Docs: frameworks compatibility standardization (#5488)

.wordlist.txt

@@ -34,6 +34,7 @@ AlexNet
 Andrej
 Arb
 Autocast
+autograd
 BARs
 BatchNorm
 BLAS
@@ -86,9 +87,11 @@ Conda
 ConnectX
 CountOnes
 CuPy
+customizable
 da
 Dashboarding
 Dataloading
+dataflows
 DBRX
 DDR
 DF
@@ -182,7 +185,7 @@ GPT
 GPU
 GPU's
 GPUs
-Graphbolt
+GraphBolt
 GraphSage
 GRBM
 GRE
@@ -212,6 +215,7 @@ Haswell
 Higgs
 href
 Hyperparameters
+HybridEngine
 Huggingface
 IB
 ICD

docs/compatibility/ml-compatibility/dgl-compatibility.rst

@@ -2,7 +2,7 @@
 
 .. meta::
     :description: Deep Graph Library (DGL) compatibility
-    :keywords: GPU, DGL compatibility
+    :keywords: GPU, CPU, deep graph library, DGL, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -10,24 +10,42 @@
 DGL compatibility
 ********************************************************************************
 
-Deep Graph Library `(DGL) <https://www.dgl.ai/>`_ is an easy-to-use, high-performance and scalable 
+Deep Graph Library (`DGL <https://www.dgl.ai/>`__) is an easy-to-use, high-performance, and scalable 
 Python package for deep learning on graphs. DGL is framework agnostic, meaning 
-if a deep graph model is a component in an end-to-end application, the rest of 
+that if a deep graph model is a component in an end-to-end application, the rest of 
 the logic is implemented using PyTorch.  
 
-* ROCm support for DGL is hosted in the `https://github.com/ROCm/dgl <https://github.com/ROCm/dgl>`_ repository. 
+DGL provides a high-performance graph object that can reside on either CPUs or GPUs. 
-* Due to independent compatibility considerations, this location differs from the `https://github.com/dmlc/dgl <https://github.com/dmlc/dgl>`_ upstream repository. 
+It bundles structural data features for better control and provides a variety of functions 
-* Use the prebuilt :ref:`Docker images <dgl-docker-compat>` with DGL, PyTorch, and ROCm preinstalled.
+for computing with graph objects, including efficient and customizable message passing 
-* See the :doc:`ROCm DGL installation guide <rocm-install-on-linux:install/3rd-party/dgl-install>` 
+primitives for Graph Neural Networks.
-  to install and get started.
 
-
+Support overview
-Supported devices
 ================================================================================
 
-- **Officially Supported**: TF32 with AMD Instinct MI300X (through hipblaslt)
+- The ROCm-supported version of DGL is maintained in the official `https://github.com/ROCm/dgl 
-- **Partially Supported**: TF32 with AMD Instinct MI250X
+  <https://github.com/ROCm/dgl>`__ repository, which differs from the 
+  `https://github.com/dmlc/dgl <https://github.com/dmlc/dgl>`__ upstream repository.
 
+- To get started and install DGL on ROCm, use the prebuilt :ref:`Docker images <dgl-docker-compat>`, 
+  which include ROCm, DGL, and all required dependencies.
+
+  - See the :doc:`ROCm DGL installation guide <rocm-install-on-linux:install/3rd-party/dgl-install>` 
+    for installation and setup instructions.
+
+  - You can also consult the upstream `Installation guide <https://www.dgl.ai/pages/start.html>`__ 
+    for additional context.
+
+Version support
+--------------------------------------------------------------------------------
+
+DGL is supported on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
+
+Supported devices
+--------------------------------------------------------------------------------
+
+- **Officially Supported**: AMD Instinct™ MI300X (through `hipBLASlt <https://rocm.docs.amd.com/projects/hipBLASLt/en/latest/index.html>`__)
+- **Partially Supported**: AMD Instinct™ MI250X
 
 .. _dgl-recommendations:
 
@@ -35,7 +53,7 @@ Use cases and recommendations
 ================================================================================
 
 DGL can be used for Graph Learning, and building popular graph models like  
-GAT, GCN and GraphSage. Using these we can support a variety of use-cases such as:
+GAT, GCN, and GraphSage. Using these models, a variety of use cases are supported:
 
 - Recommender systems
 - Network Optimization and Analysis
@@ -62,16 +80,17 @@ Docker image compatibility
 
    <i class="fab fa-docker"></i>
 
-AMD validates and publishes `DGL images <https://hub.docker.com/r/rocm/dgl>`_
+AMD validates and publishes `DGL images <https://hub.docker.com/r/rocm/dgl/tags>`__
-with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
+with ROCm backends on Docker Hub. The following Docker image tags and associated
-inventories were tested on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`_.
+inventories represent the latest available DGL version from the official Docker Hub. 
 Click the |docker-icon| to view the image on Docker Hub.
 
 .. list-table:: DGL Docker image components
     :header-rows: 1
     :class: docker-image-compatibility
 
-    * - Docker
+    * - Docker image
+      - ROCm
       - DGL
       - PyTorch
       - Ubuntu
@@ -81,102 +100,106 @@ Click the |docker-icon| to view the image on Docker Hub.
 
            <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0/images/sha256-8ce2c3bcfaa137ab94a75f9e2ea711894748980f57417739138402a542dd5564"><i class="fab fa-docker fa-lg"></i></a>
 
-      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
+      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
-      - `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`_
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`__
       - 24.04
-      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
+      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`__
 
     * - .. raw:: html
 
            <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.4.1/images/sha256-cf1683283b8eeda867b690229c8091c5bbf1edb9f52e8fb3da437c49a612ebe4"><i class="fab fa-docker fa-lg"></i></a>
 
-      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
+      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
-      - `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`__
       - 24.04
-      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
+      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`__
 
 
     * - .. raw:: html
 
            <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.4.1/images/sha256-4834f178c3614e2d09e89e32041db8984c456d45dfd20286e377ca8635686554"><i class="fab fa-docker fa-lg"></i></a>
 
-      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
+      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
-      - `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`__
       - 22.04
-      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
+      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`__
 
 
     * - .. raw:: html
 
            <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.3.0/images/sha256-88740a2c8ab4084b42b10c3c6ba984cab33dd3a044f479c6d7618e2b2cb05e69"><i class="fab fa-docker fa-lg"></i></a>
 
-      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
+      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
-      - `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`_
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`__
       - 22.04
-      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
+      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`__
       
 
 Key ROCm libraries for DGL
 ================================================================================
 
 DGL on ROCm depends on specific libraries that affect its features and performance.
-Using the DGL Docker container or building it with the provided docker file or a ROCm base image is recommended.
+Using the DGL Docker container or building it with the provided Docker file or a ROCm base image is recommended.
 If you prefer to build it yourself, ensure the following dependencies are installed:
 
 .. list-table:: 
     :header-rows: 1
 
     * - ROCm library
-      - Version
+      - ROCm 6.4.0 Version
       - Purpose
     * - `Composable Kernel <https://github.com/ROCm/composable_kernel>`_
-      - :version-ref:`"Composable Kernel" rocm_version`
+      - 1.1.0
       - Enables faster execution of core operations like matrix multiplication
         (GEMM), convolutions and transformations.
     * - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
-      - :version-ref:`hipBLAS rocm_version`
+      - 2.4.0
       - Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
         matrix and vector operations.
     * - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
-      - :version-ref:`hipBLASLt rocm_version`
+      - 0.12.0
       - hipBLASLt is an extension of the hipBLAS library, providing additional
         features like epilogues fused into the matrix multiplication kernel or
         use of integer tensor cores.
     * - `hipCUB <https://github.com/ROCm/hipCUB>`_
-      - :version-ref:`hipCUB rocm_version`
+      - 3.4.0
       - Provides a C++ template library for parallel algorithms for reduction,
         scan, sort and select.
     * - `hipFFT <https://github.com/ROCm/hipFFT>`_
-      - :version-ref:`hipFFT rocm_version`
+      - 1.0.18
       - Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
     * - `hipRAND <https://github.com/ROCm/hipRAND>`_
-      - :version-ref:`hipRAND rocm_version`
+      - 2.12.0
       - Provides fast random number generation for GPUs.
     * - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
-      - :version-ref:`hipSOLVER rocm_version`
+      - 2.4.0
       - Provides GPU-accelerated solvers for linear systems, eigenvalues, and
         singular value decompositions (SVD).
     * - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
-      - :version-ref:`hipSPARSE rocm_version`
+      - 3.2.0
       - Accelerates operations on sparse matrices, such as sparse matrix-vector
         or matrix-matrix products.
     * - `hipSPARSELt <https://github.com/ROCm/hipSPARSELt>`_
-      - :version-ref:`hipSPARSELt rocm_version`
+      - 0.2.3
       - Accelerates operations on sparse matrices, such as sparse matrix-vector
         or matrix-matrix products.
     * - `hipTensor <https://github.com/ROCm/hipTensor>`_
-      - :version-ref:`hipTensor rocm_version`
+      - 1.5.0
       - Optimizes for high-performance tensor operations, such as contractions.
     * - `MIOpen <https://github.com/ROCm/MIOpen>`_
-      - :version-ref:`MIOpen rocm_version`
+      - 3.4.0
       - Optimizes deep learning primitives such as convolutions, pooling,
         normalization, and activation functions.
     * - `MIGraphX <https://github.com/ROCm/AMDMIGraphX>`_
-      - :version-ref:`MIGraphX rocm_version`
+      - 2.12.0
       - Adds graph-level optimizations, ONNX models and mixed precision support
         and enable Ahead-of-Time (AOT) Compilation.
     * - `MIVisionX <https://github.com/ROCm/MIVisionX>`_
-      - :version-ref:`MIVisionX rocm_version`
+      - 3.2.0
       - Optimizes acceleration for computer vision and AI workloads like
         preprocessing, augmentation, and inferencing.
     * - `rocAL <https://github.com/ROCm/rocAL>`_
@@ -184,25 +207,25 @@ If you prefer to build it yourself, ensure the following dependencies are instal
       - Accelerates the data pipeline by offloading intensive preprocessing and
         augmentation tasks. rocAL is part of MIVisionX.
     * - `RCCL <https://github.com/ROCm/rccl>`_
-      - :version-ref:`RCCL rocm_version`
+      - 2.2.0
       - Optimizes for multi-GPU communication for operations like AllReduce and
         Broadcast.
     * - `rocDecode <https://github.com/ROCm/rocDecode>`_
-      - :version-ref:`rocDecode rocm_version`
+      - 0.10.0
       - Provides hardware-accelerated data decoding capabilities, particularly
         for image, video, and other dataset formats.
     * - `rocJPEG <https://github.com/ROCm/rocJPEG>`_
-      - :version-ref:`rocJPEG rocm_version`
+      - 0.8.0
       - Provides hardware-accelerated JPEG image decoding and encoding.
     * - `RPP <https://github.com/ROCm/RPP>`_
-      - :version-ref:`RPP rocm_version`
+      - 1.9.10
       - Speeds up data augmentation, transformation, and other preprocessing steps.
     * - `rocThrust <https://github.com/ROCm/rocThrust>`_
-      - :version-ref:`rocThrust rocm_version`
+      - 3.3.0
       - Provides a C++ template library for parallel algorithms like sorting,
         reduction, and scanning.
     * - `rocWMMA <https://github.com/ROCm/rocWMMA>`_
-      - :version-ref:`rocWMMA rocm_version`
+      - 1.7.0
       - Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
         multiplication (GEMM) and accumulation operations with mixed precision
         support.
@@ -211,14 +234,14 @@ If you prefer to build it yourself, ensure the following dependencies are instal
 Supported features
 ================================================================================
 
-Many functions and methods available in DGL Upstream are also supported in DGL ROCm.
+Many functions and methods available upstream are also supported in DGL on ROCm.
 Instead of listing them all, support is grouped into the following categories to provide a general overview. 
 
 * DGL Base
 * DGL Backend 
 * DGL Data
 * DGL Dataloading
-* DGL DGLGraph
+* DGL Graph
 * DGL Function
 * DGL Ops
 * DGL Sampling
@@ -235,9 +258,9 @@ Instead of listing them all, support is grouped into the following categories to
 Unsupported features
 ================================================================================
 
-* Graphbolt
+* GraphBolt
-* Partial TF32 Support (MI250x only)
+* Partial TF32 Support (MI250X only)
-* Kineto/ ROCTracer integration
+* Kineto/ROCTracer integration
 
 
 Unsupported functions

docs/compatibility/ml-compatibility/flashinfer-compatibility.rst

@@ -1,8 +1,8 @@
 :orphan:
 
 .. meta::
-    :description: FlashInfer deep learning framework compatibility
+    :description: FlashInfer compatibility
-    :keywords: GPU, LLM, FlashInfer, compatibility
+    :keywords: GPU, LLM, FlashInfer, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -11,7 +11,7 @@ FlashInfer compatibility
 ********************************************************************************
 
 `FlashInfer <https://docs.flashinfer.ai/index.html>`__ is a library and kernel generator 
-for Large Language Models (LLMs) that provides high-performance implementation of graphics 
+for Large Language Models (LLMs) that provides a high-performance implementation of graphics 
 processing units (GPUs) kernels. FlashInfer focuses on LLM serving and inference, as well 
 as advanced performance across diverse scenarios.
 
@@ -25,28 +25,30 @@ offers high-performance LLM-specific operators, with easy integration through Py
   For the latest feature compatibility matrix, refer to the ``README`` of the 
   `https://github.com/ROCm/flashinfer <https://github.com/ROCm/flashinfer>`__ repository.
 
-Support for the ROCm port of FlashInfer is available as follows:
+Support overview
+================================================================================
 
-- ROCm support for FlashInfer is hosted in the `https://github.com/ROCm/flashinfer 
+- The ROCm-supported version of FlashInfer is maintained in the official `https://github.com/ROCm/flashinfer 
-  <https://github.com/ROCm/flashinfer>`__ repository. This location differs from the 
+  <https://github.com/ROCm/flashinfer>`__ repository, which differs from the 
-  `https://github.com/flashinfer-ai/flashinfer <https://github.com/flashinfer-ai/flashinfer>`_ 
+  `https://github.com/flashinfer-ai/flashinfer <https://github.com/flashinfer-ai/flashinfer>`__ 
   upstream repository.
 
-- To install FlashInfer, use the prebuilt :ref:`Docker image <flashinfer-docker-compat>`, 
+- To get started and install FlashInfer on ROCm, use the prebuilt :ref:`Docker images <flashinfer-docker-compat>`, 
-  which includes ROCm, FlashInfer, and all required dependencies.
+  which include ROCm, FlashInfer, and all required dependencies.
 
   - See the :doc:`ROCm FlashInfer installation guide <rocm-install-on-linux:install/3rd-party/flashinfer-install>` 
-    to install and get started.
+    for installation and setup instructions.
 
-  - See the `Installation guide <https://docs.flashinfer.ai/installation.html>`__ 
+  - You can also consult the upstream `Installation guide <https://docs.flashinfer.ai/installation.html>`__ 
-    in the upstream FlashInfer documentation.
+    for additional context.
 
-.. note::
+Version support
+--------------------------------------------------------------------------------
 
-  Flashinfer is supported on ROCm 6.4.1.
+FlashInfer is supported on `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__.
 
 Supported devices
-================================================================================
+--------------------------------------------------------------------------------
 
 **Officially Supported**: AMD Instinct™ MI300X
 
@@ -78,10 +80,9 @@ Docker image compatibility
 
    <i class="fab fa-docker"></i>
 
-AMD validates and publishes `ROCm FlashInfer images <https://hub.docker.com/r/rocm/flashinfer/tags>`__
+AMD validates and publishes `FlashInfer images <https://hub.docker.com/r/rocm/flashinfer/tags>`__
-with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
+with ROCm backends on Docker Hub. The following Docker image tag and associated
-inventories represent the FlashInfer version from the official Docker Hub.
+inventories represent the latest available FlashInfer version from the official Docker Hub. 
-The Docker images have been validated for `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__.
 Click |docker-icon| to view the image on Docker Hub.
 
 .. list-table:: 

docs/compatibility/ml-compatibility/jax-compatibility.rst

@@ -2,7 +2,7 @@
 
 .. meta::
    :description: JAX compatibility
-   :keywords: GPU, JAX compatibility
+   :keywords: GPU, JAX, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -10,42 +10,38 @@
 JAX compatibility
 *******************************************************************************
 
-JAX provides a NumPy-like API, which combines automatic differentiation and the
+`JAX <https://docs.jax.dev/en/latest/notebooks/thinking_in_jax.html>`__ is a library 
+for array-oriented numerical computation (similar to NumPy), with automatic differentiation 
+and just-in-time (JIT) compilation to enable high-performance machine learning research.
+
+JAX provides an API that combines automatic differentiation and the 
 Accelerated Linear Algebra (XLA) compiler to achieve high-performance machine 
-learning at scale.
+learning at scale. JAX uses composable transformations of Python and NumPy through 
+JIT compilation, automatic vectorization, and parallelization.
 
-JAX uses composable transformations of Python and NumPy through just-in-time
+Support overview
-(JIT) compilation, automatic vectorization, and parallelization. To learn about
+================================================================================
-JAX, including profiling and optimizations, see the official `JAX documentation
-<https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_.
 
-ROCm support for JAX is upstreamed, and users can build the official source code
+- The ROCm-supported version of JAX is maintained in the official `https://github.com/ROCm/rocm-jax 
-with ROCm support:
+  <https://github.com/ROCm/rocm-jax>`__ repository, which differs from the 
+  `https://github.com/jax-ml/jax <https://github.com/jax-ml/jax>`__ upstream repository.
 
-- ROCm JAX release:
+- To get started and install JAX on ROCm, use the prebuilt :ref:`Docker images <jax-docker-compat>`, 
-
+  which include ROCm, JAX, and all required dependencies.
-  - Offers AMD-validated and community :ref:`Docker images <jax-docker-compat>`
-    with ROCm and JAX preinstalled.
-
-  - ROCm JAX repository: `ROCm/rocm-jax <https://github.com/ROCm/rocm-jax>`_
 
   - See the :doc:`ROCm JAX installation guide <rocm-install-on-linux:install/3rd-party/jax-install>` 
-    to get started.
+    for installation and setup instructions.
 
-- Official JAX release:
+  - You can also consult the upstream `Installation guide <https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`__ 
+    for additional context.
 
-  - Official JAX repository: `jax-ml/jax <https://github.com/jax-ml/jax>`_
+Version support
+--------------------------------------------------------------------------------
 
-  - See the `AMD GPU (Linux) installation section
+AMD releases official `ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax/tags>`_
-    <https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`_ in
+quarterly alongside new ROCm releases. These images undergo full AMD testing.
-    the JAX documentation.
+`Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community/tags>`_
-
+follow upstream JAX releases and use the latest available ROCm version.
-.. note::
-
-   AMD releases official `ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax>`_
-   quarterly alongside new ROCm releases. These images undergo full AMD testing.
-   `Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
-   follow upstream JAX releases and use the latest available ROCm version.
 
 Use cases and recommendations
 ================================================================================
@@ -71,7 +67,7 @@ Use cases and recommendations
 * The `Distributed fine-tuning with JAX on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/distributed-sft-jax/README.html>`_
   outlines the process of fine-tuning a Bidirectional Encoder Representations
   from Transformers (BERT)-based large language model (LLM) using JAX for a text
-  classification task. The blog post discuss techniques for parallelizing the
+  classification task. The blog post discusses techniques for parallelizing the
   fine-tuning across multiple AMD GPUs and assess the model's performance on a
   holdout dataset. During the fine-tuning, a BERT-base-cased transformer model
   and the General Language Understanding Evaluation (GLUE) benchmark dataset was
@@ -90,9 +86,9 @@ For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.b
 Docker image compatibility
 ================================================================================
 
-AMD provides preconfigured Docker images with JAX and the ROCm backend.
+AMD validates and publishes `JAX images <https://hub.docker.com/r/rocm/jax/tags>`__
-These images are published on `Docker Hub <https://hub.docker.com/r/rocm/jax>`__ and are the
+with ROCm backends on Docker Hub.
-recommended way to get started with deep learning with JAX on ROCm.
+
 For ``jax-community`` images, see `rocm/jax-community
 <https://hub.docker.com/r/rocm/jax-community/tags>`__ on Docker Hub.
 
@@ -234,7 +230,7 @@ The ROCm supported data types in JAX are collected in the following table.
 
 .. note::
 
-  JAX data type support is effected by the :ref:`key_rocm_libraries` and it's
+  JAX data type support is affected by the :ref:`key_rocm_libraries` and it's
   collected on :doc:`ROCm data types and precision support <rocm:reference/precision-support>`
   page.
 

docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst

@@ -1,8 +1,8 @@
 :orphan:
 
 .. meta::
-    :description: llama.cpp deep learning framework compatibility
+    :description: llama.cpp compatibility
-    :keywords: GPU, GGML, llama.cpp compatibility
+    :keywords: GPU, GGML, llama.cpp, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -20,33 +20,32 @@ to accelerate inference and reduce memory usage. Originally built as a CPU-first
 llama.cpp is easy to integrate with other programming environments and is widely 
 adopted across diverse platforms, including consumer devices. 
 
-ROCm support for llama.cpp is upstreamed, and you can build the official source code
+Support overview
-with ROCm support:
-
-- ROCm support for llama.cpp is hosted in the official `https://github.com/ROCm/llama.cpp 
-  <https://github.com/ROCm/llama.cpp>`_ repository.
-
-- Due to independent compatibility considerations, this location differs from the 
-  `https://github.com/ggml-org/llama.cpp <https://github.com/ggml-org/llama.cpp>`_ upstream repository.
-
-- To install llama.cpp, use the prebuilt :ref:`Docker image <llama-cpp-docker-compat>`, 
-  which includes ROCm, llama.cpp, and all required dependencies.
-
-  - See the :doc:`ROCm llama.cpp installation guide <rocm-install-on-linux:install/3rd-party/llama-cpp-install>` 
-    to install and get started.
-
-  - See the `Installation guide <https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md#hip>`__ 
-    in the upstream llama.cpp documentation.
-
-.. note::
-
-  llama.cpp is supported on ROCm 7.0.0 and ROCm 6.4.x.
-
-Supported devices
 ================================================================================
 
-**Officially Supported**: AMD Instinct™ MI300X, MI325X, MI210
+- The ROCm-supported version of llama.cpp is maintained in the official `https://github.com/ROCm/llama.cpp 
+  <https://github.com/ROCm/llama.cpp>`__ repository, which differs from the 
+  `https://github.com/ggml-org/llama.cpp <https://github.com/ggml-org/llama.cpp>`__ upstream repository.
 
+- To get started and install llama.cpp on ROCm, use the prebuilt :ref:`Docker images <llama-cpp-docker-compat>`, 
+  which include ROCm, llama.cpp, and all required dependencies.
+
+  - See the :doc:`ROCm llama.cpp installation guide <rocm-install-on-linux:install/3rd-party/llama-cpp-install>` 
+    for installation and setup instructions.
+
+  - You can also consult the upstream `Installation guide <https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md>`__ 
+    for additional context.
+
+Version support
+--------------------------------------------------------------------------------
+
+llama.cpp is supported on `ROCm 7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__ and 
+`ROCm 6.4.x <https://repo.radeon.com/rocm/apt/6.4/>`__.
+
+Supported devices
+--------------------------------------------------------------------------------
+
+**Officially Supported**: AMD Instinct™ MI300X, MI325X, MI210
 
 Use cases and recommendations
 ================================================================================
@@ -84,9 +83,9 @@ Docker image compatibility
 
    <i class="fab fa-docker"></i>
 
-AMD validates and publishes `ROCm llama.cpp Docker images <https://hub.docker.com/r/rocm/llama.cpp/tags>`__
+AMD validates and publishes `llama.cpp images <https://hub.docker.com/r/rocm/llama.cpp/tags>`__
 with ROCm backends on Docker Hub. The following Docker image tags and associated
-inventories represent the available llama.cpp versions from the official Docker Hub.
+inventories represent the latest available llama.cpp versions from the official Docker Hub.
 Click |docker-icon| to view the image on Docker Hub.
 
 .. important::

docs/compatibility/ml-compatibility/megablocks-compatibility.rst

@@ -2,7 +2,7 @@
 
 .. meta::
     :description: Megablocks compatibility
-    :keywords: GPU, megablocks, compatibility
+    :keywords: GPU, megablocks, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -10,28 +10,42 @@
 Megablocks compatibility
 ********************************************************************************
 
-Megablocks is a light-weight library for mixture-of-experts (MoE) training. 
+`Megablocks <https://github.com/databricks/megablocks>`__ is a lightweight library 
+for mixture-of-experts `(MoE) <https://huggingface.co/blog/moe>`__ training. 
 The core of the system is efficient "dropless-MoE" and standard MoE layers. 
-Megablocks is integrated with `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_, 
+Megablocks is integrated with `https://github.com/stanford-futuredata/Megatron-LM 
+<https://github.com/stanford-futuredata/Megatron-LM>`__, 
 where data and pipeline parallel training of MoEs is supported.
 
-* ROCm support for Megablocks is hosted in the official `https://github.com/ROCm/megablocks <https://github.com/ROCm/megablocks>`_ repository. 
+Support overview
-* Due to independent compatibility considerations, this location differs from the `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_ upstream repository. 
+================================================================================
-* Use the prebuilt :ref:`Docker image <megablocks-docker-compat>` with ROCm, PyTorch, and Megablocks preinstalled. 
-* See the :doc:`ROCm Megablocks installation guide <rocm-install-on-linux:install/3rd-party/megablocks-install>` to install and get started.
 
-.. note::
+- The ROCm-supported version of Megablocks is maintained in the official `https://github.com/ROCm/megablocks 
+  <https://github.com/ROCm/megablocks>`__ repository, which differs from the 
+  `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`__ upstream repository.
 
-  Megablocks is supported on ROCm 6.3.0.
+- To get started and install Megablocks on ROCm, use the prebuilt :ref:`Docker image <megablocks-docker-compat>`, 
+  which includes ROCm, Megablocks, and all required dependencies.
+
+  - See the :doc:`ROCm Megablocks installation guide <rocm-install-on-linux:install/3rd-party/megablocks-install>` 
+    for installation and setup instructions.
+
+  - You can also consult the upstream `Installation guide <https://github.com/databricks/megablocks>`__ 
+    for additional context.
+
+Version support
+--------------------------------------------------------------------------------
+
+Megablocks is supported on `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`__.
 
 Supported devices
-================================================================================
+--------------------------------------------------------------------------------
 
-- **Officially Supported**: AMD Instinct MI300X
+- **Officially Supported**: AMD Instinct™ MI300X
-- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210
+- **Partially Supported** (functionality or performance limitations): AMD Instinct™ MI250X, MI210
 
 Supported models and features
-================================================================================
+--------------------------------------------------------------------------------
 
 This section summarizes the Megablocks features supported by ROCm.
 
@@ -41,20 +55,28 @@ This section summarizes the Megablocks features supported by ROCm.
 * Mixture-of-Experts
 * dropless-Mixture-of-Experts
 
-
 .. _megablocks-recommendations:
 
 Use cases and recommendations
 ================================================================================
 
-The `ROCm Megablocks blog posts <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`_ 
+* The `Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs 
-guide how to leverage the ROCm platform for pre-training using the Megablocks framework. 
+  <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`__ 
+  blog post guides how to leverage the ROCm platform for pre-training using the 
+  Megablocks framework. It introduces a streamlined approach for training Mixture-of-Experts 
+  (MoE) models using the Megablocks library on AMD hardware. Focusing on GPT-2, it 
+  demonstrates how block-sparse computations can enhance scalability and efficiency in MoE 
+  training. The guide provides step-by-step instructions for setting up the environment, 
+  including cloning the repository, building the Docker image, and running the training container. 
+  Additionally, it offers insights into utilizing the ``oscar-1GB.json`` dataset for pre-training 
+  language models. By leveraging Megablocks and the ROCm platform, you can optimize your MoE 
+  training workflows for large-scale transformer models.
+
 It features how to pre-process datasets and how to begin pre-training on AMD GPUs through:
 
 * Single-GPU pre-training
 * Multi-GPU pre-training
 
-
 .. _megablocks-docker-compat:
 
 Docker image compatibility
@@ -64,10 +86,9 @@ Docker image compatibility
 
    <i class="fab fa-docker"></i>
 
-AMD validates and publishes `ROCm Megablocks images <https://hub.docker.com/r/rocm/megablocks/tags>`_
+AMD validates and publishes `Megablocks images <https://hub.docker.com/r/rocm/megablocks/tags>`__
-with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
+with ROCm backends on Docker Hub. The following Docker image tag and associated
-inventories represent the latest Megatron-LM version from the official Docker Hub.
+inventories represent the latest available Megablocks version from the official Docker Hub. 
-The Docker images have been validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
 Click |docker-icon| to view the image on Docker Hub.
 
 .. list-table:: 

docs/compatibility/ml-compatibility/pytorch-compatibility.rst

@@ -2,7 +2,7 @@
 
 .. meta::
     :description: PyTorch compatibility
-    :keywords: GPU, PyTorch compatibility
+    :keywords: GPU, PyTorch, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -15,40 +15,42 @@ deep learning. PyTorch on ROCm provides mixed-precision and large-scale training
 using `MIOpen <https://github.com/ROCm/MIOpen>`__ and
 `RCCL <https://github.com/ROCm/rccl>`__ libraries.
 
-ROCm support for PyTorch is upstreamed into the official PyTorch repository. Due
+PyTorch provides two high-level features:
-to independent compatibility considerations, this results in two distinct
-release cycles for PyTorch on ROCm:
 
-- ROCm PyTorch release:
+- Tensor computation (like NumPy) with strong GPU acceleration
 
-  - Provides the latest version of ROCm but might not necessarily support the
+- Deep neural networks built on a tape-based autograd system (rapid computation 
-    latest stable PyTorch version.
+  of multiple partial derivatives or gradients)
 
-  - Offers :ref:`Docker images <pytorch-docker-compat>` with ROCm and PyTorch
+Support overview
-    preinstalled.
+================================================================================
 
-  - ROCm PyTorch repository: `<https://github.com/ROCm/pytorch>`__
+ROCm support for PyTorch is upstreamed into the official PyTorch repository. 
+ROCm development is aligned with the stable release of PyTorch, while upstream 
+PyTorch testing uses the stable release of ROCm to maintain consistency:
+
+- The ROCm-supported version of PyTorch is maintained in the official `https://github.com/ROCm/pytorch 
+  <https://github.com/ROCm/pytorch>`__ repository, which differs from the 
+  `https://github.com/pytorch/pytorch <https://github.com/pytorch/pytorch>`__ upstream repository.
+
+- To get started and install PyTorch on ROCm, use the prebuilt :ref:`Docker images <pytorch-docker-compat>`, 
+  which include ROCm, PyTorch, and all required dependencies.
 
   - See the :doc:`ROCm PyTorch installation guide <rocm-install-on-linux:install/3rd-party/pytorch-install>` 
-    to get started.
+    for installation and setup instructions.
 
-- Official PyTorch release:
+  - You can also consult the upstream `Installation guide <https://pytorch.org/get-started/locally/>`__ or 
-
+    `Previous versions <https://pytorch.org/get-started/previous-versions/>`__ for additional context.
-  - Provides the latest stable version of PyTorch  but might not necessarily
-    support the latest ROCm version.
-
-  - Official PyTorch repository: `<https://github.com/pytorch/pytorch>`__
-
-  - See the `Nightly and latest stable version installation guide <https://pytorch.org/get-started/locally/>`__
-    or `Previous versions <https://pytorch.org/get-started/previous-versions/>`__
-    to get started.
 
 PyTorch includes tooling that generates HIP source code from the CUDA backend.
 This approach allows PyTorch to support ROCm without requiring manual code
 modifications. For more information, see :doc:`HIPIFY <hipify:index>`.
 
-ROCm development is aligned with the stable release of PyTorch, while upstream
+Version support
-PyTorch testing uses the stable release of ROCm to maintain consistency.
+--------------------------------------------------------------------------------
+
+AMD releases official `ROCm PyTorch Docker images <https://hub.docker.com/r/rocm/pytorch/tags>`_
+quarterly alongside new ROCm releases. These images undergo full AMD testing.
 
 .. _pytorch-recommendations:
 
@@ -78,7 +80,7 @@ Use cases and recommendations
   GPU.
 
 * The :doc:`Inception with PyTorch documentation </conceptual/ai-pytorch-inception>`
-  describes how PyTorch integrates with ROCm for AI workloads It outlines the
+  describes how PyTorch integrates with ROCm for AI workloads. It outlines the
   use of PyTorch on the ROCm platform and focuses on efficiently leveraging AMD
   GPU hardware for training and inference tasks in AI applications.
 
@@ -89,9 +91,8 @@ For more use cases and recommendations, see `ROCm PyTorch blog posts <https://ro
 Docker image compatibility
 ================================================================================
 
-AMD provides preconfigured Docker images with PyTorch and the ROCm backend.
+AMD validates and publishes `PyTorch images <https://hub.docker.com/r/rocm/pytorch/tags>`__
-These images are published on `Docker Hub <https://hub.docker.com/r/rocm/pytorch>`__ and are the
+with ROCm backends on Docker Hub.
-recommended way to get started with deep learning with PyTorch on ROCm.
 
 To find the right image tag, see the :ref:`PyTorch on ROCm installation
 documentation <rocm-install-on-linux:pytorch-docker-support>` for a list of

docs/compatibility/ml-compatibility/ray-compatibility.rst

@@ -1,8 +1,8 @@
 :orphan:
 
 .. meta::
-    :description: Ray deep learning framework compatibility
+    :description: Ray compatibility
-    :keywords: GPU, Ray compatibility
+    :keywords: GPU, Ray, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -19,36 +19,35 @@ simplifying machine learning computations.
 Ray is a general-purpose framework that runs many types of workloads efficiently. 
 Any Python application can be scaled with Ray, without extra infrastructure.
 
-ROCm support for Ray is upstreamed, and you can build the official source code
+Support overview
-with ROCm support: 
-
-- ROCm support for Ray is hosted in the official `https://github.com/ROCm/ray 
-  <https://github.com/ROCm/ray>`_ repository.
-
-- Due to independent compatibility considerations, this location differs from the 
-  `https://github.com/ray-project/ray <https://github.com/ray-project/ray>`_ upstream repository.
-
-- To install Ray, use the prebuilt :ref:`Docker image <ray-docker-compat>` 
-  which includes ROCm, Ray, and all required dependencies.
-
-  - See the :doc:`ROCm Ray installation guide <rocm-install-on-linux:install/3rd-party/ray-install>` 
-    for instructions to get started.
-
-  - See the `Installation section <https://docs.ray.io/en/latest/ray-overview/installation.html>`_ 
-    in the upstream Ray documentation.
-
-  - The Docker image provided is based on the upstream Ray `Daily Release (Nightly) wheels <https://docs.ray.io/en/latest/ray-overview/installation.html#daily-releases-nightlies>`__ 
-    corresponding to commit `005c372 <https://github.com/ray-project/ray/commit/005c372262e050d5745f475e22e64305fa07f8b8>`__.
-
-.. note::
-
-  Ray is supported on ROCm 6.4.1.
-
-Supported devices
 ================================================================================
 
-**Officially Supported**: AMD Instinct™ MI300X, MI210
+- The ROCm-supported version of Ray is maintained in the official `https://github.com/ROCm/ray 
+  <https://github.com/ROCm/ray>`__ repository, which differs from the 
+  `https://github.com/ray-project/ray <https://github.com/ray-project/ray>`__ upstream repository.
 
+- To get started and install Ray on ROCm, use the prebuilt :ref:`Docker image <ray-docker-compat>`, 
+  which includes ROCm, Ray, and all required dependencies.
+
+  - The Docker image provided is based on the upstream Ray `Daily Release (Nightly) wheels 
+    <https://docs.ray.io/en/latest/ray-overview/installation.html#daily-releases-nightlies>`__ 
+    corresponding to commit `005c372 <https://github.com/ray-project/ray/commit/005c372262e050d5745f475e22e64305fa07f8b8>`__.
+
+  - See the :doc:`ROCm Ray installation guide <rocm-install-on-linux:install/3rd-party/ray-install>` 
+    for installation and setup instructions.
+
+  - You can also consult the upstream `Installation guide <https://docs.ray.io/en/latest/ray-overview/installation.html>`__ 
+    for additional context.
+
+Version support
+--------------------------------------------------------------------------------
+
+Ray is supported on `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__.
+
+Supported devices
+--------------------------------------------------------------------------------
+
+**Officially Supported**: AMD Instinct™ MI300X, MI210
 
 Use cases and recommendations
 ================================================================================
@@ -88,15 +87,15 @@ Docker image compatibility
 
 AMD validates and publishes ready-made `ROCm Ray Docker images <https://hub.docker.com/r/rocm/ray/tags>`__
 with ROCm backends on Docker Hub. The following Docker image tags and
-associated inventories represent the latest Ray version from the official Docker Hub and are validated for
+associated inventories represent the latest Ray version from the official Docker Hub.
-`ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`_. Click the |docker-icon|
+Click the |docker-icon| icon to view the image on Docker Hub.
-icon to view the image on Docker Hub.
 
 .. list-table::
     :header-rows: 1
     :class: docker-image-compatibility
 
     * - Docker image
+      - ROCm
       - Ray
       - Pytorch
       - Ubuntu
@@ -105,6 +104,7 @@ icon to view the image on Docker Hub.
     * - .. raw:: html
 
            <a href="https://hub.docker.com/layers/rocm/ray/ray-2.48.0.post0_rocm6.4.1_ubuntu24.04_py3.12_pytorch2.6.0/images/sha256-0d166fe6bdced38338c78eedfb96eff92655fb797da3478a62dd636365133cc0"><i class="fab fa-docker fa-lg"></i> rocm/ray</a>
+      - `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__.
       - `2.48.0.post0 <https://github.com/ROCm/ray/tree/release/2.48.0.post0>`_
       - 2.6.0+git684f6f2
       - 24.04

docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst

@@ -2,7 +2,7 @@
 
 .. meta::
     :description: Stanford Megatron-LM compatibility
-    :keywords: Stanford, Megatron-LM, compatibility
+    :keywords: Stanford, Megatron-LM, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -10,34 +10,50 @@
 Stanford Megatron-LM compatibility
 ********************************************************************************
 
-Stanford Megatron-LM is a large-scale language model training framework developed by NVIDIA `https://github.com/NVIDIA/Megatron-LM <https://github.com/NVIDIA/Megatron-LM>`_. It is
+Stanford Megatron-LM is a large-scale language model training framework developed 
-designed to train massive transformer-based language models efficiently by model and data parallelism. 
+by NVIDIA at `https://github.com/NVIDIA/Megatron-LM <https://github.com/NVIDIA/Megatron-LM>`_. 
+It is designed to train massive transformer-based language models efficiently by model 
+and data parallelism. 
 
-* ROCm support for Stanford Megatron-LM is hosted in the official `https://github.com/ROCm/Stanford-Megatron-LM <https://github.com/ROCm/Stanford-Megatron-LM>`_ repository. 
+It provides efficient tensor, pipeline, and sequence-based model parallelism for 
-* Due to independent compatibility considerations, this location differs from the `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_ upstream repository. 
+pre-training transformer-based language models such as GPT (Decoder Only), BERT 
-* Use the prebuilt :ref:`Docker image <megatron-lm-docker-compat>` with ROCm, PyTorch, and Megatron-LM preinstalled. 
+(Encoder Only), and T5 (Encoder-Decoder). 
-* See the :doc:`ROCm Stanford Megatron-LM installation guide <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>` to install and get started.
 
-.. note::
+Support overview
-
-	Stanford Megatron-LM is supported on ROCm 6.3.0.
-
-
-Supported Devices
 ================================================================================
 
-- **Officially Supported**: AMD Instinct MI300X
+- The ROCm-supported version of Stanford Megatron-LM is maintained in the official `https://github.com/ROCm/Stanford-Megatron-LM 
-- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210
+  <https://github.com/ROCm/Stanford-Megatron-LM>`__ repository, which differs from the 
+  `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`__ upstream repository.
 
+- To get started and install Stanford Megatron-LM on ROCm, use the prebuilt :ref:`Docker image <megatron-lm-docker-compat>`, 
+  which includes ROCm, Stanford Megatron-LM, and all required dependencies.
+
+  - See the :doc:`ROCm Stanford Megatron-LM installation guide <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>` 
+    for installation and setup instructions.
+
+  - You can also consult the upstream `Installation guide <https://github.com/NVIDIA/Megatron-LM>`__ 
+    for additional context.
+
+Version support
+--------------------------------------------------------------------------------
+
+Stanford Megatron-LM is supported on `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`__.
+
+Supported devices
+--------------------------------------------------------------------------------
+
+- **Officially Supported**: AMD Instinct™ MI300X
+- **Partially Supported** (functionality or performance limitations): AMD Instinct™ MI250X, MI210
 
 Supported models and features
-================================================================================
+--------------------------------------------------------------------------------
 
 This section details models & features that are supported by the ROCm version on Stanford Megatron-LM.
 
 Models:
 
-* Bert
+* BERT
 * GPT
 * T5
 * ICT
@@ -54,13 +70,24 @@ Features:
 Use cases and recommendations
 ================================================================================
 
-See the `Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs blog <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`_ post  
+The following blog post mentions Megablocks, but you can run Stanford Megatron-LM with the same steps to pre-process datasets on AMD GPUs:
-to leverage the ROCm platform for pre-training by using the Stanford Megatron-LM framework of pre-processing datasets on AMD GPUs. 
-Coverage includes:
 
-  * Single-GPU pre-training
+* The `Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs 
-  * Multi-GPU pre-training
+  <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`__ 
+  blog post guides how to leverage the ROCm platform for pre-training using the 
+  Megablocks framework. It introduces a streamlined approach for training Mixture-of-Experts 
+  (MoE) models using the Megablocks library on AMD hardware. Focusing on GPT-2, it 
+  demonstrates how block-sparse computations can enhance scalability and efficiency in MoE 
+  training. The guide provides step-by-step instructions for setting up the environment, 
+  including cloning the repository, building the Docker image, and running the training container. 
+  Additionally, it offers insights into utilizing the ``oscar-1GB.json`` dataset for pre-training 
+  language models. By leveraging Megablocks and the ROCm platform, you can optimize your MoE 
+  training workflows for large-scale transformer models.
 
+It features how to pre-process datasets and how to begin pre-training on AMD GPUs through:
+
+* Single-GPU pre-training
+* Multi-GPU pre-training
 
 .. _megatron-lm-docker-compat:
 
@@ -71,10 +98,9 @@ Docker image compatibility
 
    <i class="fab fa-docker"></i>
 
-AMD validates and publishes `Stanford Megatron-LM images <https://hub.docker.com/r/rocm/megatron-lm>`_
+AMD validates and publishes `Stanford Megatron-LM images <https://hub.docker.com/r/rocm/stanford-megatron-lm/tags>`_
 with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
-inventories represent the latest Megatron-LM version from the official Docker Hub.
+inventories represent the latest Stanford Megatron-LM version from the official Docker Hub.
-The Docker images have been validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
 Click |docker-icon| to view the image on Docker Hub.
 
 .. list-table:: 
@@ -82,6 +108,7 @@ Click |docker-icon| to view the image on Docker Hub.
     :class: docker-image-compatibility
 
     * - Docker image
+      - ROCm
       - Stanford Megatron-LM
       - PyTorch
       - Ubuntu
@@ -91,6 +118,7 @@ Click |docker-icon| to view the image on Docker Hub.
 
            <a href="https://hub.docker.com/layers/rocm/stanford-megatron-lm/stanford-megatron-lm85f95ae_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-070556f078be10888a1421a2cb4f48c29f28b02bfeddae02588d1f7fc02a96a6"><i class="fab fa-docker fa-lg"></i></a>
 
+      - `6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_
       - `85f95ae <https://github.com/stanford-futuredata/Megatron-LM/commit/85f95aef3b648075fe6f291c86714fdcbd9cd1f5>`_
       - `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
       - 24.04

docs/compatibility/ml-compatibility/taichi-compatibility.rst

@@ -2,7 +2,7 @@
 
 .. meta::
     :description: Taichi compatibility
-    :keywords: GPU, Taichi compatibility
+    :keywords: GPU, Taichi, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -19,28 +19,52 @@ Taichi is widely used across various domains, including real-time physical simul
 numerical computing, augmented reality, artificial intelligence, computer vision, robotics, 
 visual effects in film and gaming, and general-purpose computing.
 
-* ROCm support for Taichi is hosted in the official `https://github.com/ROCm/taichi <https://github.com/ROCm/taichi>`_ repository.
+Support overview
-* Due to independent compatibility considerations, this location differs from the `https://github.com/taichi-dev <https://github.com/taichi-dev>`_ upstream repository.
+================================================================================
-* Use the prebuilt :ref:`Docker image <taichi-docker-compat>` with ROCm, PyTorch, and Taichi preinstalled.
-* See the :doc:`ROCm Taichi installation guide <rocm-install-on-linux:install/3rd-party/taichi-install>` to install and get started.
 
-.. note::
+- The ROCm-supported version of Taichi is maintained in the official `https://github.com/ROCm/taichi 
+  <https://github.com/ROCm/taichi>`__ repository, which differs from the 
+  `https://github.com/taichi-dev/taichi <https://github.com/taichi-dev/taichi>`__ upstream repository.
 
-	Taichi is supported on ROCm 6.3.2.
+- To get started and install Taichi on ROCm, use the prebuilt :ref:`Docker image <taichi-docker-compat>`, 
+  which includes ROCm, Taichi, and all required dependencies.
 
-Supported devices and features
+  - See the :doc:`ROCm Taichi installation guide <rocm-install-on-linux:install/3rd-party/taichi-install>` 
-===============================================================================
+    for installation and setup instructions.
-There is support through the ROCm software stack for all Taichi GPU features on AMD Instinct MI250X and MI210X Series GPUs with the exception of Taichi’s GPU rendering system, CGUI.
+
-AMD Instinct MI300X Series GPUs will be supported by November.
+  - You can also consult the upstream `Installation guide <https://github.com/taichi-dev/taichi>`__ 
+    for additional context.
+
+Version support
+--------------------------------------------------------------------------------
+
+Taichi is supported on `ROCm 6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`__.
+
+Supported devices
+--------------------------------------------------------------------------------
+
+- **Officially Supported**: AMD Instinct™ MI250X, MI210X (with the exception of Taichi’s GPU rendering system, CGUI)
+- **Upcoming Support**: AMD Instinct™ MI300X
 
 .. _taichi-recommendations:
 
 Use cases and recommendations
 ================================================================================
-To fully leverage Taichi's performance capabilities in compute-intensive tasks, it is best to adhere to specific coding patterns and utilize Taichi decorators. 
+
-A collection of example use cases is available in the `https://github.com/ROCm/taichi_examples <https://github.com/ROCm/taichi_examples>`_ repository, 
+* The `Accelerating Parallel Programming in Python with Taichi Lang on AMD GPUs 
-providing practical insights and foundational knowledge for working with the Taichi programming language. 
+  <https://rocm.blogs.amd.com/artificial-intelligence/taichi/README.html>`__
-You can also refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_ to search for Taichi examples and best practices to optimize your workflows on AMD GPUs.
+  blog highlights Taichi as an open-source programming language designed for high-performance 
+  numerical computation, particularly in domains like real-time physical simulation, 
+  artificial intelligence, computer vision, robotics, and visual effects. Taichi 
+  is embedded in Python and uses just-in-time (JIT) compilation frameworks like 
+  LLVM to optimize execution on GPUs and CPUs. The blog emphasizes the versatility 
+  of Taichi in enabling complex simulations and numerical algorithms, making 
+  it ideal for developers working on compute-intensive tasks. Developers are 
+  encouraged to follow recommended coding patterns and utilize Taichi decorators 
+  for performance optimization, with examples available in the `https://github.com/ROCm/taichi_examples 
+  <https://github.com/ROCm/taichi_examples>`_ repository. Prebuilt Docker images 
+  integrating ROCm, PyTorch, and Taichi are provided for simplified installation 
+  and deployment, making it easier to leverage Taichi for advanced computational workloads.
 
 .. _taichi-docker-compat:
 
@@ -52,9 +76,8 @@ Docker image compatibility
    <i class="fab fa-docker"></i>
 
 AMD validates and publishes ready-made `ROCm Taichi Docker images <https://hub.docker.com/r/rocm/taichi/tags>`_
-with ROCm backends on Docker Hub. The following Docker image tags and associated inventories 
+with ROCm backends on Docker Hub. The following Docker image tag and associated inventories 
 represent the latest Taichi version from the official Docker Hub.
-The Docker images have been validated for `ROCm 6.3.2 <https://rocm.docs.amd.com/en/docs-6.3.2/about/release-notes.html>`_. 
 Click |docker-icon| to view the image on Docker Hub.
 
 .. list-table:: 

docs/compatibility/ml-compatibility/tensorflow-compatibility.rst

@@ -2,7 +2,7 @@
 
 .. meta::
     :description: TensorFlow compatibility
-    :keywords: GPU, TensorFlow compatibility
+    :keywords: GPU, TensorFlow, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -12,37 +12,33 @@ TensorFlow compatibility
 
 `TensorFlow <https://www.tensorflow.org/>`__ is an open-source library for
 solving machine learning, deep learning, and AI problems. It can solve many
-problems across different sectors and industries but primarily focuses on
+problems across different sectors and industries, but primarily focuses on
-neural network training and inference. It is one of the most popular and
+neural network training and inference. It is one of the most popular deep 
-in-demand frameworks and is very active in open-source contribution and
+learning frameworks and is very active in open-source development.
-development.
+
+Support overview
+================================================================================
+
+- The ROCm-supported version of TensorFlow is maintained in the official `https://github.com/ROCm/tensorflow-upstream 
+  <https://github.com/ROCm/tensorflow-upstream>`__ repository, which differs from the 
+  `https://github.com/tensorflow/tensorflow <https://github.com/tensorflow/tensorflow>`__ upstream repository.
+
+- To get started and install TensorFlow on ROCm, use the prebuilt :ref:`Docker images <tensorflow-docker-compat>`, 
+  which include ROCm, TensorFlow, and all required dependencies.
+
+  - See the :doc:`ROCm TensorFlow installation guide <rocm-install-on-linux:install/3rd-party/tensorflow-install>` 
+    for installation and setup instructions.
+
+  - You can also consult the `TensorFlow API versions <https://www.tensorflow.org/versions>`__ list 
+    for additional context.
+
+Version support
+--------------------------------------------------------------------------------
 
 The `official TensorFlow repository <http://github.com/tensorflow/tensorflow>`__
 includes full ROCm support. AMD maintains a TensorFlow `ROCm repository
 <http://github.com/rocm/tensorflow-upstream>`__ in order to quickly add bug
-fixes, updates, and support for the latest ROCM versions.
+fixes, updates, and support for the latest ROCm versions.
-
-- ROCm TensorFlow release:
-
-  - Offers :ref:`Docker images <tensorflow-docker-compat>` with
-    ROCm and TensorFlow pre-installed.
-
-  - ROCm TensorFlow repository: `<https://github.com/ROCm/tensorflow-upstream>`__
-
-  - See the :doc:`ROCm TensorFlow installation guide <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
-    to get started.
-
-- Official TensorFlow release:
-
-  - Official TensorFlow repository: `<https://github.com/tensorflow/tensorflow>`__
-
-  - See the `TensorFlow API versions <https://www.tensorflow.org/versions>`__ list.
-
-  .. note::
-
-     The official TensorFlow documentation does not cover ROCm support. Use the
-     ROCm documentation for installation instructions for Tensorflow on ROCm.
-     See :doc:`rocm-install-on-linux:install/3rd-party/tensorflow-install`.
 
 .. _tensorflow-docker-compat:
 

docs/compatibility/ml-compatibility/verl-compatibility.rst

@@ -2,7 +2,7 @@
 
 .. meta::
    :description: verl compatibility
-   :keywords: GPU, verl compatibility
+   :keywords: GPU, verl, deep learning, framework compatibility
 
 .. version-set:: rocm_version latest
 
@@ -10,24 +10,58 @@
 verl compatibility
 *******************************************************************************
 
-Volcano Engine Reinforcement Learning for LLMs (verl) is a reinforcement learning framework designed for large language models (LLMs). 
+Volcano Engine Reinforcement Learning for LLMs (`verl <https://verl.readthedocs.io/en/latest/>`__)  
-verl offers a scalable, open-source fine-tuning solution optimized for AMD Instinct GPUs with full ROCm support.
+is a reinforcement learning framework designed for large language models (LLMs). 
+verl offers a scalable, open-source fine-tuning solution by using a hybrid programming model 
+that makes it easy to define and run complex post-training dataflows efficiently. 
 
-* See the `verl documentation <https://verl.readthedocs.io/en/latest/>`_ for more information about verl. 
+Its modular APIs separate computation from data, allowing smooth integration with other frameworks. 
-* The official verl GitHub repository is `https://github.com/volcengine/verl <https://github.com/volcengine/verl>`_.
+It also supports flexible model placement across GPUs for efficient scaling on different cluster sizes.
-* Use the AMD-validated :ref:`Docker images <verl-docker-compat>` with ROCm and verl preinstalled. 
+verl achieves high training and generation throughput by building on existing LLM frameworks. 
-* See the :doc:`ROCm verl installation guide <rocm-install-on-linux:install/3rd-party/verl-install>` to install and get started.
+Its 3D-HybridEngine reduces memory use and communication overhead when switching between training 
+and inference, improving overall performance.
 
-.. note::
+Support overview
+================================================================================
 
-	verl is supported on ROCm 6.2.0.
+- The ROCm-supported version of verl is maintained in the official `https://github.com/ROCm/verl 
+  <https://github.com/ROCm/verl>`__ repository, which differs from the 
+  `https://github.com/volcengine/verl <https://github.com/volcengine/verl>`__ upstream repository.
+
+- To get started and install verl on ROCm, use the prebuilt :ref:`Docker image <verl-docker-compat>`, 
+  which includes ROCm, verl, and all required dependencies.
+
+  - See the :doc:`ROCm verl installation guide <rocm-install-on-linux:install/3rd-party/verl-install>` 
+    for installation and setup instructions.
+
+  - You can also consult the upstream `verl documentation <https://verl.readthedocs.io/en/latest/>`__ 
+    for additional context.
+
+Version support
+--------------------------------------------------------------------------------
+
+verl is supported on `ROCm 6.2.0 <https://repo.radeon.com/rocm/apt/6.2/>`__.
+
+Supported devices
+--------------------------------------------------------------------------------
+
+**Officially Supported**: AMD Instinct™ MI300X
 
 .. _verl-recommendations:
 
 Use cases and recommendations
 ================================================================================
 
-The benefits of verl in large-scale reinforcement learning from human feedback (RLHF) are discussed in the `Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`_ blog.
+* The benefits of verl in large-scale reinforcement learning from human feedback 
+  (RLHF) are discussed in the `Reinforcement Learning from Human Feedback on AMD 
+  GPUs with verl and ROCm Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`__ 
+  blog. The blog post outlines how the Volcano Engine Reinforcement Learning 
+  (verl) framework integrates with the AMD ROCm platform to optimize training on 
+  Instinct™ MI300X GPUs. The guide details the process of building a Docker image, 
+  setting up single-node and multi-node training environments, and highlights 
+  performance benchmarks demonstrating improved throughput and convergence accuracy. 
+  This resource serves as a comprehensive starting point for deploying verl on AMD GPUs, 
+  facilitating efficient RLHF training workflows.
 
 .. _verl-supported_features:
 
@@ -61,8 +95,10 @@ Docker image compatibility
 
    <i class="fab fa-docker"></i>
 
-AMD validates and publishes ready-made `ROCm verl Docker images <https://hub.docker.com/r/rocm/verl/tags>`_
+AMD validates and publishes ready-made `verl Docker images <https://hub.docker.com/r/rocm/verl/tags>`_
-with ROCm backends on Docker Hub. The following Docker image tags and associated inventories represent the available verl versions from the official Docker Hub. 
+with ROCm backends on Docker Hub. The following Docker image tag and associated inventories 
+represent the latest verl version from the official Docker Hub. 
+Click |docker-icon| to view the image on Docker Hub.
 
 .. list-table:: 
     :header-rows: 1