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Use of Radeon and Ryzen reference updated [Develop] (#5432)

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* Use of Radeon and Ryzen reference updated

* Pytorch link update
This commit is contained in:
Pratik Basyal
2025-09-24 20:07:41 -04:00
committed by GitHub
parent 1629d3f0ea
commit d92d9268dc
3 changed files with 5 additions and 5 deletions
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2
RELEASE.md
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@@ -152,7 +152,7 @@ The release notes provide a summary of notable changes since the previous ROCm r
- [ROCm upcoming changes](#rocm-upcoming-changes)
```{note}
If you’re using AMD Radeon™ PRO or Radeon GPUs in a workstation setting with a display connected, see the [Use ROCm on Radeon GPUs](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/compatibility/native_linux/native_linux_compatibility.html)
If you’re using AMD Radeon GPUs or Ryzen APUs in a workstation setting with a display connected, see the [Use ROCm on Radeon and Ryzen](https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/index.html)
documentation to verify compatibility and system requirements.
```

6
docs/compatibility/compatibility-matrix.rst
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@@ -11,9 +11,9 @@ Use this matrix to view the ROCm compatibility and system requirements across su
You can also refer to the :ref:`past versions of ROCm compatibility matrix<past-rocm-compatibility-matrix>`.
Accelerators and GPUs listed in the following table support compute workloads (no display
information or graphics). If you’re using ROCm with AMD Radeon or Radeon Pro GPUs for graphics
workloads, see the `Use ROCm on Radeon GPU documentation
<https://rocm.docs.amd.com/projects/radeon/en/latest/docs/compatibility.html>`_ to verify
information or graphics). If you’re using ROCm with AMD Radeon GPUs or Ryzen APUs for graphics
workloads, see the `Use ROCm on Radeon and Ryzen
<https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/index.html>`_ to verify
compatibility and system requirements.
.. |br| raw:: html

2
docs/how-to/rocm-for-ai/install.rst
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@@ -47,7 +47,7 @@ Deep learning frameworks
========================
ROCm supports deep learning frameworks and libraries including `PyTorch
<https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-available-as-python-package>`_, `TensorFlow
<https://pytorch.org>`_, `TensorFlow
<https://tensorflow.org>`_, `JAX <https://jax.readthedocs.io/en/latest>`_, and more.
Review the :doc:`framework installation documentation <../deep-learning-rocm>`. For ease-of-use, it's recommended to use official ROCm prebuilt Docker