bump rocm-docs-core version to 1.26.0

* 6.4.0 updates synced

* Minor change

.wordlist.txt

@@ -34,6 +34,7 @@ Autocast
 BARs
 BLAS
 BMC
+BabelStream
 Blit
 Blockwise
 Bluefield
@@ -138,6 +139,7 @@ GDR
 GDS
 GEMM
 GEMMs
+GFLOPS
 GFortran
 GFXIP
 Gemma
@@ -641,6 +643,7 @@ hipSPARSELt
 hipTensor
 hipamd
 hipblas
+hipcc
 hipcub
 hipfft
 hipfort

RELEASE.md

@@ -260,7 +260,7 @@ Click {fab}`github` to go to the component's source code on GitHub.
                 <td><a href="https://github.com/ROCm/rccl"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-            <td><a href="https://github.com/ROCm/rocSHMEM">rocSHMEM</a></td>
+            <td><a href="https://rocm.docs.amd.com/projects/rocSHMEM/en/docs-6.4.0/index.html">rocSHMEM</a></td>
                 <td>2.0.0</td>
                 <td><a href="https://github.com/ROCm/rocSHMEM"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
@@ -777,7 +777,7 @@ and in-depth descriptions.
 #### Optimized
 
 * `hipGraphLaunch` parallelism is improved for complex data-parallel graphs.
-* Make the round-robin queue selection in command scheduling. For multi-streams execution, HSA queue from null stream lock is freed and won't occupy the queue ID after the kernel in the stream is finished.
+* Round-robin queue mechanism is updated for command scheduling. For multi-streams execution, HSA queue from null stream lock is freed and won't occupy the queue ID after the kernel in the stream is finished.
 * The HIP runtime doesn't free bitcode object before code generation. It adds a cache, which allows compiled code objects to be reused instead of recompiling. This improves performance on multi-GPU systems.
 * Runtime now uses unified copy approach:
 
@@ -786,6 +786,11 @@ and in-depth descriptions.
     - The default environment variable `GPU_FORCE_BLIT_COPY_SIZE` is set to `16`, which limits the kernel copy to sizes less than 16 KB, while copies larger than that would be handled by `SDMA` engine.
     - Blit code is refactored, and ASAN instrumentation is cleaned up.
 
+* HIP runtime uses signals without interrupts:
+
+    - In active wait mode, uses signals without interrupts by default.
+    - Only when a callback is required, switches to the interrupts.
+
 #### Resolved issues
 
 * Out-of-memory error on Microsoft Windows. When the user calls `hipMalloc` for device memory allocation while specifying a size larger than the available device memory, the HIP runtime fixes the error in the API implementation, allocating the available device memory plus system memory (shared virtual memory).
@@ -796,13 +801,15 @@ and in-depth descriptions.
 
 The following lists the backward incompatible changes planned for upcoming major ROCm releases.
 
-* Signature changes in APIs to correspond with NVIDIA CUDA APIs,
+* Signature changes in APIs to match corresponding CUDA APIs,
 
     - `hiprtcCreateProgram`
     - `hiprtcCompileProgram`
     - `hipCtxGetApiVersion`
 
 * Behavior of `hipPointerGetAttributes` is changed to match corresponding CUDA API in version 11 and later releases.
+* Behavior of `hipFree` is changed to match corresponding CUDA API `cudaFree`.
+* HIP vector constructor changes for `hipComplex`.
 * Return error/value code updates in the following hip APIs to match the corresponding CUDA APIs,
 
     - `hipModuleLaunchKernel`
@@ -1763,4 +1770,5 @@ There are a number of upcoming changes planned for HIP runtime API in an upcomin
 that are not backward compatible with prior releases. Most of these changes increase 
 alignment between HIP and CUDA APIs or behavior. Some of the upcoming changes are to 
 clean up header files, remove namespace collision, and have a clear separation between 
-`hipRTC` and HIP runtime. For more information refer to [HIP Upcoming changes](#hip-6-4-0).
+`hipRTC` and HIP runtime. For more information, see [HIP Upcoming changes](#hip-6-4-0)
+or [HIP 7.0 Is Coming: What You Need to Know to Stay Ahead](https://rocm.blogs.amd.com/ecosystems-and-partners/transition-to-hip-7.0-blog/README.html).

docs/compatibility/compatibility-matrix-historical-6.0.csv

@@ -37,7 +37,7 @@ ROCm Version,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.5, 6.1.2
       CUB,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
 ,,,,,,,,,,,,,,,
       KMD & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,
-      KMD versions,"6.4.x, 6.3.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
+      :doc:`KMD versions <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
       ,,,,,,,,,,,,,,,
       ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,
       :doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0
@@ -52,7 +52,7 @@ ROCm Version,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.5, 6.1.2
       ,,,,,,,,,,,,,,,
       COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,
       :doc:`RCCL <rccl:index>`,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
-`rocSHMEM <https://github.com/ROCm/rocSHMEM>`_,2.0.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`rocSHMEM <rocSHMEM:index>`,2.0.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
       ,,,,,,,,,,,,,,,
       MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,
       `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0

docs/compatibility/compatibility-matrix.rst

@@ -62,7 +62,7 @@ compatibility and system requirements.
       CUB,2.5.0,2.3.2,2.2.0
       ,,,
       KMD & USER SPACE [#kfd_support]_,.. _kfd-userspace-support-compatibility-matrix:,,
-      KMD versions,"6.4.x, 6.3.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x"
+      :doc:`KMD versions <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x"
       ,,,
       ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix:,,
       :doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0
@@ -77,7 +77,7 @@ compatibility and system requirements.
       ,,,
       COMMUNICATION,.. _commlibs-support-compatibility-matrix:,,
       :doc:`RCCL <rccl:index>`,2.22.3,2.21.5,2.20.5
-      `rocSHMEM <https://github.com/ROCm/rocSHMEM>`_ ,2.0.0,N/A,N/A
+      :doc:`rocSHMEM <rocSHMEM:index>`,2.0.0,N/A,N/A
       ,,,
       MATH LIBS,.. _mathlibs-support-compatibility-matrix:,,
       `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0
@@ -151,7 +151,7 @@ compatibility and system requirements.
 .. [#mi300x] Oracle Linux and Azure Linux are supported only on AMD Instinct MI300X.
 .. [#single-node] Debian 12 is supported only on AMD Instinct MI300X for single-node functionality. 
 .. [#mi300_620] **For ROCm 6.2.0** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
-.. [#kfd_support] Starting from ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software is provided up to a year apart (assuming hardware support is available in both). For earlier ROCm releases, the compatibility is provided for +/- 2 releases. These are the compatibility combinations that are currently supported.
+.. [#kfd_support] As of ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The tested user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and kernel-space support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
 .. [#ROCT-rocr] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.
 
 .. _OS-kernel-versions:
@@ -229,5 +229,5 @@ Expand for full historical view of:
    .. [#mi300_610-past-60] **For ROCm 6.1.0** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.4.
    .. [#mi300_602-past-60] **For ROCm 6.0.2** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.3.
    .. [#mi300_600-past-60] **For ROCm 6.0.0** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.3.
-   .. [#kfd_support-past-60] Starting from ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software is provided up to a year apart (assuming hardware support is available in both). For earlier ROCm releases, the compatibility is provided for +/- 2 releases. These are the compatibility combinations that are currently supported.
+   .. [#kfd_support-past-60] As of ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The tested user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and kernel-space support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
    .. [#ROCT-rocr-past-60] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.

docs/conf.py

@@ -51,6 +51,8 @@ article_pages = [
     {"file": "how-to/deep-learning-rocm", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/index", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/install", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/system-health-check", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/training/index", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/train-a-model", "os": ["linux"]},
@@ -67,7 +69,6 @@ article_pages = [
     {"file": "how-to/rocm-for-ai/fine-tuning/multi-gpu-fine-tuning-and-inference", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/inference/index", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/inference/install", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/hugging-face-models", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/llm-inference-frameworks", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/vllm-benchmark", "os": ["linux"]},

docs/how-to/rocm-for-ai/inference/pytorch-inference-benchmark.rst

@@ -62,47 +62,52 @@ PyTorch inference performance testing
       {% endfor %}
    {% endfor %}
 
-   Getting started
-   ===============
+   System validation
+   =================
 
-   Use the following procedures to reproduce the benchmark results on an
-   MI300X series accelerator with the prebuilt PyTorch Docker image.
+   Before running AI workloads, it's important to validate that your AMD hardware is configured
+   correctly and performing optimally.
 
-   .. _pytorch-benchmark-get-started:
+   To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
+   might hang until the periodic balancing is finalized. For more information,
+   see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
 
-   1. Disable NUMA auto-balancing.
+   .. code-block:: shell
 
-      To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
-      might hang until the periodic balancing is finalized. For more information,
-      see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
+      # disable automatic NUMA balancing
+      sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
+      # check if NUMA balancing is disabled (returns 0 if disabled)
+      cat /proc/sys/kernel/numa_balancing
+      0
 
-      .. code-block:: shell
+   To test for optimal performance, consult the recommended :ref:`System health benchmarks
+   <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+   system's configuration.
 
-         # disable automatic NUMA balancing
-         sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
-         # check if NUMA balancing is disabled (returns 0 if disabled)
-         cat /proc/sys/kernel/numa_balancing
-         0
+   Pull the Docker image
+   =====================
 
    .. container:: model-doc pyt_chai1_inference
 
-      2. Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue/images/sha256-b736a4239ab38a9d0e448af6d4adca83b117debed00bfbe33846f99c4540f79b>`_ from Docker Hub.
+      Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue/images/sha256-b736a4239ab38a9d0e448af6d4adca83b117debed00bfbe33846f99c4540f79b>`_ from Docker Hub.
 
-         .. code-block:: shell
+      .. code-block:: shell
 
-            docker pull rocm/pytorch:rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue
+         docker pull rocm/pytorch:rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue
 
-         .. note::
+      .. note::
 
-            The Chai-1 benchmark uses a specifically selected Docker image using ROCm 6.2.3 and PyTorch 2.3.0 to address an accuracy issue.
+         The Chai-1 benchmark uses a specifically selected Docker image using ROCm 6.2.3 and PyTorch 2.3.0 to address an accuracy issue.
 
    .. container:: model-doc pyt_clip_inference
 
-      2. Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/latest/images/sha256-05b55983e5154f46e7441897d0908d79877370adca4d1fff4899d9539d6c4969>`_ from Docker Hub.
+      Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/latest/images/sha256-05b55983e5154f46e7441897d0908d79877370adca4d1fff4899d9539d6c4969>`_ from Docker Hub.
 
-         .. code-block:: shell
+      .. code-block:: shell
 
-            docker pull rocm/pytorch:latest
+         docker pull rocm/pytorch:latest
+
+   .. _pytorch-benchmark-get-started:
 
    Benchmarking
    ============

docs/how-to/rocm-for-ai/inference/vllm-benchmark.rst

@@ -109,37 +109,39 @@ vLLM inference performance testing
    ==================================
 
    For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
-   see the developer's guide at `<https://github.com/ROCm/vllm/blob/main/docs/dev-docker/README.md>`__.
+   see the developer's guide at `<https://github.com/ROCm/vllm/tree/7a9f58aae0e7215a5f3dccde60e35072c41656c2/docs/dev-docker>`__.
 
-   Getting started
-   ===============
+   System validation
+   =================
 
-   Use the following procedures to reproduce the benchmark results on an
-   MI300X accelerator with the prebuilt vLLM Docker image.
+   Before running AI workloads, it's important to validate that your AMD hardware is configured
+   correctly and performing optimally.
 
-   .. _vllm-benchmark-get-started:
+   To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
+   might hang until the periodic balancing is finalized. For more information,
+   see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
 
-   1. Disable NUMA auto-balancing.
+   .. code-block:: shell
 
-      To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
-      might hang until the periodic balancing is finalized. For more information,
-      see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
+      # disable automatic NUMA balancing
+      sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
+      # check if NUMA balancing is disabled (returns 0 if disabled)
+      cat /proc/sys/kernel/numa_balancing
+      0
 
-      .. code-block:: shell
+   To test for optimal performance, consult the recommended :ref:`System health benchmarks
+   <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+   system's configuration.
 
-         # disable automatic NUMA balancing
-         sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
-         # check if NUMA balancing is disabled (returns 0 if disabled)
-         cat /proc/sys/kernel/numa_balancing
-         0
+   Pull the Docker image
+   =====================
 
-   2. Download the `ROCm vLLM Docker image <{{ unified_docker.docker_hub_url }}>`_.
+   Download the `ROCm vLLM Docker image <{{ unified_docker.docker_hub_url }}>`_.
+   Use the following command to pull the Docker image from Docker Hub.
 
-      Use the following command to pull the Docker image from Docker Hub.
+   .. code-block:: shell
 
-      .. code-block:: shell
-
-         docker pull {{ unified_docker.pull_tag }}
+      docker pull {{ unified_docker.pull_tag }}
 
    Benchmarking
    ============

docs/how-to/rocm-for-ai/install.rst

@@ -28,9 +28,9 @@ ROCm supports multiple :doc:`installation methods <rocm-install-on-linux:install
 
 * :doc:`Using your Linux distribution's package manager <rocm-install-on-linux:install/install-methods/package-manager-index>`
 
-* :doc:`Using the AMDGPU installer <rocm-install-on-linux:install/amdgpu-install>`
+* :doc:`Using the AMDGPU installer <rocm-install-on-linux:install/install-methods/amdgpu-installer-index>`
 
-* :ref:`Multi-version installation <rocm-install-on-linux:installation-types>`.
+* :ref:`Multi-version installation <rocm-install-on-linux:installation-types>`
 
 .. grid:: 1
 
@@ -59,4 +59,8 @@ images with the framework pre-installed.
 
 * :doc:`JAX for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
 
-The sections that follow in :doc:`Training a model <../training/train-a-model>` are geared for a ROCm with PyTorch installation.
+Next steps
+==========
+
+After installing ROCm and your desired ML libraries -- and before running AI workloads -- conduct system health benchmarks
+to test the optimal performance of your AMD hardware. See :doc:`system-health-check` to get started.

docs/how-to/rocm-for-ai/system-health-check.rst

@@ -0,0 +1,104 @@
+.. meta::
+   :description: System health checks with RVS, RCCL tests, BabelStream, and TransferBench to validate AMD hardware performance running AI workloads.
+   :keywords: gpu, accelerator, system, health, validation, bench, perf, performance, rvs, rccl, babel, mi300x, mi325x, flops, bandwidth, rbt, training, inference
+
+.. _rocm-for-ai-system-health-bench:
+
+************************
+System health benchmarks
+************************
+
+Before running AI workloads, it is important to validate that your AMD hardware is configured correctly and is performing optimally. This topic outlines several system health benchmarks you can use to test key aspects like GPU compute capabilities (FLOPS), memory bandwidth, and interconnect performance. Many of these tests are part of the ROCm Validation Suite (RVS).
+
+ROCm Validation Suite (RVS) tests
+=================================
+
+RVS provides a collection of tests, benchmarks, and qualification tools, each
+targeting a specific subsystem of the system under test. It includes tests for
+GPU stress and memory bandwidth.
+
+.. _healthcheck-install-rvs:
+
+Install ROCm Validation Suite
+-----------------------------
+
+To get started, install RVS. For example, on an Ubuntu system with ROCm already
+installed, run the following command:
+
+.. code-block:: shell
+
+   sudo apt update
+   sudo apt install rocm-validation-suite
+
+See the `ROCm Validation Suite installation instructions <https://rocm.docs.amd.com/projects/ROCmValidationSuite/en/latest/install/installation.html>`_,
+and `System validation tests <https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/system-validation.html#system-validation-tests>`_
+in the Instinct documentation for more detailed instructions.
+
+Benchmark, stress, and qualification tests
+------------------------------------------
+
+The GPU stress test runs various GEMM computations as workloads to stress the GPU FLOPS performance and check whether it
+meets the configured target GFLOPS.
+
+Run the benchmark, stress, and qualification tests included with RVS. See the `Benchmark, stress, qualification
+<https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/system-validation.html#benchmark-stress-qualification>`_
+section of the Instinct documentation for usage instructions.
+
+BabelStream test
+----------------
+
+BabelStream is a synthetic GPU benchmark based on the STREAM benchmark for
+CPUs, measuring memory transfer rates to and from global device memory.
+BabelStream tests are included with the RVS package as part of the `BABEL module
+<https://rocm.docs.amd.com/projects/ROCmValidationSuite/en/latest/conceptual/rvs-modules.html#babel-benchmark-test-babel-module>`_.
+
+For more information, see `Performance benchmarking
+<https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/performance-bench.html#babelstream-benchmarking-results>`_
+in the Instinct documentation.
+
+RCCL tests
+==========
+
+The ROCm Communication Collectives Library (RCCL) enables efficient multi-GPU
+communication. The `<https://github.com/ROCm/rccl-tests>`__ suite benchmarks
+the performance and verifies the correctness of these collective operations.
+This helps ensure optimal scaling for multi-accelerator tasks.
+
+1. To get started, build RCCL-tests using the official instructions in the README at
+   `<https://github.com/ROCm/rccl-tests?tab=readme-ov-file#build>`__ or use the
+   following commands:
+
+   .. code-block:: shell
+
+      git clone https://github.com/ROCm/rccl-tests.git
+      cd rccl-tests
+      make
+
+2. Run the suggested RCCL tests -- see `RCCL benchmarking
+   <https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/performance-bench.html#rccl-benchmarking-results>`_
+   in the Instinct performance benchmarking documentation for instructions.
+
+TransferBench test
+==================
+
+TransferBench is a standalone utility for benchmarking simultaneous data
+transfer performance between various devices in the system, including
+CPU-to-GPU and GPU-to-GPU (peer-to-peer). This helps identify potential
+bottlenecks in data movement between the host system and the GPUs, or between
+GPUs, which can impact end-to-end latency.
+
+.. _healthcheck-install-transferbench:
+
+1. To get started, use the instructions in the `TransferBench documentation
+   <https://rocm.docs.amd.com/projects/TransferBench/en/latest/install/install.html#install-transferbench>`_
+   or use the following commands:
+
+   .. code:: shell
+
+      git clone https://github.com/ROCm/TransferBench.git
+      cd TransferBench
+      CC=hipcc make
+
+2. Run the suggested TransferBench tests -- see `TransferBench benchmarking
+   <https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/performance-bench.html#transferbench-benchmarking-results>`_
+   in the Instinct performance benchmarking documentation for instructions.

docs/how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst

@@ -79,11 +79,18 @@ across different input sequences. Support for packed input format is planned for
 System validation
 =================
 
-If you have already validated your system settings, including NUMA
-auto-balancing, skip this step. Otherwise, complete the :ref:`system validation
-and optimization steps <train-a-model-system-validation>` to set up your system
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting training.
 
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
 Environment setup
 =================
 
@@ -175,8 +182,8 @@ with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
 
 .. _amd-maxtext-download-docker:
 
-Download the Docker image
--------------------------
+Pull the Docker image
+---------------------
 
 1. Use the following command to pull the Docker image from Docker Hub.
 

docs/how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst

@@ -103,11 +103,18 @@ popular AI models.
 System validation
 =================
 
-If you have already validated your system settings, including NUMA
-auto-balancing, skip this step. Otherwise, complete the :ref:`system validation
-and optimization steps <train-a-model-system-validation>` to set up your system
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting training.
 
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
 .. _mi300x-amd-megatron-lm-training:
 
 Environment setup

docs/how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry.rst

@@ -34,11 +34,18 @@ for MPT-30B with access to detailed logs and performance metrics.
 System validation
 =================
 
-If you have already validated your system settings, including NUMA
-auto-balancing, skip this step. Otherwise, complete the :ref:`system validation
-and optimization steps <train-a-model-system-validation>` to set up your system
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting training.
 
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
 Getting started
 ===============
 

docs/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst

@@ -77,11 +77,18 @@ popular AI models.
 System validation
 =================
 
-If you have already validated your system settings, including NUMA
-auto-balancing, skip this step. Otherwise, complete the :ref:`system validation
-and optimization steps <train-a-model-system-validation>` to set up your system
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting training.
 
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
 This Docker image is optimized for specific model configurations outlined
 below. Performance can vary for other training workloads, as AMD 
 doesn’t validate configurations and run conditions outside those described.

docs/how-to/rocm-for-ai/training/index.rst

@@ -21,8 +21,12 @@ In this guide, you'll learn about:
 
 - Training a model
 
-  - :doc:`Train a model with Megatron-LM <benchmark-docker/megatron-lm>`
+  - :doc:`With Megatron-LM <benchmark-docker/megatron-lm>`
 
-  - :doc:`Train a model with PyTorch <benchmark-docker/pytorch-training>`
+  - :doc:`With PyTorch <benchmark-docker/pytorch-training>`
+
+  - :doc:`With JAX MaxText <benchmark-docker/jax-maxtext>`
+
+  - :doc:`With LLM Foundry <benchmark-docker/mpt-llm-foundry>`
 
 - :doc:`Scaling model training <scale-model-training>`

docs/how-to/rocm-for-ai/training/prerequisite-system-validation.rst

@@ -5,12 +5,13 @@
    :keywords: ROCm, AI, LLM, train, megatron, Llama, tutorial, docker, torch, pytorch, jax
 
 .. _train-a-model-system-validation:
+.. _rocm-for-ai-system-optimization:
 
-**********************************************
-Prerequisite system validation before training
-**********************************************
+**********************************************************
+Prerequisite system validation before running AI workloads
+**********************************************************
 
-Complete the following system validation and optimization steps to set up your system before starting training.
+Complete the following system validation and optimization steps to set up your system before starting training and inference.
 
 Disable NUMA auto-balancing
 ---------------------------
@@ -26,7 +27,8 @@ the output is ``1``, run the following command to disable NUMA auto-balancing.
 
    sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
 
-See :ref:`mi300x-disable-numa` for more information.
+See `Disable NUMA auto-balancing <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#disable-numa-auto-balancing>`_
+in the Instinct documentation for more information.
 
 Hardware verification with ROCm
 -------------------------------
@@ -42,7 +44,8 @@ Run the command:
 
    rocm-smi --setperfdeterminism 1900
 
-See :ref:`mi300x-hardware-verification-with-rocm` for more information.
+See `Hardware verfication for ROCm <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#hardware-verification-with-rocm>`_
+in the Instinct documentation for more information.
 
 RCCL Bandwidth Test for multi-node setups
 -----------------------------------------

docs/how-to/rocm-for-hpc/index.rst

@@ -76,14 +76,6 @@ Ubuntu versions.
           single node workstations, multi and many-core nodes, clusters of nodes via
           QMP, and classic vector computers.
 
-      * -
-        - `Grid <https://github.com/amd/InfinityHub-CI/tree/main/grid/>`_
-        - Grid is a library for lattice QCD calculations that employs a high-level data parallel
-          approach while using a number of techniques to target multiple types of parallelism.
-          The library currently supports MPI, OpenMP and short vector parallelism. The SIMD
-          instructions sets covered include SSE, AVX, AVX2, FMA4, IMCI and AVX512. Recent
-          releases expanded this support to include GPU offloading.
-
       * -
         - `MILC <https://github.com/amd/InfinityHub-CI/tree/main/milc/>`_
         - The MILC Code is a set of research codes developed by MIMD Lattice Computation
@@ -237,12 +229,18 @@ Ubuntu versions.
           of these applications.
 
       * - Tools and libraries
-        - `ROCm with GPU-aware MPI container <https://github.com/amd/InfinityHub-CI/tree/main/base-gpu-mpi-rocm-docker>`_
+        - `AMD ROCm with OpenMPI container <https://github.com/amd/InfinityHub-CI/blob/main/base-gpu-mpi-rocm-docker>`_
         - Base container for GPU-aware MPI with ROCm for HPC applications. This
           project provides a boilerplate for building and running a Docker
           container with ROCm supporting GPU-aware MPI implementations using
           OpenMPI or UCX.
-
+      
+      * - 
+        - `AMD ROCm with MPICH container <https://github.com/amd/InfinityHub-CI/blob/main/base-mpich-rocm-docker>`_
+        - Base container for GPU-aware MPI with ROCm for HPC applications. This
+          project provides a boilerplate for building and running a Docker
+          container with ROCm supporting GPU-aware MPI implementations using MPICH.
+      
       * -
         - `Kokkos <https://github.com/amd/InfinityHub-CI/tree/main/kokkos>`_
         - Kokkos is a programming model in C++ for writing performance portable

docs/how-to/setting-cus.rst

@@ -38,5 +38,5 @@ The variable parsing stops when a syntax error occurs. The erroneous set and the
 
     These environment variables only affect ROCm software, not graphics applications.
 
-Not all CU configurations are valid on all devices. For example, on devices where two CUs can be combined into a WGP (for kernels running in WGP mode), it’s not valid to disable only a single CU in a WGP. For more information about what to expect when disabling CUs, see the `Exploring AMD GPU Scheduling Details by Experimenting With “Worst Practices” <https://www.cs.unc.edu/~otternes/papers/rtsj2022.pdf>`_ paper.
+Not all CU configurations are valid on all devices. For example, on devices where two CUs can be combined into a WGP (for kernels running in WGP mode), it’s not valid to disable only a single CU in a WGP.
 

docs/reference/api-libraries.md

@@ -45,7 +45,7 @@
 (communication-libraries)=
 
 * {doc}`RCCL <rccl:index>`
-* [rocSHMEM](https://github.com/ROCm/rocSHMEM)
+* {doc}`rocSHMEM <rocSHMEM:index>`
 :::
 
 :::{grid-item-card} Math

docs/reference/gpu-arch-specs.rst

@@ -296,7 +296,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -314,7 +314,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -332,7 +332,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -350,7 +350,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -368,7 +368,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -386,7 +386,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -404,7 +404,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -422,7 +422,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -440,7 +440,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -519,7 +519,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -537,7 +537,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -555,7 +555,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -573,7 +573,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -591,7 +591,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -609,7 +609,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -627,7 +627,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -645,7 +645,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -663,7 +663,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -681,7 +681,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -699,7 +699,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -717,7 +717,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -735,7 +735,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -753,7 +753,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -771,7 +771,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -789,7 +789,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *

docs/sphinx/_toc.yml.in

@@ -36,6 +36,10 @@ subtrees:
     title: Use ROCm for AI
     subtrees:
     - entries:
+      - file: how-to/rocm-for-ai/install.rst
+        title: Installation
+      - file: how-to/rocm-for-ai/system-health-check.rst
+        title: System health benchmarks
       - file: how-to/rocm-for-ai/training/index.rst
         title: Training
         subtrees:
@@ -70,8 +74,6 @@ subtrees:
         title: Inference
         subtrees:
         - entries:
-          - file: how-to/rocm-for-ai/inference/install.rst
-            title: Installation
           - file: how-to/rocm-for-ai/inference/hugging-face-models.rst
             title: Run models from Hugging Face
           - file: how-to/rocm-for-ai/inference/llm-inference-frameworks.rst

docs/sphinx/requirements.in

@@ -1,4 +1,4 @@
-rocm-docs-core==1.18.2
+rocm-docs-core==1.26.0
 sphinx-reredirects
 sphinx-sitemap
 sphinxcontrib.datatemplates==0.11.0

docs/sphinx/requirements.txt

@@ -8,11 +8,9 @@ accessible-pygments==0.0.5
     # via pydata-sphinx-theme
 alabaster==1.0.0
     # via sphinx
-appnope==0.1.4
-    # via ipykernel
 asttokens==3.0.0
     # via stack-data
-attrs==25.3.0
+attrs==25.4.0
     # via
     #   jsonschema
     #   jupyter-cache
@@ -21,62 +19,62 @@ babel==2.17.0
     # via
     #   pydata-sphinx-theme
     #   sphinx
-beautifulsoup4==4.13.3
+beautifulsoup4==4.14.2
     # via pydata-sphinx-theme
 breathe==4.36.0
     # via rocm-docs-core
-certifi==2025.1.31
+certifi==2025.10.5
     # via requests
-cffi==1.17.1
+cffi==2.0.0
     # via
     #   cryptography
     #   pynacl
-charset-normalizer==3.4.1
+charset-normalizer==3.4.4
     # via requests
-click==8.1.8
+click==8.3.0
     # via
     #   jupyter-cache
     #   sphinx-external-toc
-comm==0.2.2
+comm==0.2.3
     # via ipykernel
-cryptography==44.0.2
+cryptography==46.0.2
     # via pyjwt
-debugpy==1.8.14
+debugpy==1.8.17
     # via ipykernel
 decorator==5.2.1
     # via ipython
 defusedxml==0.7.1
     # via sphinxcontrib-datatemplates
-deprecated==1.2.18
-    # via pygithub
 docutils==0.21.2
     # via
     #   myst-parser
     #   pydata-sphinx-theme
     #   sphinx
-exceptiongroup==1.2.2
+exceptiongroup==1.3.0
     # via ipython
-executing==2.2.0
+executing==2.2.1
     # via stack-data
-fastjsonschema==2.21.1
+fastjsonschema==2.21.2
     # via
     #   nbformat
     #   rocm-docs-core
 gitdb==4.0.12
     # via gitpython
-gitpython==3.1.44
+gitpython==3.1.45
     # via rocm-docs-core
-idna==3.10
+greenlet==3.2.4
+    # via sqlalchemy
+idna==3.11
     # via requests
 imagesize==1.4.1
     # via sphinx
-importlib-metadata==8.6.1
+importlib-metadata==8.7.0
     # via
     #   jupyter-cache
     #   myst-nb
-ipykernel==6.29.5
+ipykernel==7.0.0
     # via myst-nb
-ipython==8.35.0
+ipython==8.37.0
     # via
     #   ipykernel
     #   myst-nb
@@ -86,9 +84,9 @@ jinja2==3.1.6
     # via
     #   myst-parser
     #   sphinx
-jsonschema==4.23.0
+jsonschema==4.25.1
     # via nbformat
-jsonschema-specifications==2024.10.1
+jsonschema-specifications==2025.9.1
     # via jsonschema
 jupyter-cache==1.0.1
     # via myst-nb
@@ -96,7 +94,7 @@ jupyter-client==8.6.3
     # via
     #   ipykernel
     #   nbclient
-jupyter-core==5.7.2
+jupyter-core==5.8.1
     # via
     #   ipykernel
     #   jupyter-client
@@ -106,17 +104,17 @@ markdown-it-py==3.0.0
     # via
     #   mdit-py-plugins
     #   myst-parser
-markupsafe==3.0.2
+markupsafe==3.0.3
     # via jinja2
 matplotlib-inline==0.1.7
     # via
     #   ipykernel
     #   ipython
-mdit-py-plugins==0.4.2
+mdit-py-plugins==0.5.0
     # via myst-parser
 mdurl==0.1.2
     # via markdown-it-py
-myst-nb==1.2.0
+myst-nb==1.3.0
     # via rocm-docs-core
 myst-parser==4.0.1
     # via myst-nb
@@ -131,34 +129,33 @@ nbformat==5.10.4
     #   nbclient
 nest-asyncio==1.6.0
     # via ipykernel
-packaging==24.2
+packaging==25.0
     # via
     #   ipykernel
-    #   pydata-sphinx-theme
     #   sphinx
-parso==0.8.4
+parso==0.8.5
     # via jedi
 pexpect==4.9.0
     # via ipython
-platformdirs==4.3.7
+platformdirs==4.5.0
     # via jupyter-core
-prompt-toolkit==3.0.50
+prompt-toolkit==3.0.52
     # via ipython
-psutil==7.0.0
+psutil==7.1.0
     # via ipykernel
 ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.3
     # via stack-data
-pycparser==2.22
+pycparser==2.23
     # via cffi
-pydata-sphinx-theme==0.15.4
+pydata-sphinx-theme==0.16.1
     # via
     #   rocm-docs-core
     #   sphinx-book-theme
-pygithub==2.6.1
+pygithub==2.8.1
     # via rocm-docs-core
-pygments==2.19.1
+pygments==2.19.2
     # via
     #   accessible-pygments
     #   ipython
@@ -166,11 +163,11 @@ pygments==2.19.1
     #   sphinx
 pyjwt[crypto]==2.10.1
     # via pygithub
-pynacl==1.5.0
+pynacl==1.6.0
     # via pygithub
 python-dateutil==2.9.0.post0
     # via jupyter-client
-pyyaml==6.0.2
+pyyaml==6.0.3
     # via
     #   jupyter-cache
     #   myst-nb
@@ -178,21 +175,21 @@ pyyaml==6.0.2
     #   rocm-docs-core
     #   sphinx-external-toc
     #   sphinxcontrib-datatemplates
-pyzmq==26.4.0
+pyzmq==27.1.0
     # via
     #   ipykernel
     #   jupyter-client
-referencing==0.36.2
+referencing==0.37.0
     # via
     #   jsonschema
     #   jsonschema-specifications
-requests==2.32.3
+requests==2.32.5
     # via
     #   pygithub
     #   sphinx
-rocm-docs-core==1.18.2
+rocm-docs-core==1.26.0
     # via -r requirements.in
-rpds-py==0.24.0
+rpds-py==0.27.1
     # via
     #   jsonschema
     #   referencing
@@ -200,9 +197,9 @@ six==1.17.0
     # via python-dateutil
 smmap==5.0.2
     # via gitdb
-snowballstemmer==2.2.0
+snowballstemmer==3.0.1
     # via sphinx
-soupsieve==2.6
+soupsieve==2.8
     # via beautifulsoup4
 sphinx==8.1.3
     # via
@@ -215,12 +212,12 @@ sphinx==8.1.3
     #   sphinx-copybutton
     #   sphinx-design
     #   sphinx-external-toc
+    #   sphinx-last-updated-by-git
     #   sphinx-notfound-page
     #   sphinx-reredirects
-    #   sphinx-sitemap
     #   sphinxcontrib-datatemplates
     #   sphinxcontrib-runcmd
-sphinx-book-theme==1.1.4
+sphinx-book-theme==1.1.3
     # via rocm-docs-core
 sphinx-copybutton==0.5.2
     # via rocm-docs-core
@@ -228,11 +225,13 @@ sphinx-design==0.6.1
     # via rocm-docs-core
 sphinx-external-toc==1.0.1
     # via rocm-docs-core
+sphinx-last-updated-by-git==0.3.8
+    # via sphinx-sitemap
 sphinx-notfound-page==1.1.0
     # via rocm-docs-core
 sphinx-reredirects==0.1.6
     # via -r requirements.in
-sphinx-sitemap==2.6.0
+sphinx-sitemap==2.9.0
     # via -r requirements.in
 sphinxcontrib-applehelp==2.0.0
     # via sphinx
@@ -250,21 +249,20 @@ sphinxcontrib-runcmd==0.2.0
     # via sphinxcontrib-datatemplates
 sphinxcontrib-serializinghtml==2.0.0
     # via sphinx
-sqlalchemy==2.0.40
+sqlalchemy==2.0.44
     # via jupyter-cache
 stack-data==0.6.3
     # via ipython
 tabulate==0.9.0
     # via jupyter-cache
-tomli==2.2.1
+tomli==2.3.0
     # via sphinx
-tornado==6.4.2
+tornado==6.5.2
     # via
     #   ipykernel
     #   jupyter-client
 traitlets==5.14.3
     # via
-    #   comm
     #   ipykernel
     #   ipython
     #   jupyter-client
@@ -272,22 +270,22 @@ traitlets==5.14.3
     #   matplotlib-inline
     #   nbclient
     #   nbformat
-typing-extensions==4.13.2
+typing-extensions==4.15.0
     # via
     #   beautifulsoup4
+    #   cryptography
+    #   exceptiongroup
     #   ipython
     #   myst-nb
     #   pydata-sphinx-theme
     #   pygithub
     #   referencing
     #   sqlalchemy
-urllib3==2.4.0
+urllib3==2.5.0
     # via
     #   pygithub
     #   requests
-wcwidth==0.2.13
+wcwidth==0.2.14
     # via prompt-toolkit
-wrapt==1.17.2
-    # via deprecated
-zipp==3.21.0
+zipp==3.23.0
     # via importlib-metadata

docs/what-is-rocm.rst

@@ -52,7 +52,7 @@ Communication
   :header: "Component", "Description"
 
   ":doc:`RCCL <rccl:index>`", "Standalone library that provides multi-GPU and multi-node collective communication primitives"
-  "`rocSHMEM <https://github.com/ROCm/rocSHMEM>`_", "Runtime that provides GPU-centric networking through an OpenSHMEM-like interface. This intra-kernel networking library simplifies application code complexity and enables more fine-grained communication/computation overlap than traditional host-driven networking"
+  ":doc:`rocSHMEM <rocSHMEM:index>`", "An intra-kernel networking library that provides GPU-centric networking through an OpenSHMEM-like interface"
 
 Math
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^