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Matt Williams
@@ -6,25 +6,25 @@
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Deep learning frameworks for ROCm
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**********************************
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Deep learning frameworks provide environments for machine learning, training, fine-tuning, inference, and performance optimization.
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Deep learning frameworks provide environments for machine learning, training, fine-tuning, inference, and performance optimization.
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ROCm provides a comprehensive ecosystem for optimized deep learning development and operations, as well as ROCm-aware versions of widely used deep learning frameworks and libraries, including PyTorch, TensorFlow, and JAX.
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ROCm offers a comprehensive ecosystem for optimized deep learning development and operations, along with ROCm-aware versions of widely used deep learning frameworks and libraries, including PyTorch, TensorFlow, and JAX.
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The AMD ROCm organization, which is actively involved in open-source contributions and development, collaborates closely with in-demand framework organizations to ensure that framework-specific optimizations effectively leverage AMD accelerators and GPU architectures.
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These topics in the ROCm documentation provide info on the installation and compatibility of these ROCm-enabled deep learning frameworks. These deep learning framework compatibility topics note the ROCm and third-party tool version support. Additionally, the Compatibility matrix topic notes the supported deep learning framework versions.
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The topics noted in the following table provide information about the installation and compatibility of these ROCm-enabled deep learning frameworks. The compatibility topics note the ROCm and third-party tool version support. Additionally, the :doc:`Compatibility matrix <../compatibility/compatibility-matrix>`_ topic notes the supported deep learning framework versions.
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.. list-table:: Deep learning frameworks
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.. list-table::
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:header-rows: 1
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:widths: 15 10 20 10
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:align: center
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* - Framework
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* - Framework (ROCm GitHub)
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- Installation topic
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- Installation options
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- Compatibility topic
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* - `PyTorch ROCm GitHub <https://github.com/ROCm/pytorch>`_
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* - `PyTorch <https://github.com/ROCm/pytorch>`_
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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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@@ -37,7 +37,7 @@ These topics in the ROCm documentation provide info on the installation and comp
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<a href="https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html"><i class="fas fa-link fa-lg"></i></a>
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* - `TensorFlow ROCm GitHub <https://github.com/ROCm/tensorflow-upstream>`_
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* - `TensorFlow <https://github.com/ROCm/tensorflow-upstream>`_
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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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@@ -49,7 +49,7 @@ These topics in the ROCm documentation provide info on the installation and comp
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<a href="https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/tensorflow-compatibility.html"><i class="fas fa-link fa-lg"></i></a>
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* - `JAX ROCm GitHub <https://github.com/ROCm/jax>`_
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* - `JAX <https://github.com/ROCm/jax>`_
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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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@@ -59,7 +59,7 @@ These topics in the ROCm documentation provide info on the installation and comp
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<a href="https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/jax-compatibility.html"><i class="fas fa-link fa-lg"></i></a>
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* - `verl ROCm GitHub <https://github.com/ROCm/verl>`_
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* - `verl <https://github.com/ROCm/verl>`_
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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/verl-install.html"><i class="fas fa-link fa-lg"></i></a>
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@@ -69,7 +69,7 @@ These topics in the ROCm documentation provide info on the installation and comp
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<a href="https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/verl-compatibility.html"><i class="fas fa-link fa-lg"></i></a>
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* - `Stanford Megatron-LM ROCm GitHub <https://github.com/ROCm/Stanford-Megatron-LM>`_
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* - `Stanford Megatron-LM <https://github.com/ROCm/Stanford-Megatron-LM>`_
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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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@@ -79,7 +79,7 @@ These topics in the ROCm documentation provide info on the installation and comp
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<a href="https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.html"><i class="fas fa-link fa-lg"></i></a>
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* - `DGL ROCm GitHub <https://github.com/ROCm/dgl>`_
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* - `DGL <https://github.com/ROCm/dgl>`_
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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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@@ -89,7 +89,7 @@ These topics in the ROCm documentation provide info on the installation and comp
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<a href="https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/dgl-compatibility.html"><i class="fas fa-link fa-lg"></i></a>
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* - `Megablocks ROCm GitHub <https://github.com/ROCm/megablocks>`_
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* - `Megablocks <https://github.com/ROCm/megablocks>`_
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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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@@ -99,7 +99,7 @@ These topics in the ROCm documentation provide info on the installation and comp
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<a href="https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/megablocks-compatibility.html"><i class="fas fa-link fa-lg"></i></a>
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* - `Taichi ROCm GitHub <https://github.com/ROCm/taichi>`_
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* - `Taichi <https://github.com/ROCm/taichi>`_
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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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@@ -111,9 +111,6 @@ These topics in the ROCm documentation provide info on the installation and comp
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<a href="https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/taichi-compatibility.html"><i class="fas fa-link fa-lg"></i></a>
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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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