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Before getting started, install ROCm and supported deep learning frameworks.
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.. grid:: 1
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@@ -43,29 +43,16 @@ distribution's package manager. See the following documentation resources to get
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If you encounter any issues during installation, refer to the
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:doc:`Installation troubleshooting <rocm-install-on-linux:reference/install-faq>` guide.
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Machine learning frameworks
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===========================
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Deep learning frameworks
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========================
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ROCm supports popular machine learning frameworks and libraries including `PyTorch
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ROCm supports deep learning frameworks and libraries including `PyTorch
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<https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-available-as-python-package>`_, `TensorFlow
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<https://tensorflow.org>`_, `JAX <https://jax.readthedocs.io/en/latest>`_, and `DeepSpeed
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<https://cloudblogs.microsoft.com/opensource/2022/03/21/supporting-efficient-large-model-training-on-amd-instinct-gpus-with-deepspeed/>`_.
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<https://tensorflow.org>`_, `JAX <https://jax.readthedocs.io/en/latest>`_, and more.
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Review the framework installation documentation. For ease-of-use, it's recommended to use official ROCm prebuilt Docker
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Review the :doc:`framework installation documentation <../deep-learning-rocm>`. For ease-of-use, it's recommended to use official ROCm prebuilt Docker
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images with the framework pre-installed.
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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:`verl for ROCm <rocm-install-on-linux:install/3rd-party/verl-install>`
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* :doc:`Stanford Megatron-LM for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
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* :doc:`DGL for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
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Next steps
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==========
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@@ -27,6 +27,24 @@ subtrees:
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title: ROCm on Radeon GPUs
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- file: how-to/deep-learning-rocm.md
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title: Deep learning frameworks
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subtrees:
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- entries:
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- file: compatibility/ml-compatibility/pytorch-compatibility.rst
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title: PyTorch compatibility
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- file: compatibility/ml-compatibility/tensorflow-compatibility.rst
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title: TensorFlow compatibility
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- file: compatibility/ml-compatibility/jax-compatibility.rst
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title: JAX compatibility
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- file: compatibility/ml-compatibility/verl-compatibility.rst
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title: verl compatibility
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- file: compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
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title: Stanford Megatron-LM compatibility
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- file: compatibility/ml-compatibility/dgl-compatibility.rst
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title: DGL compatibility
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- file: compatibility/ml-compatibility/megablocks-compatibility.rst
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title: Megablocks compatibility
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- file: compatibility/ml-compatibility/taichi-compatibility.rst
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title: Taichi compatibility
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- file: how-to/build-rocm.rst
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title: Build ROCm from source
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