Update PyTorch training benchmark docker doc to 25.7 (#5255)

* Update PyTorch training benchmark docker doc to 25.7

* update .wordlist.txt

* update conf.py

* update data sheet

* fix sphinx warnings

.azuredevops/components/HIP.yml

@@ -1,10 +1,29 @@
 parameters:
+- name: componentName
+  type: string
+  default: hip_clr_combined
 - name: checkoutRepo
   type: string
   default: 'self'
 - name: checkoutRef
   type: string
   default: ''
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -35,93 +54,24 @@ parameters:
   type: object
   default:
     - llvm-project
-
-# hip and clr are tightly-coupled
-# run this same template for both repos
-# any changes for clr should just trigger HIP pipeline
-# similarly for hipother repo, for Nvidia backend
+    - ROCR-Runtime
 
 - name: jobMatrix
   type: object
   default:
     buildJobs:
-      - { os: ubuntu2204, packageManager: apt }
-      - { os: almalinux8, packageManager: dnf }
+      - { os: ubuntu2204, packageManager: apt, platform: amd }
+      - { os: ubuntu2204, packageManager: apt, platform: nvidia }
+      - { os: almalinux8, packageManager: dnf, platform: amd }
+      - { os: almalinux8, packageManager: dnf, platform: nvidia }
 
-# HIP with AMD backend
 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
-  - job: hip_clr_combined_${{ job.os }}_amd
-    pool:
-      vmImage: 'ubuntu-22.04'
-    ${{ if eq(job.os, 'almalinux8') }}:
-      container:
-        image: rocmexternalcicd.azurecr.io/manylinux228:latest
-        endpoint: ContainerService3
-    variables:
-    - group: common
-    - template: /.azuredevops/variables-global.yml
-    workspace:
-      clean: all
-    steps:
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        pipModules: ${{ parameters.pipModules }}
-        packageManager: ${{ job.packageManager }}
-  # checkout triggering repo (either HIP or clr)
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
-      parameters:
-        checkoutRepo: ${{ parameters.checkoutRepo }}
-  # if this is triggered by HIP repo, matching repo is clr
-  # if this is triggered by clr repo, matching repo is HIP
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
-      parameters:
-        checkoutRepo: matching_repo
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
-      parameters:
-        checkoutRepo: hipother_repo
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
-      parameters:
-        checkoutRef: ${{ parameters.checkoutRef }}
-        dependencyList: ${{ parameters.rocmDependenciesAMD }}
-        aggregatePipeline: ${{ parameters.aggregatePipeline }}
-        os: ${{ job.os }}
-  # compile clr
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
-      parameters:
-        componentName: clr
-        cmakeBuildDir: '$(Build.SourcesDirectory)/clr/build'
-        cmakeSourceDir: '$(Build.SourcesDirectory)/clr'
-        os: ${{ job.os }}
-        useAmdclang: false
-        extraBuildFlags: >-
-          -DHIP_COMMON_DIR=$(Build.SourcesDirectory)/HIP
-          -DHIP_PLATFORM=amd
-          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-          -DROCM_PATH=$(Agent.BuildDirectory)/rocm
-          -DHIPCC_BIN_DIR=$(Agent.BuildDirectory)/rocm/bin
-          -DCLR_BUILD_HIP=ON
-          -DCLR_BUILD_OCL=ON
-          -GNinja
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
-      parameters:
-        artifactName: amd
-        os: ${{ job.os }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
-      parameters:
-        artifactName: amd
-        os: ${{ job.os }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
-    # - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-    #   parameters:
-    #     aptPackages: ${{ parameters.aptPackages }}
-    #     pipModules: ${{ parameters.pipModules }}
-    #     environment: amd
-
-# HIP with Nvidia backend
-- ${{ each job in parameters.jobMatrix.buildJobs }}:
-  - job: hip_clr_combined_${{ job.os }}_nvidia
+  - job: ${{ parameters.componentName }}_${{ job.os }}_${{ job.platform }}
+    ${{ if parameters.buildDependsOn }}:
+      dependsOn:
+        - ${{ each build in parameters.buildDependsOn }}:
+          - ${{ build }}_${{ job.os }}
     pool:
       vmImage: 'ubuntu-22.04'
     ${{ if eq(job.os, 'almalinux8') }}:
@@ -140,49 +90,45 @@ jobs:
         pipModules: ${{ parameters.pipModules }}
         packageManager: ${{ job.packageManager }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
-  # checkout triggering repo (either HIP or clr)
+    # full checkout of rocm-systems superrepo, we need clr, hip, and hipother
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
       parameters:
         checkoutRepo: ${{ parameters.checkoutRepo }}
-  # if this is triggered by HIP repo, matching repo is clr
-  # if this is triggered by clr repo, matching repo is HIP
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
-      parameters:
-        checkoutRepo: matching_repo
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
-      parameters:
-        checkoutRepo: hipother_repo
+        # sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
       parameters:
         checkoutRef: ${{ parameters.checkoutRef }}
-        dependencyList: ${{ parameters.rocmDependenciesNvidia }}
         aggregatePipeline: ${{ parameters.aggregatePipeline }}
         os: ${{ job.os }}
-    - script: 'ls -1R $(Agent.BuildDirectory)/rocm'
-      displayName: 'Artifact listing'
-  # compile clr
+        ${{ if eq(job.platform, 'amd') }}:
+          dependencyList: ${{ parameters.rocmDependenciesAMD }}
+        ${{ elseif eq(job.platform, 'nvidia') }}:
+          dependencyList: ${{ parameters.rocmDependenciesNvidia }}
+        ${{ if parameters.triggerDownstreamJobs }}:
+          downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
       parameters:
         componentName: clr
-        cmakeBuildDir: '$(Build.SourcesDirectory)/clr/build'
-        cmakeSourceDir: '$(Build.SourcesDirectory)/clr'
+        cmakeBuildDir: $(Agent.BuildDirectory)/s/projects/clr/build
+        cmakeSourceDir: $(Agent.BuildDirectory)/s/projects/clr
         os: ${{ job.os }}
         useAmdclang: false
         extraBuildFlags: >-
-          -DHIP_COMMON_DIR=$(Build.SourcesDirectory)/HIP
-          -DHIP_PLATFORM=nvidia
+          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
+          -DROCM_PATH=$(Agent.BuildDirectory)/rocm
           -DHIPCC_BIN_DIR=$(Agent.BuildDirectory)/rocm/bin
+          -DHIP_COMMON_DIR=$(Agent.BuildDirectory)/s/projects/hip
+          -DHIPNV_DIR=$(Agent.BuildDirectory)/s/projects/hipother/hipnv
+          -DHIP_PLATFORM=${{ job.platform }}
           -DCLR_BUILD_HIP=ON
-          -DCLR_BUILD_OCL=OFF
-          -DHIPNV_DIR=$(Build.SourcesDirectory)/hipother/hipnv
+          -DCLR_BUILD_OCL=ON
           -GNinja
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
+      parameters:
+        artifactName: ${{ job.platform }}
+        os: ${{ job.os }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
       parameters:
-        artifactName: nvidia
+        artifactName: ${{ job.platform }}
         os: ${{ job.os }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
-    # - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-    #   parameters:
-    #     aptPackages: ${{ parameters.aptPackages }}
-    #     pipModules: ${{ parameters.pipModules }}
-    #     environment: nvidia

.azuredevops/components/MIOpen.yml

@@ -150,6 +150,7 @@ jobs:
           downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
     - task: Bash@3
       displayName: Build and install other dependencies
+      retryCountOnTaskFailure: 3
       inputs:
         targetType: inline
         workingDirectory: $(Agent.BuildDirectory)/s
@@ -230,6 +231,7 @@ jobs:
           downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
     - task: Bash@3
       displayName: Build and install other dependencies
+      retryCountOnTaskFailure: 3
       inputs:
         targetType: inline
         workingDirectory: $(Agent.BuildDirectory)/s

.azuredevops/components/Tensile.yml

@@ -171,6 +171,7 @@ jobs:
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
     - task: DownloadPipelineArtifact@2
       displayName: 'Download Pipeline Wheel Files'
+      retryCountOnTaskFailure: 3
       inputs:
         itemPattern: '**/*${{ job.os }}*.whl'
         targetPath: $(Agent.BuildDirectory)

.azuredevops/components/hipBLASLt.yml

@@ -35,6 +35,8 @@ parameters:
     - ccache
     - gfortran
     - git
+    - libboost-filesystem-dev
+    - libboost-program-options-dev
     - libdrm-dev
     - liblapack-dev
     - libmsgpack-dev

.azuredevops/components/rocBLAS.yml

@@ -115,6 +115,13 @@ parameters:
 #        buildDependsOn:
 #          - rocBLAS_build
 #          - rocPRIM_build
+    # temporary rocblas->hipblas downstream path while the SOLVERs are disabled
+    - hipBLAS:
+      name: hipBLAS
+      sparseCheckoutDir: projects/hipblas
+      skipUnifiedBuild: 'false'
+      buildDependsOn:
+        - rocBLAS_build
 
 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:

.azuredevops/components/rocDecode.yml

@@ -8,6 +8,22 @@ parameters:
 - name: checkoutRef
   type: string
   default: ''
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -56,10 +72,23 @@ parameters:
     testJobs:
       - { os: ubuntu2204, packageManager: apt, target: gfx942 }
       - { os: ubuntu2204, packageManager: apt, target: gfx90a }
+- name: downstreamComponentMatrix
+  type: object
+  default:
+    - rocPyDecode:
+      name: rocPyDecode
+      sparseCheckoutDir: ''
+      skipUnifiedBuild: 'false'
+      buildDependsOn:
+        - rocDecode_build
 
 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
   - job: ${{ parameters.componentName }}_build_${{ job.os }}
+    ${{ if parameters.buildDependsOn }}:
+      dependsOn:
+        - ${{ each build in parameters.buildDependsOn }}:
+          - ${{ build }}_${{ job.os }}
     variables:
     - group: common
     - template: /.azuredevops/variables-global.yml
@@ -83,12 +112,15 @@ jobs:
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
       parameters:
         checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
       parameters:
         checkoutRef: ${{ parameters.checkoutRef }}
         dependencyList: ${{ parameters.rocmDependencies }}
         os: ${{ job.os }}
         aggregatePipeline: ${{ parameters.aggregatePipeline }}
+        ${{ if parameters.triggerDownstreamJobs }}:
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
       parameters:
         os: ${{ job.os }}
@@ -169,3 +201,15 @@ jobs:
         registerROCmPackages: true
         environment: test
         gpuTarget: ${{ job.target }}
+
+- ${{ if parameters.triggerDownstreamJobs }}:
+  - ${{ each component in parameters.downstreamComponentMatrix }}:
+    - ${{ if not(and(parameters.unifiedBuild, eq(component.skipUnifiedBuild, 'true'))) }}:
+      - template: /.azuredevops/components/${{ component.name }}.yml@pipelines_repo
+        parameters:
+          checkoutRepo: ${{ parameters.checkoutRepo }}
+          sparseCheckoutDir: ${{ component.sparseCheckoutDir }}
+          buildDependsOn: ${{ component.buildDependsOn }}
+          downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}
+          triggerDownstreamJobs: true
+          unifiedBuild: ${{ parameters.unifiedBuild }}

.azuredevops/components/rocPyDecode.yml

@@ -5,6 +5,22 @@ parameters:
 - name: checkoutRef
   type: string
   default: ''
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -47,19 +63,19 @@ parameters:
   type: object
   default:
     buildJobs:
-      - gfx942:
-        target: gfx942
-      - gfx90a:
-        target: gfx90a
+      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
+      - { os: ubuntu2204, packageManager: apt, target: gfx90a }
     testJobs:
-      - gfx942:
-        target: gfx942
-      - gfx90a:
-        target: gfx90a
+      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
+      - { os: ubuntu2204, packageManager: apt, target: gfx90a }
 
 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
   - job: rocPyDecode_build_${{ job.target }}
+    ${{ if parameters.buildDependsOn }}:
+      dependsOn:
+        - ${{ each build in parameters.buildDependsOn }}:
+          - ${{ build }}_${{ job.os }}
     variables:
     - group: common
     - template: /.azuredevops/variables-global.yml
@@ -74,16 +90,20 @@ jobs:
       parameters:
         aptPackages: ${{ parameters.aptPackages }}
         pipModules: ${{ parameters.pipModules }}
+        packageManager: ${{ job.packageManager }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
       parameters:
         checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
       parameters:
         checkoutRef: ${{ parameters.checkoutRef }}
         dependencyList: ${{ parameters.rocmDependencies }}
         gpuTarget: ${{ job.target }}
         aggregatePipeline: ${{ parameters.aggregatePipeline }}
+        ${{ if parameters.triggerDownstreamJobs }}:
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
     - task: Bash@3
       displayName: 'Save Python Package Paths'
       inputs:
@@ -190,6 +210,7 @@ jobs:
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
     - task: DownloadPipelineArtifact@2
       displayName: 'Download Pipeline Wheel Files'
+      retryCountOnTaskFailure: 3
       inputs:
         itemPattern: '**/*.whl'
         targetPath: $(Agent.BuildDirectory)

.azuredevops/components/rocm-libraries.yml

@@ -36,8 +36,10 @@ parameters:
     - gfortran
     - git
     - libdrm-dev
+    - liblapack-dev
     - libmsgpack-dev
     - libnuma-dev
+    - libopenblas-dev
     - ninja-build
     - python3-pip
     - python3-venv
@@ -46,6 +48,8 @@ parameters:
   default:
     - joblib
     - "packaging>=22.0"
+    - pytest
+    - pytest-cmake
     - --upgrade
 - name: rocmDependencies
   type: object
@@ -98,12 +102,12 @@ jobs:
     workspace:
       clean: all
     steps:
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-latest.yml
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
       parameters:
         aptPackages: ${{ parameters.aptPackages }}
         pipModules: ${{ parameters.pipModules }}
         packageManager: ${{ job.packageManager }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-latest.yml
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
       parameters:
@@ -134,12 +138,26 @@ jobs:
           rocm-libraries | ${{ job.os }} | ${{ job.target }} | $(DAY_STRING)
           rocm-libraries | ${{ job.os }} | ${{ job.target }}
           rocm-libraries | ${{ job.os }}
+    - task: Bash@3
+      displayName: Add paths for CMake and Python site-packages binaries
+      inputs:
+        targetType: inline
+        script: |
+          USER_BASE=$(python3 -m site --user-base)
+          echo "##vso[task.prependpath]$USER_BASE/bin"
+          echo "##vso[task.setvariable variable=PytestCmakePath]$USER_BASE/share/Pytest/cmake"
+        displayName: Set cmake configure paths
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
       parameters:
         os: ${{ job.os }}
         extraBuildFlags: >-
-          -DROCM_LIBRARIES_SUPERBUILD=ON
-          -GNinja
+          -D CMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor;$(PytestCmakePath)
+          -D CMAKE_INCLUDE_PATH=$(Agent.BuildDirectory)/rocm/llvm/include
+          -D CMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
+          -D CMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
+          -D CMAKE_CXX_COMPILER_LAUNCHER=ccache
+          -D CMAKE_C_COMPILER_LAUNCHER=ccache
+          -G Ninja
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
       parameters:
         componentName: ${{ parameters.componentName }}

.azuredevops/components/rocprofiler-compute.yml

@@ -1,10 +1,29 @@
 parameters:
+- name: componentName
+  type: string
+  default: rocprofiler-compute
 - name: checkoutRepo
   type: string
   default: 'self'
 - name: checkoutRef
   type: string
   default: ''
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -78,6 +97,10 @@ parameters:
 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
   - job: rocprofiler_compute_build_${{ job.target }}
+    ${{ if parameters.buildDependsOn }}:
+      dependsOn:
+        - ${{ each build in parameters.buildDependsOn }}:
+          - ${{ build }}_${{ job.os }}_${{ job.target }}
     variables:
     - group: common
     - template: /.azuredevops/variables-global.yml
@@ -94,15 +117,19 @@ jobs:
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
       parameters:
         checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
       parameters:
         extraBuildFlags: >-
           -GNinja
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
       parameters:
+        componentName: ${{ parameters.componentName }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
         gpuTarget: ${{ job.target }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
       parameters:
+        componentName: ${{ parameters.componentName }}
         gpuTarget: ${{ job.target }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
     # - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -111,78 +138,83 @@ jobs:
     #     pipModules: ${{ parameters.pipModules }}
     #     gpuTarget: ${{ job.target }}
 
-- ${{ each job in parameters.jobMatrix.testJobs }}:
-  - job: rocprofiler_compute_test_${{ job.target }}
-    timeoutInMinutes: 120
-    dependsOn: rocprofiler_compute_build_${{ job.target }}
-    condition:
-      and(succeeded(),
-        eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
-        not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
-        eq(${{ parameters.aggregatePipeline }}, False)
-      )
-    variables:
-    - group: common
-    - template: /.azuredevops/variables-global.yml
-    - name: PYTHON_VERSION
-      value: 3.10
-    pool: ${{ job.target }}_test_pool
-    workspace:
-      clean: all
-    steps:
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        pipModules: ${{ parameters.pipModules }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
-      parameters:
-        checkoutRepo: ${{ parameters.checkoutRepo }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
-      parameters:
-        gpuTarget: ${{ job.target }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
-      parameters:
-        checkoutRef: ${{ parameters.checkoutRef }}
-        dependencyList: ${{ parameters.rocmTestDependencies }}
-        gpuTarget: ${{ job.target }}
-    - task: Bash@3
-      displayName: Add en_US.UTF-8 locale
-      inputs:
-        targetType: inline
-        script: |
-          sudo locale-gen en_US.UTF-8
-          sudo update-locale
-          locale -a
-    - task: Bash@3
-      displayName: Add ROCm binaries to PATH
-      inputs:
-        targetType: inline
-        script: |
-          echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/bin"
-          echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/llvm/bin"
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
-      parameters:
-        extraBuildFlags: >-
-          -DCMAKE_HIP_ARCHITECTURES=${{ job.target }}
-          -DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
-          -DCMAKE_MODULE_PATH=$(Agent.BuildDirectory)/rocm/lib/cmake/hip
-          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-          -DROCM_PATH=$(Agent.BuildDirectory)/rocm
-          -DCMAKE_BUILD_TYPE=Release
-          -DENABLE_TESTS=ON
-          -DINSTALL_TESTS=ON
-          -GNinja
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
-      parameters:
-        componentName: rocprofiler-compute
-        testDir: $(Build.BinariesDirectory)/libexec/rocprofiler-compute
-        testExecutable: ROCM_PATH=$(Agent.BuildDirectory)/rocm ctest
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        pipModules: ${{ parameters.pipModules }}
-        environment: test
-        gpuTarget: ${{ job.target }}
+- ${{ if eq(parameters.unifiedBuild, False) }}:
+  - ${{ each job in parameters.jobMatrix.testJobs }}:
+    - job: rocprofiler_compute_test_${{ job.target }}
+      timeoutInMinutes: 120
+      dependsOn: rocprofiler_compute_build_${{ job.target }}
+      condition:
+        and(succeeded(),
+          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
+          not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
+          eq(${{ parameters.aggregatePipeline }}, False)
+        )
+      variables:
+      - group: common
+      - template: /.azuredevops/variables-global.yml
+      - name: PYTHON_VERSION
+        value: 3.10
+      pool: ${{ job.target }}_test_pool
+      workspace:
+        clean: all
+      steps:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          pipModules: ${{ parameters.pipModules }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
+        parameters:
+          checkoutRepo: ${{ parameters.checkoutRepo }}
+          sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
+        parameters:
+          preTargetFilter: ${{ parameters.componentName }}
+          gpuTarget: ${{ job.target }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
+        parameters:
+          checkoutRef: ${{ parameters.checkoutRef }}
+          dependencyList: ${{ parameters.rocmTestDependencies }}
+          gpuTarget: ${{ job.target }}
+          ${{ if parameters.triggerDownstreamJobs }}:
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
+      - task: Bash@3
+        displayName: Add en_US.UTF-8 locale
+        inputs:
+          targetType: inline
+          script: |
+            sudo locale-gen en_US.UTF-8
+            sudo update-locale
+            locale -a
+      - task: Bash@3
+        displayName: Add ROCm binaries to PATH
+        inputs:
+          targetType: inline
+          script: |
+            echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/bin"
+            echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/llvm/bin"
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
+        parameters:
+          extraBuildFlags: >-
+            -DCMAKE_HIP_ARCHITECTURES=${{ job.target }}
+            -DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
+            -DCMAKE_MODULE_PATH=$(Agent.BuildDirectory)/rocm/lib/cmake/hip
+            -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
+            -DROCM_PATH=$(Agent.BuildDirectory)/rocm
+            -DCMAKE_BUILD_TYPE=Release
+            -DENABLE_TESTS=ON
+            -DINSTALL_TESTS=ON
+            -GNinja
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
+        parameters:
+          componentName: ${{ parameters.componentName }}
+          testDir: $(Build.BinariesDirectory)/libexec/rocprofiler-compute
+          testExecutable: ROCM_PATH=$(Agent.BuildDirectory)/rocm ctest
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          pipModules: ${{ parameters.pipModules }}
+          environment: test
+          gpuTarget: ${{ job.target }}

.azuredevops/components/roctracer.yml

@@ -8,6 +8,22 @@ parameters:
 - name: checkoutRef
   type: string
   default: ''
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -65,6 +81,10 @@ parameters:
 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
   - job: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
+    ${{ if parameters.buildDependsOn }}:
+      dependsOn:
+        - ${{ each build in parameters.buildDependsOn }}:
+          - ${{ build }}_${{ job.os }}_${{ job.target }}
     variables:
     - group: common
     - template: /.azuredevops/variables-global.yml
@@ -87,6 +107,7 @@ jobs:
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
       parameters:
         checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
       parameters:
         checkoutRef: ${{ parameters.checkoutRef }}
@@ -94,6 +115,8 @@ jobs:
         gpuTarget: ${{ job.target }}
         aggregatePipeline: ${{ parameters.aggregatePipeline }}
         os: ${{ job.os }}
+        ${{ if parameters.triggerDownstreamJobs }}:
+          downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
     # the linker flags will not affect ubuntu2204 builds as the paths do not exist
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
       parameters:
@@ -109,10 +132,13 @@ jobs:
           -GNinja
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
       parameters:
+        componentName: ${{ parameters.componentName }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
         os: ${{ job.os }}
         gpuTarget: ${{ job.target }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
       parameters:
+        componentName: ${{ parameters.componentName }}
         os: ${{ job.os }}
         gpuTarget: ${{ job.target }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
@@ -123,53 +149,57 @@ jobs:
     #     gpuTarget: ${{ job.target }}
     #     registerROCmPackages: true
 
-- ${{ each job in parameters.jobMatrix.testJobs }}:
-  - job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
-    dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
-    condition:
-      and(succeeded(),
-        eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
-        not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
-        eq(${{ parameters.aggregatePipeline }}, False)
-      )
-    variables:
-    - group: common
-    - template: /.azuredevops/variables-global.yml
-    pool: ${{ job.target }}_test_pool
-    workspace:
-      clean: all
-    steps:
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        pipModules: ${{ parameters.pipModules }}
-        packageManager: ${{ job.packageManager }}
-        registerROCmPackages: true
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
-      parameters:
-        gpuTarget: ${{ job.target }}
-        os: ${{ job.os }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
-      parameters:
-        checkoutRef: ${{ parameters.checkoutRef }}
-        dependencyList: ${{ parameters.rocmTestDependencies }}
-        gpuTarget: ${{ job.target }}
-        os: ${{ job.os }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
-      parameters:
-        componentName: roctracer
-        testExecutable: $(Agent.BuildDirectory)/rocm/share/roctracer/run_tests.sh
-        testParameters: ''
-        testDir: $(Agent.BuildDirectory)
-        testPublishResults: false
-        os: ${{ job.os }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        pipModules: ${{ parameters.pipModules }}
-        environment: test
-        gpuTarget: ${{ job.target }}
-        registerROCmPackages: true
+- ${{ if eq(parameters.unifiedBuild, False) }}:
+  - ${{ each job in parameters.jobMatrix.testJobs }}:
+    - job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
+      dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
+      condition:
+        and(succeeded(),
+          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
+          not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
+          eq(${{ parameters.aggregatePipeline }}, False)
+        )
+      variables:
+      - group: common
+      - template: /.azuredevops/variables-global.yml
+      pool: ${{ job.target }}_test_pool
+      workspace:
+        clean: all
+      steps:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          pipModules: ${{ parameters.pipModules }}
+          packageManager: ${{ job.packageManager }}
+          registerROCmPackages: true
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
+        parameters:
+          preTargetFilter: ${{ parameters.componentName }}
+          gpuTarget: ${{ job.target }}
+          os: ${{ job.os }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
+        parameters:
+          checkoutRef: ${{ parameters.checkoutRef }}
+          dependencyList: ${{ parameters.rocmTestDependencies }}
+          gpuTarget: ${{ job.target }}
+          os: ${{ job.os }}
+          ${{ if parameters.triggerDownstreamJobs }}:
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
+        parameters:
+          componentName: ${{ parameters.componentName }}
+          testExecutable: $(Agent.BuildDirectory)/rocm/share/roctracer/run_tests.sh
+          testParameters: ''
+          testDir: $(Agent.BuildDirectory)
+          testPublishResults: false
+          os: ${{ job.os }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          pipModules: ${{ parameters.pipModules }}
+          environment: test
+          gpuTarget: ${{ job.target }}
+          registerROCmPackages: true

.azuredevops/dependencies/catch2.yml

@@ -0,0 +1,63 @@
+parameters:
+- name: checkoutRepo
+  type: string
+  default: 'self'
+- name: checkoutRef
+  type: string
+  default: ''
+- name: catch2Version
+  type: string
+  default: ''
+- name: aptPackages
+  type: object
+  default:
+    - cmake
+    - git
+    - ninja-build
+
+- name: jobMatrix
+  type: object
+  default:
+    buildJobs:
+      - { os: ubuntu2204, packageManager: apt}
+      - { os: almalinux8, packageManager: dnf}
+
+jobs:
+- ${{ each job in parameters.jobMatrix.buildJobs }}:
+  - job: catch2_${{ job.os }}
+    variables:
+    - group: common
+    - template: /.azuredevops/variables-global.yml
+    pool:
+      vmImage: 'ubuntu-22.04'
+    ${{ if eq(job.os, 'almalinux8') }}:
+      container:
+        image: rocmexternalcicd.azurecr.io/manylinux228:latest
+        endpoint: ContainerService3
+    workspace:
+      clean: all
+    steps:
+    - checkout: none
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+      parameters:
+        aptPackages: ${{ parameters.aptPackages }}
+        packageManager: ${{ job.packageManager }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+    - task: Bash@3
+      displayName: Clone catch2 ${{ parameters.catch2Version }}
+      inputs:
+        targetType: inline
+        script: git clone https://github.com/catchorg/Catch2.git -b ${{ parameters.catch2Version }}
+        workingDirectory: $(Agent.BuildDirectory)
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
+      parameters:
+        os: ${{ job.os }}
+        cmakeBuildDir: $(Agent.BuildDirectory)/Catch2/build
+        cmakeSourceDir: $(Agent.BuildDirectory)/Catch2
+        useAmdclang: false
+        extraBuildFlags: >-
+          -DCMAKE_BUILD_TYPE=Release
+          -GNinja
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
+      parameters:
+        os: ${{ job.os }}

.azuredevops/dependencies/fmtlib.yml

@@ -0,0 +1,67 @@
+parameters:
+- name: checkoutRepo
+  type: string
+  default: 'self'
+- name: checkoutRef
+  type: string
+  default: ''
+- name: fmtlibVersion
+  type: string
+  default: ''
+- name: aptPackages
+  type: object
+  default:
+    - cmake
+    - git
+    - ninja-build
+    - libfmt-dev
+
+- name: jobMatrix
+  type: object
+  default:
+    buildJobs:
+      - { os: ubuntu2204, packageManager: apt}
+      - { os: almalinux8, packageManager: dnf}
+
+jobs:
+- ${{ each job in parameters.jobMatrix.buildJobs }}:
+  - job: fmtlib_${{ job.os }}
+    variables:
+    - group: common
+    - template: /.azuredevops/variables-global.yml
+    pool:
+      vmImage: 'ubuntu-22.04'
+    ${{ if eq(job.os, 'almalinux8') }}:
+      container:
+        image: rocmexternalcicd.azurecr.io/manylinux228:latest
+        endpoint: ContainerService3
+    workspace:
+      clean: all
+    steps:
+    - checkout: none
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+      parameters:
+        aptPackages: ${{ parameters.aptPackages }}
+        packageManager: ${{ job.packageManager }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+    - task: Bash@3
+      displayName: Clone fmtlib ${{ parameters.fmtlibVersion }}
+      inputs:
+        targetType: inline
+        script: git clone https://github.com/fmtlib/fmt.git -b ${{ parameters.fmtlibVersion }}
+        workingDirectory: $(Agent.BuildDirectory)
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
+      parameters:
+        os: ${{ job.os }}
+        cmakeBuildDir: $(Agent.BuildDirectory)/fmt/build
+        cmakeSourceDir: $(Agent.BuildDirectory)/fmt
+        useAmdclang: false
+        extraBuildFlags: >-
+          -DCMAKE_BUILD_TYPE=Release
+          -DFMT_SYSTEM_HEADERS=ON
+          -DFMT_INSTALL=ON
+          -DFMT_TEST=OFF
+          -GNinja
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
+      parameters:
+        os: ${{ job.os }}

.azuredevops/dependencies/libdivide.yml

@@ -0,0 +1,64 @@
+parameters:
+- name: checkoutRepo
+  type: string
+  default: 'self'
+- name: checkoutRef
+  type: string
+  default: ''
+- name: libdivideVersion
+  type: string
+  default: ''
+- name: aptPackages
+  type: object
+  default:
+    - cmake
+    - git
+    - ninja-build
+
+- name: jobMatrix
+  type: object
+  default:
+    buildJobs:
+      - { os: ubuntu2204, packageManager: apt}
+      - { os: almalinux8, packageManager: dnf}
+
+jobs:
+- ${{ each job in parameters.jobMatrix.buildJobs }}:
+  - job: libdivide_${{ job.os }}
+    variables:
+    - group: common
+    - template: /.azuredevops/variables-global.yml
+    pool:
+      vmImage: 'ubuntu-22.04'
+    ${{ if eq(job.os, 'almalinux8') }}:
+      container:
+        image: rocmexternalcicd.azurecr.io/manylinux228:latest
+        endpoint: ContainerService3
+    workspace:
+      clean: all
+    steps:
+    - checkout: none
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+      parameters:
+        aptPackages: ${{ parameters.aptPackages }}
+        packageManager: ${{ job.packageManager }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+    - task: Bash@3
+      displayName: Clone libdivide ${{ parameters.libdivideVersion }}
+      inputs:
+        targetType: inline
+        script: git clone https://github.com/ridiculousfish/libdivide.git -b ${{ parameters.libdivideVersion }}
+        workingDirectory: $(Agent.BuildDirectory)
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
+      parameters:
+        os: ${{ job.os }}
+        cmakeBuildDir: $(Agent.BuildDirectory)/libdivide/build
+        cmakeSourceDir: $(Agent.BuildDirectory)/libdivide
+        useAmdclang: false
+        extraBuildFlags: >-
+          -DCMAKE_BUILD_TYPE=Release
+          -DLIBDIVIDE_BUILD_TESTS=OFF
+          -GNinja
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
+      parameters:
+        os: ${{ job.os }}

.azuredevops/dependencies/spdlog.yml

@@ -0,0 +1,71 @@
+parameters:
+- name: checkoutRepo
+  type: string
+  default: 'self'
+- name: checkoutRef
+  type: string
+  default: ''
+- name: spdlogVersion
+  type: string
+  default: ''
+- name: aptPackages
+  type: object
+  default:
+    - cmake
+    - git
+    - ninja-build
+
+- name: jobMatrix
+  type: object
+  default:
+    buildJobs:
+      - { os: ubuntu2204, packageManager: apt}
+      - { os: almalinux8, packageManager: dnf}
+
+jobs:
+- ${{ each job in parameters.jobMatrix.buildJobs }}:
+  - job: spdlog_${{ job.os }}
+    variables:
+    - group: common
+    - template: /.azuredevops/variables-global.yml
+    pool:
+      vmImage: 'ubuntu-22.04'
+    ${{ if eq(job.os, 'almalinux8') }}:
+      container:
+        image: rocmexternalcicd.azurecr.io/manylinux228:latest
+        endpoint: ContainerService3
+    workspace:
+      clean: all
+    steps:
+    - checkout: none
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+      parameters:
+        aptPackages: ${{ parameters.aptPackages }}
+        packageManager: ${{ job.packageManager }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
+      parameters:
+        dependencyList:
+          - fmtlib
+    - task: Bash@3
+      displayName: Clone spdlog ${{ parameters.spdlogVersion }}
+      inputs:
+        targetType: inline
+        script: git clone https://github.com/gabime/spdlog.git -b ${{ parameters.spdlogVersion }}
+        workingDirectory: $(Agent.BuildDirectory)
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
+      parameters:
+        os: ${{ job.os }}
+        cmakeBuildDir: $(Agent.BuildDirectory)/spdlog/build
+        cmakeSourceDir: $(Agent.BuildDirectory)/spdlog
+        useAmdclang: false
+        extraBuildFlags: >-
+          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/vendor
+          -DCMAKE_BUILD_TYPE=Release
+          -DSPDLOG_USE_STD_FORMAT=OFF
+          -DSPDLOG_FMT_EXTERNAL_HO=ON
+          -DSPDLOG_INSTALL=ON
+          -GNinja
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
+      parameters:
+        os: ${{ job.os }}

.azuredevops/nightly/pytorch.yml

@@ -397,6 +397,7 @@ jobs:
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - task: DownloadPipelineArtifact@2
     displayName: 'Download Pipeline Wheel Files'
+    retryCountOnTaskFailure: 3
     inputs:
       itemPattern: '**/*.whl'
       targetPath: $(Agent.BuildDirectory)

.azuredevops/nightly/rocm-nightly.yml

@@ -93,7 +93,7 @@ schedules:
 jobs:
 - ${{ each job in parameters.jobList }}:
   - job: nightly_${{ job.os }}_${{ job.target }}
-    timeoutInMinutes: 90
+    timeoutInMinutes: 120
     variables:
     - group: common
     - template: /.azuredevops/variables-global.yml
@@ -226,6 +226,7 @@ jobs:
             cat Dockerfile
     - task: Docker@2
       displayName: Build and upload Docker image
+      retryCountOnTaskFailure: 3
       inputs:
         containerRegistry: ContainerService3
         repository: 'nightly-${{ job.os }}-${{ job.target }}'

.azuredevops/tag-builds/catch2.yml

@@ -0,0 +1,23 @@
+variables:
+- group: common
+- template: /.azuredevops/variables-global.yml
+
+parameters:
+- name: catch2Version
+  type: string
+  default: "v3.7.0"
+
+resources:
+  repositories:
+  - repository: pipelines_repo
+    type: github
+    endpoint: ROCm
+    name: ROCm/ROCm
+
+trigger: none
+pr: none
+
+jobs:
+  - template: ${{ variables.CI_DEPENDENCIES_PATH }}/catch2.yml
+    parameters:
+      catch2Version: ${{ parameters.catch2Version }}

.azuredevops/tag-builds/fmtlib.yml

@@ -0,0 +1,23 @@
+variables:
+- group: common
+- template: /.azuredevops/variables-global.yml
+
+parameters:
+- name: fmtlibVersion
+  type: string
+  default: "11.1.3"
+
+resources:
+  repositories:
+  - repository: pipelines_repo
+    type: github
+    endpoint: ROCm
+    name: ROCm/ROCm
+
+trigger: none
+pr: none
+
+jobs:
+  - template: ${{ variables.CI_DEPENDENCIES_PATH }}/fmtlib.yml
+    parameters:
+      fmtlibVersion: ${{ parameters.fmtlibVersion }}

.azuredevops/tag-builds/libdivide.yml

@@ -0,0 +1,23 @@
+variables:
+- group: common
+- template: /.azuredevops/variables-global.yml
+
+parameters:
+- name: libdivideVersion
+  type: string
+  default: master
+
+resources:
+  repositories:
+  - repository: pipelines_repo
+    type: github
+    endpoint: ROCm
+    name: ROCm/ROCm
+
+trigger: none
+pr: none
+
+jobs:
+  - template: ${{ variables.CI_DEPENDENCIES_PATH }}/libdivide.yml
+    parameters:
+      libdivideVersion: ${{ parameters.libdivideVersion }}

.azuredevops/tag-builds/spdlog.yml

@@ -0,0 +1,23 @@
+variables:
+- group: common
+- template: /.azuredevops/variables-global.yml
+
+parameters:
+- name: spdlogVersion
+  type: string
+  default: "v1.15.1"
+
+resources:
+  repositories:
+  - repository: pipelines_repo
+    type: github
+    endpoint: ROCm
+    name: ROCm/ROCm
+
+trigger: none
+pr: none
+
+jobs:
+  - template: ${{ variables.CI_DEPENDENCIES_PATH }}/spdlog.yml
+    parameters:
+      spdlogVersion: ${{ parameters.spdlogVersion }}

.azuredevops/templates/steps/artifact-download.yml

@@ -24,8 +24,12 @@ parameters:
 steps:
 - task: DownloadPipelineArtifact@2
   displayName: Download ${{ parameters.componentName }}
+  retryCountOnTaskFailure: 3
   inputs:
-    itemPattern: '**/*${{ parameters.componentName }}*${{ parameters.fileFilter }}*'
+    ${{ if eq(parameters.componentName, 'clr') }}:
+      itemPattern: '**/*${{ parameters.componentName }}*${{ parameters.fileFilter }}*amd*' # filter out nvidia clr artifacts
+    ${{ else }}:
+      itemPattern: '**/*${{ parameters.componentName }}*${{ parameters.fileFilter }}*'
     targetPath: '$(Pipeline.Workspace)/d'
     allowPartiallySucceededBuilds: true
     ${{ if parameters.aggregatePipeline }}:

.azuredevops/templates/steps/checkout.yml

@@ -20,7 +20,7 @@ steps:
     retryCountOnTaskFailure: 3
     fetchFilter: blob:none
     ${{ if ne(parameters.sparseCheckoutDir, '') }}:
-      sparseCheckoutDirectories: ${{ parameters.sparseCheckoutDir }}
+      sparseCheckoutDirectories: ${{ parameters.sparseCheckoutDir }} shared
       path: sparse
   - ${{ if ne(parameters.sparseCheckoutDir, '') }}:
     - task: Bash@3

.azuredevops/templates/steps/dependencies-apt.yml

@@ -10,6 +10,7 @@ steps:
 - ${{ if eq(parameters.registerROCmPackages, true) }}:
   - task: Bash@3
     displayName: 'Register AMDGPU & ROCm repos (apt)'
+    retryCountOnTaskFailure: 3
     inputs:
       targetType: inline
       script: |
@@ -20,7 +21,8 @@ steps:
         echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
         sudo apt update
 - task: Bash@3
-  displayName: 'sudo apt-get update'
+  displayName: 'APT update and install packages'
+  retryCountOnTaskFailure: 3
   inputs:
     targetType: inline
     script: |
@@ -28,15 +30,6 @@ steps:
       echo "deb http://archive.ubuntu.com/ubuntu/ jammy-updates main restricted universe multiverse" | sudo tee -a /etc/apt/sources.list.d/default.list
       echo "deb http://archive.ubuntu.com/ubuntu/ jammy-backports main restricted universe multiverse" | sudo tee -a /etc/apt/sources.list.d/default.list
       echo "deb http://archive.ubuntu.com/ubuntu/ jammy-security main restricted universe multiverse" | sudo tee -a /etc/apt/sources.list.d/default.list
-      sudo DEBIAN_FRONTEND=noninteractive apt-get --yes update
-- task: Bash@3
-  displayName: 'sudo apt-get fix'
-  inputs:
-    targetType: inline
-    script: sudo DEBIAN_FRONTEND=noninteractive apt-get --yes --fix-broken install
-- ${{ if gt(length(parameters.aptPackages), 0) }}:
-  - task: Bash@3
-    displayName: 'sudo apt-get install ...'
-    inputs:
-      targetType: inline
-      script: sudo DEBIAN_FRONTEND=noninteractive apt-get --yes --fix-missing install ${{ join(' ', parameters.aptPackages) }}
+      sudo DEBIAN_FRONTEND=noninteractive apt-get --yes update && \
+        sudo DEBIAN_FRONTEND=noninteractive apt-get --yes --fix-broken install && \
+        sudo DEBIAN_FRONTEND=noninteractive apt-get --yes --fix-missing install ${{ join(' ', parameters.aptPackages) }}

.azuredevops/templates/steps/dependencies-aqlprofile.yml

@@ -5,51 +5,28 @@ parameters:
 
 steps:
 - task: Bash@3
-  displayName: Get aqlprofile package name
-  inputs:
-    targetType: inline
-    ${{ if eq(parameters.os, 'ubuntu2204') }}:
-      script: |
-        export packageName=$(curl -s https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION)/pool/main/h/hsa-amd-aqlprofile/ | grep -oP "href=\"\K[^\"]*$(lsb_release -rs)[^\"]*\.deb")
-        echo "##vso[task.setvariable variable=packageName;isreadonly=true]$packageName"
-    ${{ if eq(parameters.os, 'almalinux8') }}:
-      script: |
-        export packageName=$(curl -s https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/ | grep -oP "hsa-amd-aqlprofile-[^\"]+\.rpm" | head -n1)
-        echo "##vso[task.setvariable variable=packageName;isreadonly=true]$packageName"
-- task: Bash@3
-  displayName: 'Download aqlprofile'
-  inputs:
-    targetType: inline
-    workingDirectory: '$(Pipeline.Workspace)'
-    ${{ if eq(parameters.os, 'ubuntu2204') }}:
-      script: wget -nv https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION)/pool/main/h/hsa-amd-aqlprofile/$(packageName)
-    ${{ if eq(parameters.os, 'almalinux8') }}:
-      script: wget -nv https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/$(packageName)
-- task: Bash@3
-  displayName: 'Extract aqlprofile'
-  inputs:
-    targetType: inline
-    workingDirectory: '$(Pipeline.Workspace)'
-    ${{ if eq(parameters.os, 'ubuntu2204') }}:
-      script: |
-        mkdir hsa-amd-aqlprofile
-        dpkg-deb -R $(packageName) hsa-amd-aqlprofile
-    ${{ if eq(parameters.os, 'almalinux8') }}:
-      script: |
-        mkdir hsa-amd-aqlprofile
-        sudo dnf -y install rpm-build cpio
-        rpm2cpio $(packageName) | (cd hsa-amd-aqlprofile && cpio -idmv)
-- task: Bash@3
-  displayName: 'Copy aqlprofile files'
+  displayName: Download and install aqlprofile
+  retryCountOnTaskFailure: 3
   inputs:
     targetType: inline
+    workingDirectory: $(Agent.BuildDirectory)
     script: |
-      mkdir -p $(Agent.BuildDirectory)/rocm
-      cp -R hsa-amd-aqlprofile/opt/rocm-*/* $(Agent.BuildDirectory)/rocm
-    workingDirectory: '$(Pipeline.Workspace)'
-- task: Bash@3
-  displayName: 'Clean up aqlprofile'
-  inputs:
-    targetType: inline
-    script: rm -rf hsa-amd-aqlprofile $(packageName)
-    workingDirectory: '$(Pipeline.Workspace)'
+      set -e
+      if [ "${{ parameters.os }}" = "ubuntu2204" ]; then
+        packageName=$(curl -s https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION)/pool/main/h/hsa-amd-aqlprofile/ | grep -oP "href=\"\K[^\"]*$(lsb_release -rs)[^\"]*\.deb") && \
+        wget -nv https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION)/pool/main/h/hsa-amd-aqlprofile/$packageName && \
+        mkdir -p hsa-amd-aqlprofile && \
+        dpkg-deb -R $packageName hsa-amd-aqlprofile
+      elif [ "${{ parameters.os }}" = "almalinux8" ]; then
+        sudo dnf -y install rpm-build cpio && \
+        packageName=$(curl -s https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/ | grep -oP "hsa-amd-aqlprofile-[^\"]+\.rpm" | head -n1) && \
+        wget -nv https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/$packageName && \
+        mkdir -p hsa-amd-aqlprofile && \
+        rpm2cpio $packageName | (cd hsa-amd-aqlprofile && cpio -idmv)
+      else
+        echo "Unsupported OS: ${{ parameters.os }}"
+        exit 1
+      fi && \
+      mkdir -p $(Agent.BuildDirectory)/rocm && \
+      cp -R hsa-amd-aqlprofile/opt/rocm-*/* $(Agent.BuildDirectory)/rocm && \
+      rm -rf hsa-amd-aqlprofile $packageName

.azuredevops/templates/steps/dependencies-dnf.yml

@@ -89,6 +89,7 @@ steps:
 - ${{ if eq(parameters.registerROCmPackages, true) }}:
   - task: Bash@3
     displayName: 'Register AMDGPU & ROCm repos (dnf)'
+    retryCountOnTaskFailure: 3
     inputs:
       targetType: inline
       script: |
@@ -109,12 +110,13 @@ steps:
         sudo dnf makecache
 - task: Bash@3
   displayName: 'Install base dnf packages'
+  retryCountOnTaskFailure: 3
   inputs:
     targetType: inline
     script: |
-      sudo dnf config-manager --set-enabled powertools
       # rpm fusion free repo for some dependencies
-      sudo dnf -y install https://download1.rpmfusion.org/free/el/rpmfusion-free-release-8.noarch.rpm
+      sudo dnf config-manager --set-enabled powertools && \
+      sudo dnf -y install https://download1.rpmfusion.org/free/el/rpmfusion-free-release-8.noarch.rpm && \
       sudo dnf -y install ${{ join(' ', parameters.basePackages) }}
 - task: Bash@3
   displayName: 'Check gcc environment'
@@ -128,6 +130,7 @@ steps:
       g++ -print-file-name=libstdc++.so
 - task: Bash@3
   displayName: 'Set python 3.11 as default'
+  retryCountOnTaskFailure: 3
   inputs:
     targetType: inline
     script: |
@@ -142,18 +145,20 @@ steps:
   - ${{ if eq(pkg, 'ninja-build') }}:
     - task: Bash@3
       displayName: 'Install ninja 1.11.1'
+      retryCountOnTaskFailure: 3
       inputs:
         targetType: inline
         script: |
-          curl -LO https://github.com/ninja-build/ninja/releases/download/v1.11.1/ninja-linux.zip
-          sudo dnf -y install unzip
-          unzip ninja-linux.zip
-          sudo mv ninja /usr/local/bin/ninja
-          sudo chmod +x /usr/local/bin/ninja
+          sudo dnf -y install unzip && \
+          curl -LO https://github.com/ninja-build/ninja/releases/download/v1.11.1/ninja-linux.zip && \
+          unzip ninja-linux.zip && \
+          sudo mv ninja /usr/local/bin/ninja && \
+          sudo chmod +x /usr/local/bin/ninja && \
           echo "##vso[task.prependpath]/usr/local/bin"
   - ${{ if ne(parameters.aptToDnfMap[pkg], '') }}:
     - task: Bash@3
       displayName: 'dnf install ${{ parameters.aptToDnfMap[pkg] }}'
+      retryCountOnTaskFailure: 3
       inputs:
         targetType: inline
         script: |

.azuredevops/templates/steps/dependencies-other.yml

@@ -27,6 +27,7 @@ steps:
 - ${{ if gt(length(parameters.pipModules), 0) }}:
   - task: Bash@3
     displayName: 'pip install  ...'
+    retryCountOnTaskFailure: 3
     inputs:
       targetType: inline
       script: python3 -m pip install -v --force-reinstall ${{ join(' ', parameters.pipModules) }}

.azuredevops/templates/steps/dependencies-rocm.yml

@@ -47,8 +47,8 @@ parameters:
       developBranch: aomp-dev
       hasGpuTarget: false
     clr:
-      pipelineId: 145
-      developBranch: amd-staging
+      pipelineId: 335
+      developBranch: develop
       hasGpuTarget: false
     composable_kernel:
       pipelineId: 86
@@ -59,8 +59,8 @@ parameters:
       developBranch: rocm
       hasGpuTarget: false
     HIP:
-      pipelineId: 93
-      developBranch: amd-staging
+      pipelineId: 335
+      developBranch: develop
       hasGpuTarget: false
     hip-tests:
       pipelineId: 233

.azuredevops/templates/steps/dependencies-vendor.yml

@@ -8,15 +8,20 @@ parameters:
   type: object
   default:
     boost: 250
+    catch2: 343
+    fmtlib: 341
     grpc: 72
     gtest: 73
     half560: 68
     lapack: 69
+    libdivide: 342
+    spdlog: 340
 
 steps:
 - ${{ each dependency in parameters.dependencyList }}:
   - task: DownloadPipelineArtifact@2
     displayName: Download ${{ dependency }}
+    retryCountOnTaskFailure: 3
     inputs:
       project: ROCm-CI
       buildType: specific
@@ -28,7 +33,7 @@ steps:
     inputs:
       archiveFilePatterns: '$(Pipeline.Workspace)/d/**/*.tar.gz'
       destinationFolder: $(Agent.BuildDirectory)/vendor
-      cleanDestinationFolder: true
+      cleanDestinationFolder: false
       overwriteExistingFiles: true
   - task: DeleteFiles@1
     displayName: Clean up ${{ dependency }}

.azuredevops/templates/steps/local-artifact-download.yml

@@ -33,6 +33,7 @@ parameters:
 steps:
   - task: DownloadPipelineArtifact@2
     displayName: Download ${{ parameters.preTargetFilter}}*${{ parameters.os }}_${{ parameters.gpuTarget}}*${{ parameters.postTargetFilter}}
+    retryCountOnTaskFailure: 3
     inputs:
       ${{ if eq(parameters.buildType, 'specific') }}:
         buildType: specific

.azuredevops/templates/steps/miopen-get-ck-build.yml

@@ -7,6 +7,7 @@ steps:
 - task: Bash@3
   name: downloadCKBuild
   displayName: Download specific CK build
+  retryCountOnTaskFailure: 3
   env:
     CXX: $(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
     CC: $(Agent.BuildDirectory)/rocm/llvm/bin/amdclang

.wordlist.txt

@@ -116,6 +116,7 @@ Deprecations
 DevCap
 DirectX
 Dockerfile
+Dockerized
 Doxygen
 dropless
 ELMo
@@ -123,6 +124,7 @@ ENDPGM
 EPYC
 ESXi
 EoS
+fas
 FBGEMM
 FFT
 FFTs
@@ -195,6 +197,7 @@ HWE
 HWS
 Haswell
 Higgs
+href
 Hyperparameters
 Huggingface
 ICD
@@ -361,6 +364,7 @@ PowerEdge
 PowerShell
 Pretrained
 Pretraining
+Primus
 Profiler's
 PyPi
 Pytest
@@ -525,6 +529,7 @@ Xilinx
 Xnack
 Xteam
 YAML
+YAMLs
 YML
 YModel
 ZeRO
@@ -585,6 +590,7 @@ completers
 composable
 concretization
 config
+configs
 conformant
 constructible
 convolutional
@@ -795,7 +801,9 @@ preprocessing
 preprocessor
 prequantized
 prerequisites
+pretrain
 pretraining
+primus
 profiler
 profilers
 protobuf
@@ -910,6 +918,7 @@ toolchain
 toolchains
 toolset
 toolsets
+torchtitan
 torchvision
 tqdm
 tracebacks

docs/about/license.md

@@ -29,6 +29,7 @@ additional licenses. Please review individual repositories for more information.
 | [AMD SMI](https://github.com/ROCm/amdsmi) | [MIT](https://github.com/ROCm/amdsmi/blob/amd-staging/LICENSE) |
 | [aomp](https://github.com/ROCm/aomp/) | [Apache 2.0](https://github.com/ROCm/aomp/blob/aomp-dev/LICENSE) |
 | [aomp-extras](https://github.com/ROCm/aomp-extras/) | [MIT](https://github.com/ROCm/aomp-extras/blob/aomp-dev/LICENSE) |
+| [AQLprofile] | [MIT](https://github.com/ROCm/aqlprofile/blob/amd-staging/LICENSE) |
 | [Code Object Manager (Comgr)](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/comgr) | [The University of Illinois/NCSA](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/comgr/LICENSE.txt) |
 | [Composable Kernel](https://github.com/ROCm/composable_kernel) | [MIT](https://github.com/ROCm/composable_kernel/blob/develop/LICENSE) |
 | [half](https://github.com/ROCm/half/) | [MIT](https://github.com/ROCm/half/blob/rocm/LICENSE.txt) |
@@ -46,7 +47,6 @@ additional licenses. Please review individual repositories for more information.
 | [hipSPARSE](https://github.com/ROCm/hipSPARSE/) | [MIT](https://github.com/ROCm/hipSPARSE/blob/develop/LICENSE.md) |
 | [hipSPARSELt](https://github.com/ROCm/hipSPARSELt/) | [MIT](https://github.com/ROCm/hipSPARSELt/blob/develop/LICENSE.md) |
 | [hipTensor](https://github.com/ROCm/hipTensor) | [MIT](https://github.com/ROCm/hipTensor/blob/develop/LICENSE) |
-| hsa-amd-aqlprofile | [AMD Software EULA](https://www.amd.com/en/legal/eula/amd-software-eula.html) |
 | [llvm-project](https://github.com/ROCm/llvm-project/) | [Apache](https://github.com/ROCm/llvm-project/blob/amd-staging/LICENSE.TXT) |
 | [llvm-project/flang](https://github.com/ROCm/llvm-project/tree/amd-staging/flang) | [Apache 2.0](https://github.com/ROCm/llvm-project/blob/amd-staging/flang/LICENSE.TXT) |
 | [MIGraphX](https://github.com/ROCm/AMDMIGraphX/) | [MIT](https://github.com/ROCm/AMDMIGraphX/blob/develop/LICENSE) |
@@ -132,12 +132,10 @@ companies.
 ### Package licensing
 
 :::{attention}
-AQL Profiler and AOCC CPU optimization are both provided in binary form, each
-subject to the license agreement enclosed in the directory for the binary available
-in `/opt/rocm/share/doc/hsa-amd-aqlprofile/EULA`. By using, installing,
-copying or distributing AQL Profiler and/or AOCC CPU Optimizations, you agree to
+ROCprof Trace Decoder and AOCC CPU optimizations are provided in binary form, subject to the license agreement enclosed on [GitHub](https://github.com/ROCm/rocprof-trace-decoder/blob/amd-mainline/LICENSE) for ROCprof Trace Decoder, and [Developer Central](https://www.amd.com/en/developer/aocc.html) for AOCC. By using, installing,
+copying or distributing ROCprof Trace Decoder or AOCC CPU Optimizations, you agree to
 the terms and conditions of this license agreement. If you do not agree to the
-terms of this agreement, do not install, copy or use the AQL Profiler and/or the
+terms of this agreement, do not install, copy or use ROCprof Trace Decoder or the
 AOCC CPU Optimizations.
 :::
 

docs/conf.py

@@ -124,11 +124,15 @@ article_pages = [
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.3", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.4", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.5", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.6", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-primus-migration-guide", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-megatron", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-history", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.3", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.4", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.6", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4", "os": ["linux"]},

docs/data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml

@@ -1,26 +1,15 @@
 dockers:
-  - pull_tag: rocm/megatron-lm:v25.6_py312
-    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py312/images/sha256-482ff906532285bceabdf2bda629bd32cb6174d2d07f4243a736378001b28df0
+  - pull_tag: rocm/megatron-lm:v25.7_py310
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a
     components:
-      ROCm: 6.4.1
-      PyTorch: 2.8.0a0+git7d205b2
-      Python: 3.12
-      Transformer Engine: 2.1.0.dev0+8c4a512
-      hipBLASLt: 393e413
-      Triton: 3.3.0
-      RCCL: 2.23.4.7a84c5d
-    doc_name: Ubuntu 24.04 + Python 3.12
-  - pull_tag: rocm/megatron-lm:v25.6_py310
-    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py310/images/sha256-9627bd9378684fe26cb1a10c7dd817868f553b33402e49b058355b0f095568d6
-    components:
-      ROCm: 6.4.1
-      PyTorch: 2.8.0a0+git7d205b2
+      ROCm: 6.4.2
+      Primus: v0.1.0-rc1
+      PyTorch: 2.8.0a0+gitd06a406
       Python: "3.10"
-      Transformer Engine: 2.1.0.dev0+8c4a512
-      hipBLASLt: 393e413
+      Transformer Engine: 2.1.0.dev0+ba586519
+      hipBLASLt: 37ba1d36
       Triton: 3.3.0
-      RCCL: 2.23.4.7a84c5d
-    doc_name: Ubuntu 22.04 + Python 3.10
+      RCCL: 2.22.3
 model_groups:
   - group: Meta Llama
     tag: llama

docs/data/how-to/rocm-for-ai/training/previous-versions/megatron-lm-v25.6-benchmark-models.yaml

@@ -0,0 +1,60 @@
+dockers:
+  - pull_tag: rocm/megatron-lm:v25.6_py312
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py312/images/sha256-482ff906532285bceabdf2bda629bd32cb6174d2d07f4243a736378001b28df0
+    components:
+      ROCm: 6.4.1
+      PyTorch: 2.8.0a0+git7d205b2
+      Python: 3.12
+      Transformer Engine: 2.1.0.dev0+8c4a512
+      hipBLASLt: 393e413
+      Triton: 3.3.0
+      RCCL: 2.23.4.7a84c5d
+    doc_name: Ubuntu 24.04 + Python 3.12
+  - pull_tag: rocm/megatron-lm:v25.6_py310
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py310/images/sha256-9627bd9378684fe26cb1a10c7dd817868f553b33402e49b058355b0f095568d6
+    components:
+      ROCm: 6.4.1
+      PyTorch: 2.8.0a0+git7d205b2
+      Python: "3.10"
+      Transformer Engine: 2.1.0.dev0+8c4a512
+      hipBLASLt: 393e413
+      Triton: 3.3.0
+      RCCL: 2.23.4.7a84c5d
+    doc_name: Ubuntu 22.04 + Python 3.10
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.3 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.3-70b
+      - model: Llama 3.1 8B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-8b
+      - model: Llama 3.1 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-70b
+      - model: Llama 3.1 70B (proxy)
+        mad_tag: pyt_megatron_lm_train_llama-3.1-70b-proxy
+      - model: Llama 2 7B
+        mad_tag: pyt_megatron_lm_train_llama-2-7b
+      - model: Llama 2 70B
+        mad_tag: pyt_megatron_lm_train_llama-2-70b
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
+        mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
+      - model: DeepSeek-V2-Lite
+        mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: pyt_megatron_lm_train_mixtral-8x7b
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-7b
+      - model: Qwen 2.5 72B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-72b

docs/data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.6-benchmark-models.yaml

@@ -0,0 +1,120 @@
+unified_docker:
+  latest:
+    pull_tag: rocm/pytorch-training:v25.6
+    docker_hub_url: https://hub.docker.com/r/rocm/pytorch-training/tags
+    rocm_version: 6.4.1
+    pytorch_version: 2.8.0a0+git7d205b2
+    python_version: 3.10.17
+    transformer_engine_version: 1.14.0+2f85f5f2
+    flash_attention_version: 3.0.0.post1
+    hipblaslt_version: 0.15.0-8c6919d
+    triton_version: 3.3.0
+model_groups:
+  - group: Pre-training
+    tag: pre-training
+    models:
+    - model: Llama 3.1 8B
+      mad_tag: pyt_train_llama-3.1-8b
+      model_repo: Llama-3.1-8B
+      url: https://huggingface.co/meta-llama/Llama-3.1-8B
+      precision: BF16
+      training_modes: [pretrain]
+    - model: Llama 3.1 70B
+      mad_tag: pyt_train_llama-3.1-70b
+      model_repo: Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
+      precision: BF16
+      training_modes: [pretrain]
+    - model: FLUX.1-dev
+      mad_tag: pyt_train_flux
+      model_repo: Flux
+      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+      precision: BF16
+      training_modes: [pretrain]
+  - group: Fine-tuning
+    tag: fine-tuning
+    models:
+    - model: Llama 4 Scout 17B-16E
+      mad_tag: pyt_train_llama-4-scout-17b-16e
+      model_repo: Llama-4-17B_16E
+      url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.3 70B
+      mad_tag: pyt_train_llama-3.3-70b
+      model_repo: Llama-3.3-70B
+      url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 3.2 1B
+      mad_tag: pyt_train_llama-3.2-1b
+      model_repo: Llama-3.2-1B
+      url: https://huggingface.co/meta-llama/Llama-3.2-1B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 3B
+      mad_tag: pyt_train_llama-3.2-3b
+      model_repo: Llama-3.2-3B
+      url: https://huggingface.co/meta-llama/Llama-3.2-3B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 Vision 11B
+      mad_tag: pyt_train_llama-3.2-vision-11b
+      model_repo: Llama-3.2-Vision-11B
+      url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.2 Vision 90B
+      mad_tag: pyt_train_llama-3.2-vision-90b
+      model_repo: Llama-3.2-Vision-90B
+      url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.1 8B
+      mad_tag: pyt_train_llama-3.1-8b
+      model_repo: Llama-3.1-8B
+      url: https://huggingface.co/meta-llama/Llama-3.1-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.1 70B
+      mad_tag: pyt_train_llama-3.1-70b
+      model_repo: Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 3.1 405B
+      mad_tag: pyt_train_llama-3.1-405b
+      model_repo: Llama-3.1-405B
+      url: https://huggingface.co/meta-llama/Llama-3.1-405B
+      precision: BF16
+      training_modes: [finetune_qlora, HF_finetune_lora]
+    - model: Llama 3 8B
+      mad_tag: pyt_train_llama-3-8b
+      model_repo: Llama-3-8B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3 70B
+      mad_tag: pyt_train_llama-3-70b
+      model_repo: Llama-3-70B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 7B
+      mad_tag: pyt_train_llama-2-7b
+      model_repo: Llama-2-7B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 2 13B
+      mad_tag: pyt_train_llama-2-13b
+      model_repo: Llama-2-13B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 70B
+      mad_tag: pyt_train_llama-2-70b
+      model_repo: Llama-2-70B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_lora, finetune_qlora, HF_finetune_lora]

docs/data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml

@@ -0,0 +1,58 @@
+dockers:
+  - pull_tag: rocm/megatron-lm:v25.7_py310
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a
+    components:
+      ROCm: 6.4.2
+      Primus: v0.1.0-rc1
+      PyTorch: 2.8.0a0+gitd06a406
+      Python: "3.10"
+      Transformer Engine: 2.1.0.dev0+ba586519
+      hipBLASLt: 37ba1d36
+      Triton: 3.3.0
+      RCCL: 2.22.3
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.3 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
+        config_name: llama3.3_70B-pretrain.yaml
+      - model: Llama 3.1 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
+        config_name: llama3.1_70B-pretrain.yaml
+      - model: Llama 3.1 8B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
+        config_name: llama3.1_8B-pretrain.yaml
+      - model: Llama 2 7B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
+        config_name: llama2_7B-pretrain.yaml
+      - model: Llama 2 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
+        config_name: llama2_70B-pretrain.yaml
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v3-proxy
+        config_name: deepseek_v3-pretrain.yaml
+      - model: DeepSeek-V2-Lite
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
+        config_name: deepseek_v2_lite-pretrain.yaml
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x7b
+        config_name: mixtral_8x7B_v0.1-pretrain.yaml
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
+        config_name: mixtral_8x22B_v0.1-pretrain.yaml
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
+        config_name: primus_qwen2.5_7B-pretrain.yaml
+      - model: Qwen 2.5 72B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-72b
+        config_name: qwen2.5_72B-pretrain.yaml

docs/data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml

@@ -1,38 +1,17 @@
-unified_docker:
-  latest:
-    pull_tag: rocm/pytorch-training:v25.6
-    docker_hub_url: https://hub.docker.com/r/rocm/pytorch-training/tags
-    rocm_version: 6.4.1
-    pytorch_version: 2.8.0a0+git7d205b2
-    python_version: 3.10.17
-    transformer_engine_version: 1.14.0+2f85f5f2
-    flash_attention_version: 3.0.0.post1
-    hipblaslt_version: 0.15.0-8c6919d
-    triton_version: 3.3.0
+dockers:
+  - pull_tag: rocm/pytorch-training:v25.7
+    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712
+    components:
+      ROCm: 6.4.2
+      PyTorch: 2.8.0a0+gitd06a406
+      Python: 3.10.18
+      Transformer Engine: 2.2.0.dev0+94e53dd8
+      Flash Attention: 3.0.0.post1
+      hipBLASLt: 1.1.0-4b9a52edfc
+      Triton: 3.3.0
 model_groups:
-  - group: Pre-training
-    tag: pre-training
-    models:
-    - model: Llama 3.1 8B
-      mad_tag: pyt_train_llama-3.1-8b
-      model_repo: Llama-3.1-8B
-      url: https://huggingface.co/meta-llama/Llama-3.1-8B
-      precision: BF16
-      training_modes: [pretrain]
-    - model: Llama 3.1 70B
-      mad_tag: pyt_train_llama-3.1-70b
-      model_repo: Llama-3.1-70B
-      url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
-      precision: BF16
-      training_modes: [pretrain]
-    - model: FLUX.1-dev
-      mad_tag: pyt_train_flux
-      model_repo: Flux
-      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
-      precision: BF16
-      training_modes: [pretrain]
-  - group: Fine-tuning
-    tag: fine-tuning
+  - group: Meta Llama
+    tag: llama
     models:
     - model: Llama 4 Scout 17B-16E
       mad_tag: pyt_train_llama-4-scout-17b-16e
@@ -75,19 +54,19 @@ model_groups:
       model_repo: Llama-3.1-8B
       url: https://huggingface.co/meta-llama/Llama-3.1-8B
       precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
+      training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
     - model: Llama 3.1 70B
       mad_tag: pyt_train_llama-3.1-70b
       model_repo: Llama-3.1-70B
-      url: https://huggingface.co/meta-llama/Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
       precision: BF16
-      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+      training_modes: [pretrain, finetune_fw, finetune_lora]
     - model: Llama 3.1 405B
       mad_tag: pyt_train_llama-3.1-405b
       model_repo: Llama-3.1-405B
       url: https://huggingface.co/meta-llama/Llama-3.1-405B
       precision: BF16
-      training_modes: [finetune_qlora, HF_finetune_lora]
+      training_modes: [finetune_qlora]
     - model: Llama 3 8B
       mad_tag: pyt_train_llama-3-8b
       model_repo: Llama-3-8B
@@ -117,4 +96,67 @@ model_groups:
       model_repo: Llama-2-70B
       url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
       precision: BF16
-      training_modes: [finetune_lora, finetune_qlora, HF_finetune_lora]
+      training_modes: [finetune_lora, finetune_qlora]
+  - group: OpenAI
+    tag: openai
+    models:
+    - model: GPT OSS 20B
+      mad_tag: pyt_train_gpt_oss_20b
+      model_repo: GPT-OSS-20B
+      url: https://huggingface.co/openai/gpt-oss-20b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+    - model: GPT OSS 120B
+      mad_tag: pyt_train_gpt_oss_120b
+      model_repo: GPT-OSS-120B
+      url: https://huggingface.co/openai/gpt-oss-120b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+  - group: Qwen
+    tag: qwen
+    models:
+    - model: Qwen 3 8B
+      mad_tag: pyt_train_qwen3-8b
+      model_repo: Qwen3-8B
+      url: https://huggingface.co/Qwen/Qwen3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 3 32B
+      mad_tag: pyt_train_qwen3-32b
+      model_repo: Qwen3-32
+      url: https://huggingface.co/Qwen/Qwen3-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 32B
+      mad_tag: pyt_train_qwen2.5-32b
+      model_repo: Qwen2.5-32B
+      url: https://huggingface.co/Qwen/Qwen2.5-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 72B
+      mad_tag: pyt_train_qwen2.5-72b
+      model_repo: Qwen2.5-72B
+      url: https://huggingface.co/Qwen/Qwen2.5-72B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2 1.5B
+      mad_tag: pyt_train_qwen2-1.5b
+      model_repo: Qwen2-1.5B
+      url: https://huggingface.co/Qwen/Qwen2-1.5B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 2 7B
+      mad_tag: pyt_train_qwen2-7b
+      model_repo: Qwen2-7B
+      url: https://huggingface.co/Qwen/Qwen2-7B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+  - group: Flux
+    tag: flux
+    models:
+    - model: FLUX.1-dev
+      mad_tag: pyt_train_flux
+      model_repo: Flux
+      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+      precision: BF16
+      training_modes: [pretrain]

docs/how-to/deep-learning-rocm.rst

@@ -2,58 +2,124 @@
    :description: How to install deep learning frameworks for ROCm
    :keywords: deep learning, frameworks, ROCm, install, PyTorch, TensorFlow, JAX, MAGMA, DeepSpeed, ML, AI
 
-********************************************
-Installing deep learning frameworks for ROCm
-********************************************
+**********************************
+Deep learning frameworks for ROCm
+**********************************
 
-ROCm provides a comprehensive ecosystem for deep learning development, including
-:ref:`libraries <artificial-intelligence-apis>` for optimized deep learning operations and ROCm-aware versions of popular
-deep learning frameworks and libraries such as PyTorch, TensorFlow, and JAX. ROCm works closely with these
-frameworks to ensure that framework-specific optimizations take advantage of AMD accelerator and GPU architectures.
+Deep learning frameworks provide environments for machine learning, training, fine-tuning, inference, and performance optimization.
 
-The following guides provide information on compatibility and supported
-features for these ROCm-enabled deep learning frameworks.
+ROCm offers a complete ecosystem for developing and running deep learning applications efficiently. It also provides ROCm-compatible versions of popular frameworks and libraries, such as PyTorch, TensorFlow, JAX, and others.
 
-* :doc:`PyTorch compatibility <../compatibility/ml-compatibility/pytorch-compatibility>`
-* :doc:`TensorFlow compatibility <../compatibility/ml-compatibility/tensorflow-compatibility>`
-* :doc:`JAX compatibility <../compatibility/ml-compatibility/jax-compatibility>`
-* :doc:`verl compatibility <../compatibility/ml-compatibility/verl-compatibility>`
-* :doc:`Stanford Megatron-LM compatibility <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`
-* :doc:`DGL compatibility <../compatibility/ml-compatibility/dgl-compatibility>`
-* :doc:`Megablocks compatibility <../compatibility/ml-compatibility/megablocks-compatibility>`
-* :doc:`Taichi compatibility <../compatibility/ml-compatibility/taichi-compatibility>`
+The AMD ROCm organization actively contributes to open-source development and collaborates closely with framework organizations. This collaboration ensures that framework-specific optimizations effectively leverage AMD GPUs and accelerators.
 
-This chart steps through typical installation workflows for installing deep learning frameworks for ROCm.
+The table below summarizes information about ROCm-enabled deep learning frameworks. It includes details on ROCm compatibility and third-party tool support, installation steps and options, and links to GitHub resources. For a complete list of supported framework versions on ROCm, see the :doc:`Compatibility matrix <../compatibility/compatibility-matrix>` topic.
 
-.. image:: ../data/how-to/framework_install_2024_07_04.png
-   :alt: Flowchart for installing ROCm-aware machine learning frameworks
-   :align: center
+.. list-table:: 
+    :header-rows: 1
+    :widths: 5 3 6 3
 
-See the installation instructions to get started.
+    * - Framework
+      - Installation
+      - Installation options
+      - GitHub
 
-* :doc:`PyTorch for ROCm <rocm-install-on-linux:install/3rd-party/pytorch-install>`
-* :doc:`TensorFlow for ROCm <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
-* :doc:`JAX for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
-* :doc:`verl for ROCm <rocm-install-on-linux:install/3rd-party/verl-install>`
-* :doc:`Stanford Megatron-LM for ROCm <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>`
-* :doc:`DGL for ROCm <rocm-install-on-linux:install/3rd-party/dgl-install>`
-* :doc:`Megablocks for ROCm <rocm-install-on-linux:install/3rd-party/megablocks-install>`
-* :doc:`Taichi for ROCm <rocm-install-on-linux:install/3rd-party/taichi-install>`
+    * - `PyTorch <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html>`__
+      - .. raw:: html
 
-.. note::
+          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html"><i class="fas fa-link fa-lg"></i></a>
+      - 
+        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-docker-image-with-pytorch-pre-installed>`__
+        - `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-wheels-package>`__
+        - `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-rocm-base-docker-image>`__
+        - `Upstream Docker file <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-upstream-dockerfile>`__
+      - .. raw:: html
 
-   For guidance on installing ROCm itself, refer to :doc:`ROCm installation for Linux <rocm-install-on-linux:index>`.
+          <a href="https://github.com/ROCm/pytorch"><i class="fab fa-github fa-lg"></i></a>
+
+    * - `TensorFlow <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/tensorflow-compatibility.html>`__
+      - .. raw:: html
+
+          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html"><i class="fas fa-link fa-lg"></i></a>
+      - 
+        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-docker-image-with-tensorflow-pre-installed>`__
+        - `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-wheels-package>`__
+
+      - .. raw:: html
+
+          <a href="https://github.com/ROCm/tensorflow-upstream"><i class="fab fa-github fa-lg"></i></a> 
+
+    * - `JAX <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/jax-compatibility.html>`__
+      - .. raw:: html
+
+          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html"><i class="fas fa-link fa-lg"></i></a>
+      - 
+        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html#using-a-prebuilt-docker-image>`__
+      - .. raw:: html
+
+          <a href="https://github.com/ROCm/jax"><i class="fab fa-github fa-lg"></i></a>
+
+    * - `verl <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/verl-compatibility.html>`__
+      - .. raw:: html
+
+          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html"><i class="fas fa-link fa-lg"></i></a>
+      - 
+        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html#use-a-prebuilt-docker-image-with-verl-pre-installed>`__
+      - .. raw:: html
+
+          <a href="https://github.com/ROCm/verl"><i class="fab fa-github fa-lg"></i></a>
+
+    * - `Stanford Megatron-LM <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.html>`__
+      - .. raw:: html
+
+          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html"><i class="fas fa-link fa-lg"></i></a>
+      - 
+        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html#use-a-prebuilt-docker-image-with-stanford-megatron-lm-pre-installed>`__
+      - .. raw:: html
+
+          <a href="https://github.com/ROCm/Stanford-Megatron-LM"><i class="fab fa-github fa-lg"></i></a>
+
+    * - `DGL <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/dgl-compatibility.html>`__
+      - .. raw:: html
+
+          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html"><i class="fas fa-link fa-lg"></i></a>
+      - 
+        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html#use-a-prebuilt-docker-image-with-dgl-pre-installed>`__
+      - .. raw:: html
+
+          <a href="https://github.com/ROCm/dgl"><i class="fab fa-github fa-lg"></i></a> 
+
+    * - `Megablocks <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/megablocks-compatibility.html>`__
+      - .. raw:: html
+
+          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html"><i class="fas fa-link fa-lg"></i></a>
+      - 
+        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html#using-a-prebuilt-docker-image-with-megablocks-pre-installed>`__
+      - .. raw:: html
+
+          <a href="https://github.com/ROCm/megablocks"><i class="fab fa-github fa-lg"></i></a>
+
+    * - `Taichi <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/taichi-compatibility.html>`__
+      - .. raw:: html
+
+          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html"><i class="fas fa-link fa-lg"></i></a>
+      - 
+        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-prebuilt-docker-image-with-taichi-pre-installed>`__
+        - `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-wheels-package>`__
+
+      - .. raw:: html
+
+          <a href="https://github.com/ROCm/taichi"><i class="fab fa-github fa-lg"></i></a>
 
 Learn how to use your ROCm deep learning environment for training, fine-tuning, inference, and performance optimization
 through the following guides.
 
 * :doc:`rocm-for-ai/index`
 
-* :doc:`Training <rocm-for-ai/training/index>`
+* :doc:`Use ROCm for training <rocm-for-ai/training/index>`
 
-* :doc:`Fine-tuning LLMs <rocm-for-ai/fine-tuning/index>`
+* :doc:`Use ROCm for fine-tuning LLMs <rocm-for-ai/fine-tuning/index>`
 
-* :doc:`Inference <rocm-for-ai/inference/index>`
+* :doc:`Use ROCm for AI inference <rocm-for-ai/inference/index>`
 
-* :doc:`Inference optimization <rocm-for-ai/inference-optimization/index>`
+* :doc:`Use ROCm for AI inference optimization <rocm-for-ai/inference-optimization/index>`
 

docs/how-to/rocm-for-ai/inference-optimization/workload.rst

@@ -939,7 +939,7 @@ hipBLASLt benchmarking
 The GEMM library
 `hipBLASLt <https://rocm.docs.amd.com/projects/hipBLASLt/en/latest/index.html>`_
 provides a benchmark tool for its supported operations. Refer to the
-`documentation <https://github.com/ROCm/hipBLASLt/blob/develop/clients/benchmarks/README.md>`_
+`documentation <https://github.com/ROCm/hipBLASLt/blob/develop/clients/bench/README.md>`_
 for details.
 
 * Example 1: Benchmark mix fp8 GEMM

docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.1-20250715.rst

@@ -46,7 +46,7 @@ vLLM inference performance testing
         - {{ unified_docker.hipblaslt_version }}
 
 With this Docker image, you can quickly test the :ref:`expected
-inference performance numbers <vllm-benchmark-performance-measurements>` for
+inference performance numbers <vllm-benchmark-performance-measurements-715>` for
 MI300X series accelerators.
 
 What's new
@@ -219,7 +219,7 @@ system's configuration.
             ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
             model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
 
-            Although the :ref:`available models <vllm-benchmark-available-models>` are preconfigured
+            Although the :ref:`available models <vllm-benchmark-available-models-715>` are preconfigured
             to collect latency and throughput performance data, you can also change the benchmarking
             parameters. See the standalone benchmarking tab for more information.
 

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

@@ -39,7 +39,7 @@ vLLM inference performance testing
         - {{ unified_docker.hipblaslt_version }}
 
 With this Docker image, you can quickly test the :ref:`expected
-inference performance numbers <vllm-benchmark-performance-measurements>` for
+inference performance numbers <vllm-benchmark-performance-measurements-812>` for
 MI300X series accelerators.
 
 What's new
@@ -208,7 +208,7 @@ system's configuration.
             and ``{{ model.mad_tag }}_serving.csv``.
 
             Although the :ref:`available models
-            <vllm-benchmark-available-models>` are preconfigured to collect
+            <vllm-benchmark-available-models-812>` are preconfigured to collect
             offline throughput and online serving performance data, you can
             also change the benchmarking parameters. See the standalone
             benchmarking tab for more information.

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

@@ -1,14 +1,14 @@
 .. meta::
-   :description: How to install ROCm and popular machine learning frameworks.
+   :description: How to install ROCm and popular deep learning frameworks.
    :keywords: ROCm, AI, LLM, train, fine-tune, FSDP, DeepSpeed, LLaMA, tutorial
 
 .. _rocm-for-ai-install:
 
-***********************************************
-Installing ROCm and machine learning frameworks
-***********************************************
+********************************************
+Installing ROCm and deep learning frameworks
+********************************************
 
-Before getting started, install ROCm and supported machine learning frameworks.
+Before getting started, install ROCm and supported deep learning frameworks.
 
 .. grid:: 1
 
@@ -22,9 +22,9 @@ If you’re new to ROCm, refer to the :doc:`ROCm quick start install guide for L
 <rocm-install-on-linux:install/quick-start>`.
 
 If you’re using a Radeon GPU for graphics-accelerated applications, refer to the
-`Radeon installation instructions <https://rocm.docs.amd.com/projects/radeon/en/docs-6.1.3/docs/install/native_linux/install-radeon.html>`_.
+`Radeon installation instructions <https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/howto_native_linux.html>`_.
 
-You can install ROCm on :ref:`compatible systems <rocm-install-on-linux:reference/system-requirements>` via your Linux
+You can install ROCm on :doc:`compatible systems <rocm-install-on-linux:reference/system-requirements>` via your Linux
 distribution's package manager. See the following documentation resources to get started:
 
 * :doc:`ROCm installation overview <rocm-install-on-linux:install/install-overview>`
@@ -43,29 +43,16 @@ distribution's package manager. See the following documentation resources to get
       If you encounter any issues during installation, refer to the
       :doc:`Installation troubleshooting <rocm-install-on-linux:reference/install-faq>` guide.
 
-Machine learning frameworks
-===========================
+Deep learning frameworks
+========================
 
-ROCm supports popular machine learning frameworks and libraries including `PyTorch
+ROCm supports deep learning frameworks and libraries including `PyTorch
 <https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-available-as-python-package>`_, `TensorFlow
-<https://tensorflow.org>`_, `JAX <https://jax.readthedocs.io/en/latest>`_, and `DeepSpeed
-<https://cloudblogs.microsoft.com/opensource/2022/03/21/supporting-efficient-large-model-training-on-amd-instinct-gpus-with-deepspeed/>`_.
+<https://tensorflow.org>`_, `JAX <https://jax.readthedocs.io/en/latest>`_, and more.
 
-Review the framework installation documentation. For ease-of-use, it's recommended to use official ROCm prebuilt Docker
+Review the :doc:`framework installation documentation <../deep-learning-rocm>`. For ease-of-use, it's recommended to use official ROCm prebuilt Docker
 images with the framework pre-installed.
 
-* :doc:`PyTorch for ROCm <rocm-install-on-linux:install/3rd-party/pytorch-install>`
-
-* :doc:`TensorFlow for ROCm <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
-
-* :doc:`JAX for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
-
-* :doc:`verl for ROCm <rocm-install-on-linux:install/3rd-party/verl-install>`
-
-* :doc:`Stanford Megatron-LM for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
-
-* :doc:`DGL for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
-
 Next steps
 ==========
 

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

@@ -1,3 +1,5 @@
+:orphan:
+
 .. meta::
    :description: How to train a model using Megatron-LM for ROCm.
    :keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
@@ -6,6 +8,14 @@
 Training a model with Megatron-LM for ROCm
 ******************************************
 
+.. caution::
+
+   The ROCm Megatron-LM framework now has limited support with this Docker
+   environment; it now focuses on Primus with Megatron-Core. See :doc:`primus-megatron`.
+
+   To learn how to migrate your existing workloads to Primus with Megatron-Core,
+   see :doc:`previous-versions/megatron-lm-primus-migration-guide`.
+
 The `Megatron-LM framework for ROCm <https://github.com/ROCm/Megatron-LM>`_ is
 a specialized fork of the robust Megatron-LM, designed to enable efficient
 training of large-scale language models on AMD GPUs. By leveraging AMD
@@ -20,13 +30,17 @@ essential components, including PyTorch, ROCm libraries, and Megatron-LM
 utilities. It contains the following software components to accelerate training
 workloads:
 
+.. note::
+
+   This Docker environment is based on Python 3.10 and Ubuntu 22.04. For an alternative environment with
+   Python 3.12 and Ubuntu 24.04, see the :doc:`previous ROCm Megatron-LM v25.6 Docker release <previous-versions/megatron-lm-v25.6>`.
+
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
 
    {% set dockers = data.dockers %}
-   {% if dockers|length > 1 %}
    .. tab-set::
 
-      {% for docker in data.dockers %}
+      {% for docker in dockers %}
       .. tab-item:: ``{{ docker.pull_tag }}``
          :sync: {{ docker.pull_tag }}
 
@@ -42,28 +56,14 @@ workloads:
 
             {% endfor %}
       {% endfor %}
-   {% elif dockers|length == 1 %}
-   .. list-table::
-      :header-rows: 1
-
-      * - Software component
-        - Version
-
-      {% for component_name, component_version in docker.components %}
-      * - {{ component_name }}
-        - {{ component_version }}
-
-      {% endfor %}
-   {% endif %}
 
    .. _amd-megatron-lm-model-support:
 
-   The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
-
    Supported models
    ================
 
-   The following models are supported for training performance benchmarking with Megatron-LM and ROCm.
+   The following models are supported for training performance benchmarking with Megatron-LM and ROCm
+   on AMD Instinct MI300X series accelerators.
    Some instructions, commands, and training recommendations in this documentation might
    vary by model -- select one to get started.
 
@@ -177,7 +177,7 @@ Download the Docker image
       {% if dockers|length > 1 %}
       .. tab-set::
 
-         {% for docker in data.dockers %}
+         {% for docker in dockers %}
          .. tab-item:: {{ docker.doc_name }}
             :sync: {{ docker.pull_tag }}
 
@@ -227,10 +227,17 @@ Download the Docker image
       docker start megatron_training_env
       docker exec -it megatron_training_env bash
 
-The Docker container includes a pre-installed, verified version of the ROCm
-Megatron-LM development branch
-`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev>`__, including necessary
-training scripts.
+4. **Megatron-LM backward compatibility setup** -- this Docker is primarily intended for use with Primus, but it maintains Megatron-LM compatibility with limited support.
+   To roll back to using Megatron-LM, follow these steps:
+
+   .. code-block:: shell
+
+      cd /workspace/Megatron-LM/
+      pip uninstall megatron-core
+      pip install -e .
+
+The Docker container hosts
+`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev>`__ at verified commit ``e8e9edc``.
 
 .. _amd-megatron-lm-environment-setup:
 

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

@@ -16,12 +16,20 @@ previous releases of the ``ROCm/megatron-lm`` Docker image on `Docker Hub <https
      - Components
      - Resources
 
-   * - v25.6 (latest)
+   * - v25.7 (latest)
+     - 
+       * ROCm 
+       * PyTorch 
+     - 
+       * :doc:`Documentation <../megatron-lm>`
+       * `Docker Hub (py310) <https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a>`__
+
+   * - v25.6
      - 
        * ROCm 6.4.1
        * PyTorch 2.8.0a0+git7d205b2
      - 
-       * :doc:`Documentation <../megatron-lm>`
+       * :doc:`Documentation <megatron-lm-v25.6>`
        * `Docker Hub (py312) <https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py312/images/sha256-482ff906532285bceabdf2bda629bd32cb6174d2d07f4243a736378001b28df0>`__
        * `Docker Hub (py310) <https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py310/images/sha256-9627bd9378684fe26cb1a10c7dd817868f553b33402e49b058355b0f095568d6>`__
 

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-primus-migration-guide.rst

@@ -0,0 +1,175 @@
+:orphan:
+
+**********************************************************************
+Migrating workloads to Primus (Megatron-Core backend) from Megatron-LM
+**********************************************************************
+
+Primus supports Megatron-Core as backend optimization library,
+replacing ROCm Megatron-LM. This document outlines the steps to migrate
+workload from ROCm Megatron-LM to Primus with the Megatron-Core backend.
+
+Model architecture
+==================
+
+ROCm Megatron-LM defines model architecture parameters in the training scripts;
+for example, the Llama 3 8B model parameters are defined in
+`examples/llama/train_llama3.sh <https://github.com/ROCm/Megatron-LM/blob/rocm_dev/examples/llama/train_llama3.sh#L117>`__
+as shown below:
+
+.. code-block:: bash
+
+   HIDDEN_SIZE=4096 
+   FFN_HIDDEN_SIZE=14336 
+   NUM_LAYERS=32 
+   NUM_HEADS=32 
+   NUM_KV_HEADS=8
+
+Primus defines the model architecture through model YAML configuration files
+inside the ``primus/configs/models/megatron/`` repository. For example, Llama 3 8B
+model architecture parameters are defined in
+`primus/configs/models/megatron/llama3_8B.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/llama3_8B.yaml>`__
+as shown below:
+
+.. code-block:: yaml
+
+   bases:
+     - llama3_base.yaml
+
+   tokenizer_type: Llama3Tokenizer
+   tokenizer_model: meta-llama/Llama-3.1-8B
+
+   ffn_hidden_size: 14336
+   hidden_size: 4096
+   num_attention_heads: 32
+   num_layers: 32
+   num_query_groups: 8
+
+Primus' model config files follow a hierarchical design, meaning that new model
+config YAMLs can inherit existing model config files by importing them as
+bases. For example,
+`llama3.1_8B.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/llama3.1_8B.yaml>`__
+uses ``llama3_8B.yaml`` as a base config and overrides few parameters, as shown below.
+In this example, ``llama3.1_8B`` overrides the ``max_position_embeddings`` value:
+
+.. code-block:: yaml
+
+   bases:
+     - llama3_8B.yaml
+
+   tokenizer_type: Llama3Tokenizer
+   tokenizer_model: meta-llama/Llama-3.1-8B
+
+   max_position_embeddings: 131072
+
+.. tip::
+
+   Primus provides ``llama_base.yaml`` as the base configuration, which can be
+   used as bases for additional model architectures. For example,
+   `mixtral_base.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/mixtral_base.yaml>`__
+   and
+   `deepseek_v3_base.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/deepseek_v3_base.yaml>`__
+   define ``llama_base.yaml`` as its base.
+
+   .. code-block:: yaml
+
+      # Example mixtral_base.yaml:
+
+      bases:
+        - llama_base.yaml
+
+      init_method_std: 0.01
+      rotary_base: 1000000
+      qk_layernorm: false
+
+      group_query_attention: true
+      num_query_groups: 8
+
+      # moe parameters
+      num_experts: 8
+      moe_router_topk: 2
+      moe_router_load_balancing_type: aux_loss
+      moe_aux_loss_coeff: 1e-2
+      moe_grouped_gemm: true
+      moe_token_dispatcher_type: alltoall
+
+It is recommended to add a new ``${MODEL_NAME}_base.yaml`` to add a new
+category of model and define new models on top of it. For example, to add
+Qwen2.5 models in Primus, we define
+`qwen2.5_base.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/qwen2.5_base.yaml>`__
+and build
+`qwen2.5_7B.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/qwen2.5_7B.yaml>`__
+and
+`qwen2.5_72B.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/qwen2.5_72B.yaml>`__
+using ``qwen2.5_base.yaml`` as the base config.
+
+Training parameters
+===================
+
+ROCm Megatron-LM also defines the training parameters, like batch size,
+tensor-parallelism, precision, as so on, in the training scripts. For example,
+Llama3 8B model parameters are defined in
+`examples/llama/train_llama3.sh <https://github.com/ROCm/Megatron-LM/blob/rocm_dev/examples/llama/train_llama3.sh>`__
+as shown below:
+
+.. code-block:: bash
+
+   TP="${TP:-8}"
+   PP="${PP:-1}"
+   CP="${CP:-1}"
+   MBS="${MBS:-1}"
+   BS="${BS:-8}"
+
+Primus defines the training parameters in top-level YAML files -- see
+`examples/megatron/configs/
+<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/megatron/configs>`__.
+For example, the `llama3.1_8B-pretrain.yaml
+<https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/examples/megatron/configs/llama3.1_8B-pretrain.yaml>`__
+configuration imports the ``llama3.1_8B.yaml`` model architecture file. Users can then override
+the default training parameters in ``llama3.1_8B-pretrain.yaml``.
+
+.. code-block:: yaml
+
+   # model to run
+   model: llama3.1_8B.yaml  # Model architecture yaml
+   overrides:
+     # log
+     # disable_wandb: false
+     # disable_tensorboard: false
+     stderr_sink_level: DEBUG
+
+     log_avg_skip_iterations: 2
+     log_avg_reset_interval: 50
+
+     train_iters: 50
+     micro_batch_size: 2
+     global_batch_size: 128
+
+     seq_length: 8192
+     max_position_embeddings: 8192
+
+     lr: 1.0e-5
+     min_lr: 0.0
+     lr_warmup_iters: 2
+     lr_decay_iters: null
+     lr_decay_style: cosine
+     weight_decay: 0.1
+     adam_beta1: 0.9
+     adam_beta2: 0.95
+     eod_mask_loss: true
+     init_method_std: 0.008
+     norm_epsilon: 1.0e-6
+
+Backward compatibility with Megatron-LM
+=======================================
+
+The Dockerized environment used for Primus maintains compatibility with Megatron-LM with
+limited support. To roll back to using Megatron-LM, follow these steps.
+
+.. code-block:: shell
+
+   cd /workspace/Megatron-LM/
+   pip uninstall megatron-core
+   pip install -e .
+
+Once Megatron-LM is installed, follow :doc:`the documentation <../megatron-lm>` to run workloads as
+usual.

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v24.12-dev.rst

@@ -18,7 +18,7 @@ Training a model with ROCm Megatron-LM
 The ROCm Megatron-LM framework is a specialized fork of the robust Megatron-LM, designed to
 enable efficient training of large-scale language models on AMD GPUs. By leveraging AMD Instinct™ MI300X
 accelerators, AMD Megatron-LM delivers enhanced scalability, performance, and resource utilization for AI
-workloads. It is purpose-built to :ref:`support models <amd-megatron-lm-model-support>`
+workloads. It is purpose-built to :ref:`support models <amd-megatron-lm-model-support-24-12>`
 like Meta's Llama 2, Llama 3, and Llama 3.1, enabling developers to train next-generation AI models with greater
 efficiency. See the GitHub repository at `<https://github.com/ROCm/Megatron-LM>`__.
 
@@ -67,7 +67,7 @@ Megatron-LM provides the following key features to train large language models e
 
 - Pre-training
 
-.. _amd-megatron-lm-model-support:
+.. _amd-megatron-lm-model-support-24-12:
 
 The following models are pre-optimized for performance on the AMD Instinct MI300X accelerator.
 

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.3.rst

@@ -67,7 +67,7 @@ Megatron-LM provides the following key features to train large language models e
 
 - Pre-training
 
-.. _amd-megatron-lm-model-support:
+.. _amd-megatron-lm-model-support-25-3:
 
 The following models are pre-optimized for performance on the AMD Instinct MI300X accelerator.
 
@@ -278,7 +278,7 @@ handle a variety of input sequences, including unseen words or domain-specific t
    .. tab-item:: Llama
       :sync: llama
 
-      To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``Llama2Tokenizer``.
+      To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-3>`, use the ``Llama2Tokenizer``.
 
       To train any of Llama 3 and Llama 3.1 models that this Docker image supports, use the ``HuggingFaceTokenizer``.
       Set the Hugging Face model link in the ``TOKENIZER_MODEL`` variable.
@@ -292,7 +292,7 @@ handle a variety of input sequences, including unseen words or domain-specific t
    .. tab-item:: DeepSeek V2
       :sync: deepseek
 
-      To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``DeepSeekV2Tokenizer``.
+      To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-3>`, use the ``DeepSeekV2Tokenizer``.
 
 Multi-node training
 ^^^^^^^^^^^^^^^^^^^

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.4.rst

@@ -67,7 +67,7 @@ Megatron-LM provides the following key features to train large language models e
 
 - Pre-training
 
-.. _amd-megatron-lm-model-support:
+.. _amd-megatron-lm-model-support-25-4:
 
 The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
 
@@ -291,7 +291,7 @@ or ``${DATA_DIR}/tokenizer_llama2``.
    .. tab-item:: Llama
       :sync: llama
 
-      To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``Llama2Tokenizer``
+      To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-4>`, use the ``Llama2Tokenizer``
       or the default ``HuggingFaceTokenizer``.
 
       To train any of Llama 3 and Llama 3.1 models that this Docker image supports, use the ``HuggingFaceTokenizer``.
@@ -320,7 +320,7 @@ or ``${DATA_DIR}/tokenizer_llama2``.
    .. tab-item:: DeepSeek V2
       :sync: deepseek
 
-      To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``DeepSeekV2Tokenizer``.
+      To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-4>`, use the ``DeepSeekV2Tokenizer``.
 
 Multi-node training
 ^^^^^^^^^^^^^^^^^^^

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

@@ -16,12 +16,20 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
      - Components
      - Resources
 
+   * - v25.7
+     - 
+       * ROCm 6.4.2
+       * PyTorch 2.8.0a0+gitd06a406
+     - 
+       * :doc:`Documentation <../pytorch-training>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712>`__
+
    * - v25.6
      - 
        * ROCm 6.3.4
        * PyTorch 2.8.0a0+git7d205b2
      - 
-       * :doc:`Documentation <../pytorch-training>`
+       * :doc:`Documentation <pytorch-training-v25.6>`
        * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.6/images/sha256-a4cea3c493a4a03d199a3e81960ac071d79a4a7a391aa9866add3b30a7842661>`__
 
    * - v25.5

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5.rst

@@ -437,3 +437,8 @@ Once the setup is complete, choose between two options to start benchmarking:
 
            ./pytorch_benchmark_report.sh -t HF_finetune_lora -p BF16 -m Llama-2-70B
 
+Previous versions
+=================
+
+See :doc:`pytorch-training-history` to find documentation for previous releases
+of the ``ROCm/pytorch-training`` Docker image.

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.6.rst

@@ -0,0 +1,456 @@
+:orphan:
+
+.. meta::
+   :description: How to train a model using PyTorch for ROCm.
+   :keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
+
+**************************************
+Training a model with PyTorch for ROCm
+**************************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm vLLM
+   performance benchmark documentation. See :doc:`../pytorch-training` for the latest version.
+
+PyTorch is an open-source machine learning framework that is widely used for
+model training with GPU-optimized components for transformer-based models.
+
+The `PyTorch for ROCm training Docker <https://hub.docker.com/layers/rocm/pytorch-training/v25.6/images/sha256-a4cea3c493a4a03d199a3e81960ac071d79a4a7a391aa9866add3b30a7842661>`_
+(``rocm/pytorch-training:v25.6``) image provides a prebuilt optimized environment for fine-tuning and pretraining a
+model on AMD Instinct MI325X and MI300X accelerators. It includes the following software components to accelerate
+training workloads:
+
++--------------------------+--------------------------------+
+| Software component       | Version                        |
++==========================+================================+
+| ROCm                     | 6.3.4                          |
++--------------------------+--------------------------------+
+| PyTorch                  | 2.8.0a0+git7d205b2             |
++--------------------------+--------------------------------+
+| Python                   | 3.10.17                        |
++--------------------------+--------------------------------+
+| Transformer Engine       | 1.14.0+2f85f5f2                |
++--------------------------+--------------------------------+
+| Flash Attention          | 3.0.0.post1                    |
++--------------------------+--------------------------------+
+| hipBLASLt                | 0.15.0-8c6919d                 |
++--------------------------+--------------------------------+
+| Triton                   | 3.3.0                          |
++--------------------------+--------------------------------+
+
+.. _amd-pytorch-training-model-support-v256:
+
+Supported models
+================
+
+The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.6-benchmark-models.yaml
+
+   {% set unified_docker = data.unified_docker.latest %}
+   {% set model_groups = data.model_groups %}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+        <div class="row">
+          <div class="col-2 me-2 model-param-head">Workload</div>
+          <div class="row col-10">
+   {% for model_group in model_groups %}
+            <div class="col-6 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+   {% endfor %}
+          </div>
+        </div>
+
+        <div class="row mt-1">
+          <div class="col-2 me-2 model-param-head">Model</div>
+          <div class="row col-10">
+   {% for model_group in model_groups %}
+      {% set models = model_group.models %}
+      {% for model in models %}
+         {% if models|length % 3 == 0 %}
+            <div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+         {% else %}
+            <div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+         {% endif %}
+      {% endfor %}
+   {% endfor %}
+          </div>
+        </div>
+      </div>
+
+   .. note::
+
+      Some models require an external license agreement through a third party (for example, Meta).
+
+   .. _amd-pytorch-training-performance-measurements-v256:
+
+   Performance measurements
+   ========================
+
+   To evaluate performance, the
+   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
+   page provides reference throughput and latency measurements for training
+   popular AI models.
+
+   .. note::
+
+      The performance data presented in
+      `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
+      should not be interpreted as the peak performance achievable by AMD
+      Instinct MI325X and MI300X accelerators or ROCm software.
+
+   System validation
+   =================
+
+   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.
+
+   Benchmarking
+   ============
+
+   Once the setup is complete, choose between two options to start benchmarking:
+
+   .. tab-set::
+
+      .. tab-item:: MAD-integrated benchmarking
+
+         Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+         directory and install the required packages on the host machine.
+
+         .. code-block:: shell
+
+            git clone https://github.com/ROCm/MAD
+            cd MAD
+            pip install -r requirements.txt
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            For example, use this command to run the performance benchmark test on the {{ model.model }} model
+            using one GPU with the {{ model.precision }} data type on the host machine.
+
+            .. code-block:: shell
+
+               export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+               madengine run \
+                   --tags {{ model.mad_tag }} \
+                   --keep-model-dir \
+                   --live-output \
+                   --timeout 28800
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{ model.mad_tag }}``, for example. The latency and throughput reports of the
+            model are collected in the following path: ``~/MAD/perf.csv``.
+
+      {% endfor %}
+   {% endfor %}
+
+      .. tab-item:: Standalone benchmarking
+
+         .. rubric:: Download the Docker image and required packages
+
+         Use the following command to pull the Docker image from Docker Hub.
+
+         .. code-block:: shell
+
+            docker pull {{ unified_docker.pull_tag }}
+
+         Run the Docker container.
+
+         .. code-block:: shell
+
+            docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME:$HOME -v  $HOME/.ssh:/root/.ssh --shm-size 64G --name training_env {{ unified_docker.pull_tag }}
+
+         Use these commands if you exit the ``training_env`` container and need to return to it.
+
+         .. code-block:: shell
+
+            docker start training_env
+            docker exec -it training_env bash
+
+         In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
+         repository and navigate to the benchmark scripts directory
+         ``/workspace/MAD/scripts/pytorch_train``.
+
+         .. code-block:: shell
+
+            git clone https://github.com/ROCm/MAD
+            cd MAD/scripts/pytorch_train
+
+         .. rubric:: Prepare training datasets and dependencies
+
+         The following benchmarking examples require downloading models and datasets
+         from Hugging Face. To ensure successful access to gated repos, set your
+         ``HF_TOKEN``.
+
+         .. code-block:: shell
+
+            export HF_TOKEN=$your_personal_hugging_face_access_token
+
+         Run the setup script to install libraries and datasets needed for benchmarking.
+
+         .. code-block:: shell
+
+            ./pytorch_benchmark_setup.sh
+
+         .. container:: model-doc pyt_train_llama-3.1-8b
+
+            ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Library
+                 - Reference
+
+               * - ``accelerate``
+                 - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
+
+               * - ``datasets``
+                 - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+
+         .. container:: model-doc pyt_train_llama-3.1-70b
+
+            ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Library
+                 - Reference
+
+               * - ``datasets``
+                 - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+
+               * - ``torchdata``
+                 - `TorchData <https://pytorch.org/data/beta/index.html>`_
+
+               * - ``tomli``
+                 - `Tomli <https://pypi.org/project/tomli/>`_
+
+               * - ``tiktoken``
+                 - `tiktoken <https://github.com/openai/tiktoken>`_
+
+               * - ``blobfile``
+                 - `blobfile <https://pypi.org/project/blobfile/>`_
+
+               * - ``tabulate``
+                 - `tabulate <https://pypi.org/project/tabulate/>`_
+
+               * - ``wandb``
+                 - `Weights & Biases <https://github.com/wandb/wandb>`_
+
+               * - ``sentencepiece``
+                 - `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
+
+               * - ``tensorboard``
+                 - `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
+
+         .. container:: model-doc pyt_train_flux
+
+            ``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Library
+                 - Reference
+
+               * - ``accelerate``
+                 - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
+
+               * - ``datasets``
+                 - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+
+               * - ``sentencepiece``
+                 - `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
+
+               * - ``tensorboard``
+                 - `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
+
+               * - ``csvkit``
+                 - `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
+
+               * - ``deepspeed``
+                 - `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
+
+               * - ``diffusers``
+                 - `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`_ 0.31.0
+
+               * - ``GitPython``
+                 - `GitPython <https://github.com/gitpython-developers/GitPython>`_ 3.1.44
+
+               * - ``opencv-python-headless``
+                 - `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`_ 4.10.0.84
+
+               * - ``peft``
+                 - `PEFT <https://huggingface.co/docs/peft/en/index>`_ 0.14.0
+
+               * - ``protobuf``
+                 - `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
+
+               * - ``pytest``
+                 - `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
+
+               * - ``python-dotenv``
+                 - `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
+
+               * - ``seaborn``
+                 - `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
+
+               * - ``transformers``
+                 - `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
+
+         ``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
+
+         * `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+         {% if model_group.tag == "pre-training" and model.mad_tag in ["pyt_train_llama-3.1-8b", "pyt_train_llama-3.1-70b", "pyt_train_flux"] %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            .. rubric:: Pretraining
+
+            To start the pre-training benchmark, use the following command with the
+            appropriate options. See the following list of options and their descriptions.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -t pretrain -m {{ model.model_repo }} -p $datatype -s $sequence_length
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Name
+                 - Options
+                 - Description
+
+            {% if model.mad_tag == "pyt_train_llama-3.1-8b" %}
+               * - ``$datatype``
+                 - ``BF16`` or ``FP8``
+                 - Only Llama 3.1 8B supports FP8 precision.
+            {% else %}
+               * - ``$datatype``
+                 - ``BF16``
+                 - Only Llama 3.1 8B supports FP8 precision.
+            {% endif %}
+
+               * - ``$sequence_length``
+                 - Sequence length for the language model.
+                 - Between 2048 and 8192. 8192 by default.
+
+            {% if model.mad_tag == "pyt_train_flux" %}
+            .. container:: model-doc {{ model.mad_tag }}
+
+               .. note::
+
+                  Occasionally, downloading the Flux dataset might fail. In the event of this
+                  error, manually download it from Hugging Face at
+                  `black-forest-labs/FLUX.1-dev <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
+                  and save it to `/workspace/FluxBenchmark`. This ensures that the test script can access
+                  the required dataset.
+            {% endif %}
+         {% endif %}
+
+         {% if model_group.tag == "fine-tuning" %}
+         .. container:: model-doc {{ model.mad_tag }}
+
+            .. rubric:: Fine-tuning
+
+            To start the fine-tuning benchmark, use the following command with the
+            appropriate options. See the following list of options and their descriptions.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -t $training_mode -m {{ model.model_repo }} -p BF16 -s $sequence_length
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Name
+                 - Options
+                 - Description
+
+               * - ``$training_mode``
+                 - ``finetune_fw``
+                 - Full weight fine-tuning (BF16 supported)
+
+               * -
+                 - ``finetune_lora``
+                 - LoRA fine-tuning (BF16 supported)
+
+               * -
+                 - ``finetune_qlora``
+                 - QLoRA fine-tuning (BF16 supported)
+
+               * -
+                 - ``HF_finetune_lora``
+                 - LoRA fine-tuning with Hugging Face PEFT
+
+               * - ``$datatype``
+                 - ``BF16``
+                 - All models support BF16.
+
+               * - ``$sequence_length``
+                 - Between 2048 and 16384.
+                 - Sequence length for the language model.
+
+            .. note::
+
+               {{ model.model }} currently supports the following fine-tuning methods:
+
+            {% for method in model.training_modes %}
+               * ``{{ method }}``
+            {% endfor %}
+            {% if model.training_modes|length < 4 %}
+
+               The upstream `torchtune <https://github.com/pytorch/torchtune>`_ repository
+               does not currently provide YAML configuration files for other combinations of
+               model to fine-tuning method
+               However, you can still configure your own YAML files to enable support for
+               fine-tuning methods not listed here by following existing patterns in the
+               ``/workspace/torchtune/recipes/configs`` directory.
+            {% endif %}
+         {% endif %}
+      {% endfor %}
+   {% endfor %}
+
+               .. rubric:: Benchmarking examples
+
+               For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
+
+Further reading
+===============
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
+Previous versions
+=================
+
+See :doc:`pytorch-training-history` to find documentation for previous releases
+of the ``ROCm/pytorch-training`` Docker image.

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

@@ -0,0 +1,602 @@
+.. meta::
+   :description: How to train a model using Megatron-LM for ROCm.
+   :keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
+
+**********************************************
+Training a model with Primus and Megatron-Core
+**********************************************
+
+`Primus <https://github.com/AMD-AIG-AIMA/Primus>`__ is a unified and flexible
+LLM training framework designed to streamline training. It streamlines LLM
+training on AMD Instinct accelerators using a modular, reproducible configuration paradigm.
+Primus is backend-agnostic and supports multiple training engines -- including Megatron-Core.
+
+.. note::
+
+   Primus with the Megatron-Core backend is intended to replace ROCm
+   Megatron-LM in this Dockerized training environment. To learn how to migrate
+   workloads from Megatron-LM to Primus with Megatron-Core, see
+   :doc:`previous-versions/megatron-lm-primus-migration-guide`.
+
+For ease of use, AMD provides a ready-to-use Docker image for MI300 series accelerators
+containing essential components for Primus and Megatron-Core.
+
+.. note::
+
+   This Docker environment is based on Python 3.10 and Ubuntu 22.04. For an alternative environment with
+   Python 3.12 and Ubuntu 24.04, see the :doc:`previous ROCm Megatron-LM v25.6 Docker release <previous-versions/megatron-lm-v25.6>`.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+   {% set docker = dockers[0] %}
+   .. list-table::
+      :header-rows: 1
+
+      * - Software component
+        - Version
+
+      {% for component_name, component_version in docker.components.items() %}
+      * - {{ component_name }}
+        - {{ component_version }}
+      {% endfor %}
+
+.. _amd-primus-megatron-lm-model-support:
+
+Supported models
+================
+
+The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
+Some instructions, commands, and training examples in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
+
+   {% set model_groups = data.model_groups %}
+   .. raw:: html
+
+         <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+           <div class="row">
+             <div class="col-2 me-2 model-param-head">Model</div>
+             <div class="row col-10">
+      {% for model_group in model_groups %}
+               <div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+      {% endfor %}
+             </div>
+           </div>
+
+           <div class="row mt-1">
+             <div class="col-2 me-2 model-param-head">Model variant</div>
+             <div class="row col-10">
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+            {% if models|length % 3 == 0 %}
+               <div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% else %}
+               <div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% endif %}
+         {% endfor %}
+      {% endfor %}
+             </div>
+           </div>
+         </div>
+
+.. note::
+
+   Some models, such as Llama, require an external license agreement through
+   a third party (for example, Meta).
+
+System validation
+=================
+
+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-primus-megatron-lm-training:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+      {% set docker = dockers[0] %}
+
+   Environment setup
+   =================
+
+   Use the following instructions to set up the environment, configure the script to train models, and
+   reproduce the benchmark results on MI300X series accelerators with the ``{{ docker.pull_tag }}`` image.
+
+   .. _amd-primus-megatron-lm-requirements:
+
+   Download the Docker image
+   -------------------------
+
+   1. Use the following command to pull the Docker image from Docker Hub.
+
+      .. code-block:: shell
+
+         docker pull {{ docker.pull_tag }}
+
+   2. Launch the Docker container.
+
+      .. code-block:: shell
+
+         docker run -it \
+             --device /dev/dri \
+             --device /dev/kfd \
+             --device /dev/infiniband \
+             --network host --ipc host \
+             --group-add video \
+             --cap-add SYS_PTRACE \
+             --security-opt seccomp=unconfined \
+             --privileged \
+             -v $HOME:$HOME \
+             --shm-size 128G \
+             --name primus_training_env \
+             {{ docker.pull_tag }}
+
+3. Use these commands if you exit the ``primus_training_env`` container and need to return to it.
+
+   .. code-block:: shell
+
+      docker start primus_training_env
+      docker exec -it primus_training_env bash
+
+The Docker container hosts verified release tag ``v0.1.0-rc1`` of the `Primus
+<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1>`__ repository.
+
+.. _amd-primus-megatron-lm-environment-setup:
+
+Configuration
+=============
+
+Primus defines a training configuration in YAML for each model in
+`examples/megatron/configs <https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/megatron/configs>`__.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
+
+   {% set model_groups = data.model_groups %}
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+   .. container:: model-doc {{ model.mad_tag }}
+
+      To update training parameters for {{ model.model }}, you can update ``examples/megatron/configs/{{ model.config_name }}``.
+      Note that training configuration YAML files for other models follow this naming convention.
+
+      {% endfor %}
+   {% endfor %}
+
+.. note::
+
+   See :ref:`Key options <amd-primus-megatron-lm-benchmark-test-vars>` for more information on configuration options.
+
+Dataset options
+---------------
+
+You can use either mock data or real data for training.
+
+* Mock data can be useful for testing and validation. Use the ``mock_data`` field to toggle between mock and real data. The default
+  value is ``true`` for enabled.
+
+  .. code-block:: yaml
+
+     mock_data: true
+
+* If you're using a real dataset, update the ``train_data_path`` field to point to the location of your dataset.
+
+  .. code-block:: bash
+
+     mock_data: false
+     train_data_path: /path/to/your/dataset
+
+  Ensure that the files are accessible inside the Docker container.
+
+.. _amd-primus-megatron-lm-tokenizer:
+
+Tokenizer
+---------
+
+In Primus, each model uses a tokenizer from Hugging Face. For example, Llama
+3.1 8B model uses ``tokenizer_model: meta-llama/Llama-3.1-8B`` and
+``tokenizer_type: Llama3Tokenizer`` defined in the `llama3.1-8B model
+<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/primus/configs/models/megatron/llama3.1_8B.yaml>`__
+definition. As such, you need to set the ``HF_TOKEN`` environment variable with
+right permissions to access the tokenizer for each model.
+
+.. code-block:: bash
+
+   # Export your HF_TOKEN in the workspace
+   export HF_TOKEN=<your_hftoken>
+
+.. _amd-primus-megatron-lm-run-training:
+
+Run training
+============
+
+Use the following example commands to set up the environment, configure
+:ref:`key options <amd-primus-megatron-lm-benchmark-test-vars>`, and run training on
+MI300X series accelerators with the AMD Megatron-LM environment.
+
+Single node training
+--------------------
+
+To run training on a single node, navigate to ``/workspace/Primus`` and use the following setup command:
+
+.. code-block:: shell
+
+   pip install -r requirements.txt
+   export HSA_NO_SCRATCH_RECLAIM=1
+   export NVTE_CK_USES_BWD_V3=1
+
+Once setup is complete, run the appropriate training command.
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.3-70b
+
+   To run pre-training for Llama 3.3 70B BF16, run:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
+      bash ./examples/run_pretrain.sh \
+          --micro_batch_size 2 \
+          --global_batch_size 16 \
+          --train_iters 50
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
+
+   To run pre-training for Llama 3.1 8B FP8, run:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
+      bash ./examples/run_pretrain.sh \
+          --train_iters 50 \
+          --fp8 hybrid
+
+   For Llama 3.1 8B BF16, use the following command:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
+      bash ./examples/run_pretrain.sh --train_iters 50
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-70b
+
+   To run pre-training for Llama 3.1 70B BF16, run:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
+      bash ./examples/run_pretrain.sh \
+           --train_iters 50
+
+   To run the training on a single node for Llama 3.1 70B FP8 with proxy, use the following command:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
+      bash ./examples/run_pretrain.sh \
+          --train_iters 50 \
+          --num_layers 40 \
+          --fp8 hybrid \
+          --no_fp8_weight_transpose_cache true
+
+   .. note::
+
+      Use two or more nodes to run the *full* Llama 70B model with FP8 precision.
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-7b
+
+   To run pre-training for Llama 2 7B FP8, run:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
+      bash ./examples/run_pretrain.sh \
+          --train_iters 50 \
+          --fp8 hybrid
+
+   To run pre-training for Llama 2 7B BF16, run:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
+      bash ./examples/run_pretrain.sh --train_iters 50
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-70b
+
+   To run pre-training for Llama 2 70B BF16, run:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
+      bash ./examples/run_pretrain.sh --train_iters 50 
+
+.. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v3-proxy
+
+   To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy, 
+   use the following command:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/deepseek_v3-pretrain.yaml \
+      bash examples/run_pretrain.sh \
+          --num_layers 3 \
+          --moe_layer_freq 1 \
+          --train_iters 50
+
+.. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
+
+   To run training on a single node for DeepSeek-V2-Lite (MoE with expert parallel),
+   use the following command:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/deepseek_v2_lite-pretrain.yaml \
+      bash examples/run_pretrain.sh \
+          --global_batch_size 256 \
+          --train_iters 50
+
+.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x7b
+
+   To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
+   use the following command:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/mixtral_8x7B_v0.1-pretrain.yaml \
+      bash examples/run_pretrain.sh --train_iters 50
+
+.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
+
+   To run training on a single node for Mixtral 8x7B (MoE with expert parallel) with 4-layer proxy,
+   use the following command:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/mixtral_8x22B_v0.1-pretrain.yaml \
+      bash examples/run_pretrain.sh \
+          --num_layers 4 \
+          --pipeline_model_parallel_size 1 \
+          --micro_batch_size 1 \
+          --global_batch_size 16 \
+          --train_iters 50
+
+.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-7b
+
+   To run training on a single node for Qwen 2.5 7B BF16, use the following
+   command:
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
+      bash examples/run_pretrain.sh --train_iters 50
+
+   For FP8, use the following command.
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
+      bash examples/run_pretrain.sh \
+          --train_iters 50 \
+          --fp8 hybrid
+
+.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-72b
+
+   To run the training on a single node for Qwen 2.5 72B BF16, use the following command.
+
+   .. code-block:: shell
+
+      EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
+      bash examples/run_pretrain.sh --train_iters 50
+
+Multi-node training examples
+----------------------------
+
+To run training on multiple nodes, you can use the
+`run_slurm_pretrain.sh <https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/run_slurm_pretrain.sh>`__
+to launch the multi-node workload. Use the following steps to setup your environment:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+   {% set docker = dockers[0] %}
+
+   .. code-block:: shell
+
+      cd /workspace/Primus/
+      export DOCKER_IMAGE={{ docker.pull_tag }}
+      export HF_TOKEN=<your_HF_token>
+      export HSA_NO_SCRATCH_RECLAIM=1
+      export NVTE_CK_USES_BWD_V3=1
+      export NCCL_IB_HCA=<your_NCCL_IB_HCA> # specify which RDMA interfaces to use for communication
+      export NCCL_SOCKET_IFNAME=<your_NCCL_SOCKET_IFNAME> # your Network Interface
+      export GLOO_SOCKET_IFNAME=<your_GLOO_SOCKET_IFNAME> # your Network Interface
+      export NCCL_IB_GID_INDEX=3 # Set InfiniBand GID index for NCCL communication. Default is 3 for ROCE
+
+.. note::
+
+   * Make sure correct network drivers are installed on the nodes. If inside a Docker, either install the drivers inside the Docker container or pass the network drivers from the host while creating Docker container.
+   * If ``NCCL_IB_HCA`` and ``NCCL_SOCKET_IFNAME`` are not set, Primus will try to auto-detect. However, since NICs can vary accross different cluster, it is encouraged to explicitly export your NCCL parameters for the cluster.
+   * To find your network interface, you can use ``ip a``.
+   * To find RDMA interfaces, you can use ``ibv_devices`` to get the list of all the RDMA/IB  devices.
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.3-70b
+
+   To train Llama 3.3 70B FP8 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      NNODES=8 EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
+      bash examples/run_slurm_pretrain.sh \
+          --micro_batch_size 4 \
+          --global_batch_size 256 \
+          --recompute_num_layers 80 \
+          --no_fp8_weight_transpose_cache true \
+          --fp8 hybrid
+
+   To train Llama 3.3 70B BF16 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      NNODES=8 EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
+      bash examples/run_slurm_pretrain.sh \
+          --micro_batch_size 1 \
+          --global_batch_size 256 \
+          --recompute_num_layers 12
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
+
+   To train Llama 3.1 8B FP8 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      # Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case 
+      NNODES=8 EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
+      bash ./examples/run_slurm_pretrain.sh \
+          --global_batch_size 1024 \
+          --fp8 hybrid
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-70b
+
+   To train Llama 3.1 70B FP8 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      NNODES=8 EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
+      bash examples/run_slurm_pretrain.sh \
+          --micro_batch_size 4 \
+          --global_batch_size 256 \
+          --recompute_num_layers 80 \
+          --no_fp8_weight_transpose_cache true \
+          --fp8 hybrid
+
+   To train Llama 3.1 70B BF16 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      NNODES=8 EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
+      bash examples/run_slurm_pretrain.sh \
+          --micro_batch_size 1 \
+          --global_batch_size 256 \
+          --recompute_num_layers 12
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-7b
+
+   To train Llama 2 8B FP8 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      # Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case 
+      NNODES=8 EXP=examples/megatron/configs/llama2_7B-pretrain.yaml bash ./examples/run_slurm_pretrain.sh --global_batch_size 2048 --fp8 hybrid
+
+.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-70b
+
+   To train Llama 2 70B FP8 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      NNODES=8 EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
+      bash examples/run_slurm_pretrain.sh \
+          --micro_batch_size 10 \
+          --global_batch_size 640 \
+          --recompute_num_layers 80 \
+          --no_fp8_weight_transpose_cache true \
+          --fp8 hybrid
+
+   To train Llama 2 70B BF16 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      NNODES=8 EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
+      bash ./examples/run_slurm_pretrain.sh \
+          --micro_batch_size 2 \
+          --global_batch_size 1536 \
+          --recompute_num_layers 12
+
+.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x7b
+
+   To train Mixtral 8x7B BF16 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      NNODES=8 EXP=examples/megatron/configs/mixtral_8x7B_v0.1-pretrain.yaml \
+      bash examples/run_slurm_pretrain.sh \
+          --micro_batch_size 2 \
+          --global_batch_size 256
+
+.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-72b
+
+   To train Qwen2.5 72B FP8 on 8 nodes, run:
+
+   .. code-block:: shell
+
+      NNODES=8 EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
+      bash examples/run_slurm_pretrain.sh \
+          --micro_batch_size 8 \
+          --global_batch_size 512 \
+          --recompute_num_layers 80 \
+          --no_fp8_weight_transpose_cache true \
+          --fp8 hybrid
+
+.. _amd-primus-megatron-lm-benchmark-test-vars:
+
+Key options
+-----------
+
+The following are key options to take note of
+
+fp8
+  ``hybrid`` enables FP8 GEMMs.
+
+use_torch_fsdp2
+  ``use_torch_fsdp2: 1``  enables torch fsdp-v2. If FSDP is enabled,
+  set ``use_distributed_optimizer`` and ``overlap_param_gather`` to ``false``.
+
+profile
+  To enable PyTorch profiling, set these parameters:
+
+  .. code-block:: yaml
+
+     profile: true
+     use_pytorch_profiler: true
+     profile_step_end: 7
+     profile_step_start: 6
+
+train_iters
+  The total number of iterations (default: 50).
+
+mock_data
+  True by default.
+
+micro_batch_size
+  Micro batch size.
+
+global_batch_size
+  Global batch size.
+
+recompute_granularity
+  For activation checkpointing.
+
+num_layers
+  For using a reduced number of layers as with proxy models.
+
+Previous versions
+=================
+
+See :doc:`previous-versions/megatron-lm-history` to find documentation for previous releases
+of the ``ROCm/megatron-lm`` Docker image.
+
+This training environment now uses Primus with Megatron as the primary
+configuration. Limited support for the legacy ROCm Megatron-LM is still
+available. For instructions on using ROCm Megatron-LM, see the
+:doc:`megatron-lm` document.

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

@@ -9,28 +9,25 @@ Training a model with PyTorch for ROCm
 PyTorch is an open-source machine learning framework that is widely used for
 model training with GPU-optimized components for transformer-based models.
 
-The `PyTorch for ROCm training Docker <https://hub.docker.com/r/rocm/pytorch-training/tags>`_
-(``rocm/pytorch-training:v25.6``) image provides a prebuilt optimized environment for fine-tuning and pretraining a
-model on AMD Instinct MI325X and MI300X accelerators. It includes the following software components to accelerate
-training workloads:
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
 
-+--------------------------+--------------------------------+
-| Software component       | Version                        |
-+==========================+================================+
-| ROCm                     | 6.3.4                          |
-+--------------------------+--------------------------------+
-| PyTorch                  | 2.8.0a0+git7d205b2             |
-+--------------------------+--------------------------------+
-| Python                   | 3.10.17                        |
-+--------------------------+--------------------------------+
-| Transformer Engine       | 1.14.0+2f85f5f2                |
-+--------------------------+--------------------------------+
-| Flash Attention          | 3.0.0.post1                    |
-+--------------------------+--------------------------------+
-| hipBLASLt                | 0.15.0-8c6919d                 |
-+--------------------------+--------------------------------+
-| Triton                   | 3.3.0                          |
-+--------------------------+--------------------------------+
+   {% set dockers = data.dockers %}
+   {% set docker = dockers[0] %}
+   The `PyTorch for ROCm training Docker <{{ docker.docker_hub_url }}>`__
+   (``{{ docker.pull_tag }}``) image provides a prebuilt optimized environment for fine-tuning and pretraining a
+   model on AMD Instinct MI325X and MI300X accelerators. It includes the following software components to accelerate
+   training workloads:
+
+   .. list-table::
+      :header-rows: 1
+
+      * - Software component
+        - Version
+
+      {% for component_name, component_version in docker.components.items() %}
+      * - {{ component_name }}
+        - {{ component_version }}
+      {% endfor %}
 
 .. _amd-pytorch-training-model-support:
 
@@ -38,26 +35,27 @@ Supported models
 ================
 
 The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
+Some instructions, commands, and training recommendations in this documentation might
+vary by model -- select one to get started.
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
 
-   {% set unified_docker = data.unified_docker.latest %}
+   {% set unified_docker = data.dockers[0] %}
    {% set model_groups = data.model_groups %}
-
    .. raw:: html
 
       <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
         <div class="row">
-          <div class="col-2 me-2 model-param-head">Workload</div>
+          <div class="col-2 me-2 model-param-head">Model group</div>
           <div class="row col-10">
    {% for model_group in model_groups %}
-            <div class="col-6 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+            <div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
    {% endfor %}
           </div>
         </div>
 
         <div class="row mt-1">
-          <div class="col-2 me-2 model-param-head">Model</div>
+          <div class="col-2 me-2 model-param-head">Model variant</div>
           <div class="row col-10">
    {% for model_group in model_groups %}
       {% set models = model_group.models %}
@@ -73,84 +71,116 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
         </div>
       </div>
 
-   .. note::
 
-      Some models require an external license agreement through a third party (for example, Meta).
+   .. _amd-pytorch-training-supported-training-modes:
 
-   .. _amd-pytorch-training-performance-measurements:
+   The following table lists supported training modes per model.
 
-   Performance measurements
-   ========================
+   .. dropdown:: Supported training modes
 
-   To evaluate performance, the
+      .. list-table::
+         :header-rows: 1
+
+         * - Model
+           - Supported training modes
+
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+         * - {{ model.model }}
+           - ``{{ model.training_modes | join('``, ``') }}``
+
+         {% endfor %}
+      {% endfor %}
+
+      .. note::
+
+         Some model and fine-tuning combinations are not listed. This is
+         because the `upstream torchtune repository <https://github.com/pytorch/torchtune>`__
+         doesn't provide default YAML configurations for them.
+         For advanced usage, you can create a custom configuration to enable
+         unlisted fine-tuning methods by using an existing file in the
+         ``/workspace/torchtune/recipes/configs`` directory as a template.
+
+.. _amd-pytorch-training-performance-measurements:
+
+Performance measurements
+========================
+
+To evaluate performance, the
+`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
+page provides reference throughput and latency measurements for training
+popular AI models.
+
+.. note::
+
+   The performance data presented in
    `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
-   page provides reference throughput and latency measurements for training
-   popular AI models.
+   should not be interpreted as the peak performance achievable by AMD
+   Instinct MI325X and MI300X accelerators or ROCm software.
 
-   .. note::
+System validation
+=================
 
-      The performance data presented in
-      `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
-      should not be interpreted as the peak performance achievable by AMD
-      Instinct MI325X and MI300X accelerators or ROCm software.
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
 
-   System validation
-   =================
+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.
 
-   Before running AI workloads, it's important to validate that your AMD hardware is configured
-   correctly and performing optimally.
+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.
 
-   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.
+This Docker image is optimized for specific model configurations outlined
+below. Performance can vary for other training workloads, as AMD
+doesn’t test configurations and run conditions outside those described.
 
-   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.
+Run training
+============
 
-   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.
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
 
-   Benchmarking
-   ============
+   {% set unified_docker = data.dockers[0] %}
+   {% set model_groups = data.model_groups %}
 
-   Once the setup is complete, choose between two options to start benchmarking:
+   Once the setup is complete, choose between two options to start benchmarking training:
 
    .. tab-set::
 
       .. tab-item:: MAD-integrated benchmarking
 
-         Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
-         directory and install the required packages on the host machine.
+         1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+            directory and install the required packages on the host machine.
 
-         .. code-block:: shell
+            .. code-block:: shell
 
-            git clone https://github.com/ROCm/MAD
-            cd MAD
-            pip install -r requirements.txt
+               git clone https://github.com/ROCm/MAD
+               cd MAD
+               pip install -r requirements.txt
 
    {% for model_group in model_groups %}
       {% for model in model_group.models %}
 
          .. container:: model-doc {{ model.mad_tag }}
 
-            For example, use this command to run the performance benchmark test on the {{ model.model }} model
-            using one GPU with the {{ model.precision }} data type on the host machine.
+            2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
+               using one node with the {{ model.precision }} data type on the host machine.
 
-            .. code-block:: shell
+               .. code-block:: shell
 
-               export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
-               madengine run \
-                   --tags {{ model.mad_tag }} \
-                   --keep-model-dir \
-                   --live-output \
-                   --timeout 28800
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{ model.mad_tag }} \
+                      --keep-model-dir \
+                      --live-output \
+                      --timeout 28800
 
-            MAD launches a Docker container with the name
-            ``container_ci-{{ model.mad_tag }}``, for example. The latency and throughput reports of the
-            model are collected in the following path: ``~/MAD/perf.csv``.
+               MAD launches a Docker container with the name
+               ``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
+               model are collected in ``~/MAD/perf.csv``.
 
       {% endfor %}
    {% endfor %}
@@ -159,222 +189,213 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
 
          .. rubric:: Download the Docker image and required packages
 
-         Use the following command to pull the Docker image from Docker Hub.
+         1. 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 }}
 
-         Run the Docker container.
+         2. Run the Docker container.
 
-         .. code-block:: shell
+            .. code-block:: shell
 
-            docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME:$HOME -v  $HOME/.ssh:/root/.ssh --shm-size 64G --name training_env {{ unified_docker.pull_tag }}
+               docker run -it \
+                   --device /dev/dri \
+                   --device /dev/kfd \
+                   --network host \
+                   --ipc host \
+                   --group-add video \
+                   --cap-add SYS_PTRACE \
+                   --security-opt seccomp=unconfined \
+                   --privileged \
+                   -v $HOME:$HOME \
+                   -v $HOME/.ssh:/root/.ssh \
+                   --shm-size 64G \
+                   --name training_env \
+                   {{ unified_docker.pull_tag }}
 
-         Use these commands if you exit the ``training_env`` container and need to return to it.
+            Use these commands if you exit the ``training_env`` container and need to return to it.
 
-         .. code-block:: shell
+            .. code-block:: shell
 
-            docker start training_env
-            docker exec -it training_env bash
+               docker start training_env
+               docker exec -it training_env bash
 
-         In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
-         repository and navigate to the benchmark scripts directory
-         ``/workspace/MAD/scripts/pytorch_train``.
+         3. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
+            repository and navigate to the benchmark scripts directory
+            ``/workspace/MAD/scripts/pytorch_train``.
 
-         .. code-block:: shell
+            .. code-block:: shell
 
-            git clone https://github.com/ROCm/MAD
-            cd MAD/scripts/pytorch_train
+               git clone https://github.com/ROCm/MAD
+               cd MAD/scripts/pytorch_train
 
          .. rubric:: Prepare training datasets and dependencies
 
-         The following benchmarking examples require downloading models and datasets
-         from Hugging Face. To ensure successful access to gated repos, set your
-         ``HF_TOKEN``.
+         1. The following benchmarking examples require downloading models and datasets
+            from Hugging Face. To ensure successful access to gated repos, set your
+            ``HF_TOKEN``.
 
-         .. code-block:: shell
+            .. code-block:: shell
 
-            export HF_TOKEN=$your_personal_hugging_face_access_token
+               export HF_TOKEN=$your_personal_hugging_face_access_token
 
-         Run the setup script to install libraries and datasets needed for benchmarking.
+         2. Run the setup script to install libraries and datasets needed for benchmarking.
 
-         .. code-block:: shell
+            .. code-block:: shell
 
-            ./pytorch_benchmark_setup.sh
+               ./pytorch_benchmark_setup.sh
 
-         .. container:: model-doc pyt_train_llama-3.1-8b
+            .. container:: model-doc pyt_train_llama-3.1-8b
 
-            ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
+               ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
 
-            .. list-table::
-               :header-rows: 1
+               .. list-table::
+                  :header-rows: 1
 
-               * - Library
-                 - Reference
+                  * - Library
+                    - Reference
 
-               * - ``accelerate``
-                 - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
+                  * - ``accelerate``
+                    - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
 
-               * - ``datasets``
-                 - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+                  * - ``datasets``
+                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
 
-         .. container:: model-doc pyt_train_llama-3.1-70b
+            .. container:: model-doc pyt_train_llama-3.1-70b
 
-            ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
+               ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
 
-            .. list-table::
-               :header-rows: 1
+               .. list-table::
+                  :header-rows: 1
 
-               * - Library
-                 - Reference
+                  * - Library
+                    - Reference
 
-               * - ``datasets``
-                 - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+                  * - ``datasets``
+                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
 
-               * - ``torchdata``
-                 - `TorchData <https://pytorch.org/data/beta/index.html>`_
+                  * - ``torchdata``
+                    - `TorchData <https://pytorch.org/data/beta/index.html>`_
 
-               * - ``tomli``
-                 - `Tomli <https://pypi.org/project/tomli/>`_
+                  * - ``tomli``
+                    - `Tomli <https://pypi.org/project/tomli/>`_
 
-               * - ``tiktoken``
-                 - `tiktoken <https://github.com/openai/tiktoken>`_
+                  * - ``tiktoken``
+                    - `tiktoken <https://github.com/openai/tiktoken>`_
 
-               * - ``blobfile``
-                 - `blobfile <https://pypi.org/project/blobfile/>`_
+                  * - ``blobfile``
+                    - `blobfile <https://pypi.org/project/blobfile/>`_
 
-               * - ``tabulate``
-                 - `tabulate <https://pypi.org/project/tabulate/>`_
+                  * - ``tabulate``
+                    - `tabulate <https://pypi.org/project/tabulate/>`_
 
-               * - ``wandb``
-                 - `Weights & Biases <https://github.com/wandb/wandb>`_
+                  * - ``wandb``
+                    - `Weights & Biases <https://github.com/wandb/wandb>`_
 
-               * - ``sentencepiece``
-                 - `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
+                  * - ``sentencepiece``
+                    - `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
 
-               * - ``tensorboard``
-                 - `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
+                  * - ``tensorboard``
+                    - `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
 
-         .. container:: model-doc pyt_train_flux
+            .. container:: model-doc pyt_train_flux
 
-            ``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
+               ``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
 
-            .. list-table::
-               :header-rows: 1
+               .. list-table::
+                  :header-rows: 1
 
-               * - Library
-                 - Reference
+                  * - Library
+                    - Reference
 
-               * - ``accelerate``
-                 - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
+                  * - ``accelerate``
+                    - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
 
-               * - ``datasets``
-                 - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+                  * - ``datasets``
+                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
 
-               * - ``sentencepiece``
-                 - `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
+                  * - ``sentencepiece``
+                    - `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
 
-               * - ``tensorboard``
-                 - `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
+                  * - ``tensorboard``
+                    - `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
 
-               * - ``csvkit``
-                 - `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
+                  * - ``csvkit``
+                    - `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
 
-               * - ``deepspeed``
-                 - `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
+                  * - ``deepspeed``
+                    - `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
 
-               * - ``diffusers``
-                 - `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`_ 0.31.0
+                  * - ``diffusers``
+                    - `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`_ 0.31.0
 
-               * - ``GitPython``
-                 - `GitPython <https://github.com/gitpython-developers/GitPython>`_ 3.1.44
+                  * - ``GitPython``
+                    - `GitPython <https://github.com/gitpython-developers/GitPython>`_ 3.1.44
 
-               * - ``opencv-python-headless``
-                 - `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`_ 4.10.0.84
+                  * - ``opencv-python-headless``
+                    - `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`_ 4.10.0.84
 
-               * - ``peft``
-                 - `PEFT <https://huggingface.co/docs/peft/en/index>`_ 0.14.0
+                  * - ``peft``
+                    - `PEFT <https://huggingface.co/docs/peft/en/index>`_ 0.14.0
 
-               * - ``protobuf``
-                 - `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
+                  * - ``protobuf``
+                    - `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
 
-               * - ``pytest``
-                 - `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
+                  * - ``pytest``
+                    - `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
 
-               * - ``python-dotenv``
-                 - `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
+                  * - ``python-dotenv``
+                    - `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
 
-               * - ``seaborn``
-                 - `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
+                  * - ``seaborn``
+                    - `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
 
-               * - ``transformers``
-                 - `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
+                  * - ``transformers``
+                    - `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
 
-         ``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
+            ``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
 
-         * `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
+            * `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
 
    {% for model_group in model_groups %}
       {% for model in model_group.models %}
-         {% if model_group.tag == "pre-training" and model.mad_tag in ["pyt_train_llama-3.1-8b", "pyt_train_llama-3.1-70b", "pyt_train_flux"] %}
+         {% set training_modes = model.training_modes %}
+         {% set training_mode_descs = {
+            "pretrain": "Benchmark pre-training.",
+            "HF_pretrain": "Llama 3.1 8B pre-training with FP8 precision."
+         } %}
+         {% set available_modes = training_modes | select("in", ["pretrain", "HF_pretrain"]) | list %}
+         {% if available_modes %}
 
          .. container:: model-doc {{ model.mad_tag }}
 
-            .. rubric:: Pretraining
+            .. rubric:: Pre-training
 
             To start the pre-training benchmark, use the following command with the
             appropriate options. See the following list of options and their descriptions.
 
             .. code-block:: shell
 
-               ./pytorch_benchmark_report.sh -t pretrain -m {{ model.model_repo }} -p $datatype -s $sequence_length
-
-            .. list-table::
-               :header-rows: 1
-
-               * - Name
-                 - Options
-                 - Description
-
-            {% if model.mad_tag == "pyt_train_llama-3.1-8b" %}
-               * - ``$datatype``
-                 - ``BF16`` or ``FP8``
-                 - Only Llama 3.1 8B supports FP8 precision.
-            {% else %}
-               * - ``$datatype``
-                 - ``BF16``
-                 - Only Llama 3.1 8B supports FP8 precision.
-            {% endif %}
-
-               * - ``$sequence_length``
-                 - Sequence length for the language model.
-                 - Between 2048 and 8192. 8192 by default.
+               ./pytorch_benchmark_report.sh -t {% if available_modes | length == 1 %}{{ available_modes[0] }}{% else %}$training_mode{% endif %} \
+                   -m {{ model.model_repo }} \
+                   -p $datatype \
+                   -s $sequence_length
 
             {% if model.mad_tag == "pyt_train_flux" %}
             .. container:: model-doc {{ model.mad_tag }}
 
                .. note::
 
+                  Currently, FLUX models are not supported out-of-the-box on {{ unified_docker.pull_tag }}.
+                  To use FLUX, refer to the previous version of the ``pytorch-training`` Docker: :doc:`previous-versions/pytorch-training-v25.6`
+
                   Occasionally, downloading the Flux dataset might fail. In the event of this
                   error, manually download it from Hugging Face at
                   `black-forest-labs/FLUX.1-dev <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
                   and save it to `/workspace/FluxBenchmark`. This ensures that the test script can access
                   the required dataset.
             {% endif %}
-         {% endif %}
-
-         {% if model_group.tag == "fine-tuning" %}
-         .. container:: model-doc {{ model.mad_tag }}
-
-            .. rubric:: Fine-tuning
-
-            To start the fine-tuning benchmark, use the following command with the
-            appropriate options. See the following list of options and their descriptions.
-
-            .. code-block:: shell
-
-               ./pytorch_benchmark_report.sh -t $training_mode -m {{ model.model_repo }} -p BF16 -s $sequence_length
 
             .. list-table::
                :header-rows: 1
@@ -383,53 +404,143 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
                  - Options
                  - Description
 
-               * - ``$training_mode``
-                 - ``finetune_fw``
-                 - Full weight fine-tuning (BF16 supported)
-
-               * -
-                 - ``finetune_lora``
-                 - LoRA fine-tuning (BF16 supported)
-
-               * -
-                 - ``finetune_qlora``
-                 - QLoRA fine-tuning (BF16 supported)
-
-               * -
-                 - ``HF_finetune_lora``
-                 - LoRA fine-tuning with Hugging Face PEFT
+               {% for mode in available_modes %}
+               * - {% if loop.first %}``$training_mode``{% endif %}
+                 - ``{{ mode }}``
+                 - {{ training_mode_descs[mode] }}
+               {% endfor %}
 
                * - ``$datatype``
-                 - ``BF16``
-                 - All models support BF16.
+                 - ``BF16``{% if model.mad_tag == "pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
+                 - Only Llama 3.1 8B supports FP8 precision.
+
+               * - ``$sequence_length``
+                 - Sequence length for the language model.
+                 - Between 2048 and 8192. 8192 by default.
+         {% endif %}
+
+         {% set training_mode_descs = {
+            "finetune_fw": "Full weight fine-tuning (BF16 and FP8 supported).",
+            "finetune_lora": "LoRA fine-tuning (BF16 supported).",
+            "finetune_qlora": "QLoRA fine-tuning (BF16 supported).",
+            "HF_finetune_lora": "LoRA fine-tuning with Hugging Face PEFT.",
+         } %}
+         {% set available_modes = training_modes | select("in", ["finetune_fw", "finetune_lora", "finetune_qlora", "HF_finetune_lora"]) | list %}
+         {% if available_modes %}
+         .. container:: model-doc {{ model.mad_tag }}
+
+            .. rubric:: Fine-tuning
+
+            To start the fine-tuning benchmark, use the following command with the
+            appropriate options. See the following list of options and their descriptions.
+            See :ref:`supported training modes <amd-pytorch-training-supported-training-modes>`.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -t $training_mode \
+                   -m {{ model.model_repo }} \
+                   -p $datatype \
+                   -s $sequence_length
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Name
+                 - Options
+                 - Description
+
+               {% for mode in available_modes %}
+               * - {% if loop.first %}``$training_mode``{% endif %}
+                 - ``{{ mode }}``
+                 - {{ training_mode_descs[mode] }}
+               {% endfor %}
+
+               * - ``$datatype``
+                 - ``BF16``{% if "finetune_fw" in available_modes %} or ``FP8``{% endif %}
+                 - All models support BF16.{% if "finetune_fw" in available_modes %} FP8 is only available for full weight fine-tuning.{% endif %}
 
                * - ``$sequence_length``
                  - Between 2048 and 16384.
                  - Sequence length for the language model.
 
+            {% if model.mad_tag in ["pyt_train_llama3.2-vision-11b", "pyt_train_llama-3.2-vision-90b"] %}
             .. note::
 
-               {{ model.model }} currently supports the following fine-tuning methods:
+               For LoRA and QLoRA support with vision models (Llama 3.2 11B and 90B),
+               use the following torchtune commit for compatibility:
 
-            {% for method in model.training_modes %}
-               * ``{{ method }}``
-            {% endfor %}
-            {% if model.training_modes|length < 4 %}
+               .. code-block:: shell
+
+                  git checkout 48192e23188b1fc524dd6d127725ceb2348e7f0e
+
+            {% elif model.mad_tag in ["pyt_train_llama-2-7b", "pyt_train_llama-2-13b", "pyt_train_llama-2-70b"] %}
+            .. note::
+
+               You might encounter the following error with Llama 2: ``ValueError: seq_len (16384) of
+               input tensor should be smaller than max_seq_len (4096)``.
+               This error indicates that an input sequence is longer than the model's maximum context window.
+
+               Ensure your tokenized input does not exceed the model's ``max_seq_len`` (4096
+               tokens in this case). You can resolve this by truncating the input or splitting
+               it into smaller chunks before passing it to the model.
+
+               Note on reproducibility: The results in this guide are based on
+               commit ``b4c98ac`` from the upstream
+               `<https://github.com/pytorch/torchtune>`__ repository. For the
+               latest updates, you can use the main branch.
 
-               The upstream `torchtune <https://github.com/pytorch/torchtune>`_ repository
-               does not currently provide YAML configuration files for other combinations of
-               model to fine-tuning method
-               However, you can still configure your own YAML files to enable support for
-               fine-tuning methods not listed here by following existing patterns in the
-               ``/workspace/torchtune/recipes/configs`` directory.
             {% endif %}
          {% endif %}
       {% endfor %}
    {% endfor %}
 
-               .. rubric:: Benchmarking examples
+            .. rubric:: Benchmarking examples
 
-               For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
+            For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
+
+Multi-node training
+-------------------
+
+Pre-training
+~~~~~~~~~~~~
+
+Multi-node training with torchtitan is supported. The provided SLURM script is pre-configured for Llama 3 70B.
+
+To launch the training job on a SLURM cluster for Llama 3 70B, run the following commands from the MAD repository.
+
+.. code-block:: shell
+
+   # In the MAD repository
+   cd scripts/pytorch_train
+   sbatch run_slurm_train.sh
+
+Fine-tuning
+~~~~~~~~~~~
+
+Multi-node training with torchtune is supported. The provided SLURM script is pre-configured for Llama 3.3 70B.
+
+To launch the training job on a SLURM cluster for Llama 3.3 70B, run the following commands from the MAD repository.
+
+.. code-block:: shell
+
+   huggingface-cli login # Get access to HF Llama model space
+   huggingface-cli download meta-llama/Llama-3.3-70B-Instruct --local-dir ./models/Llama-3.3-70B-Instruct # Download the Llama 3.3 model locally
+   # In the MAD repository
+   cd scripts/pytorch_train
+   sbatch Torchtune_Multinode.sh
+
+.. note::
+
+   Information regarding benchmark setup:
+
+   * By default, Llama 3.3 70B is fine-tuned using ``alpaca_dataset``.
+   * You can adjust the torchtune `YAML configuration file
+     <https://github.com/pytorch/torchtune/blob/main/recipes/configs/llama3_3/70B_full_multinode.yaml>`__
+     if you're using a different model.
+   * The number of nodes and other parameters can be tuned in the SLURM script ``Torchtune_Multinode.sh``.
+   * Set the ``mounting_paths`` inside the SLURM script.
+
+Once the run is finished, you can find the log files in the ``result_torchtune/`` directory.
 
 Further reading
 ===============

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

@@ -21,6 +21,8 @@ In this guide, you'll learn about:
 
 - Training a model
 
+  - :doc:`With Primus (Megatron-LM backend) <benchmark-docker/primus-megatron>`
+
   - :doc:`With Megatron-LM <benchmark-docker/megatron-lm>`
 
   - :doc:`With PyTorch <benchmark-docker/pytorch-training>`

docs/sphinx/_toc.yml.in

@@ -27,6 +27,24 @@ subtrees:
     title: ROCm on Radeon GPUs
   - file: how-to/deep-learning-rocm.md
     title: Deep learning frameworks
+    subtrees:
+    - entries:
+      - file: compatibility/ml-compatibility/pytorch-compatibility.rst
+        title: PyTorch compatibility
+      - file: compatibility/ml-compatibility/tensorflow-compatibility.rst
+        title: TensorFlow compatibility  
+      - file: compatibility/ml-compatibility/jax-compatibility.rst
+        title: JAX compatibility
+      - file: compatibility/ml-compatibility/verl-compatibility.rst
+        title: verl compatibility  
+      - file: compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
+        title: Stanford Megatron-LM compatibility
+      - file: compatibility/ml-compatibility/dgl-compatibility.rst
+        title: DGL compatibility  
+      - file: compatibility/ml-compatibility/megablocks-compatibility.rst
+        title: Megablocks compatibility
+      - file: compatibility/ml-compatibility/taichi-compatibility.rst
+        title: Taichi compatibility 
   - file: how-to/build-rocm.rst
     title: Build ROCm from source
 
@@ -44,8 +62,8 @@ subtrees:
         title: Training
         subtrees:
         - entries:
-          - file: how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
-            title: Train a model with Megatron-LM
+          - file: how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
+            title: Train a model with Primus and Megatron-Core
           - file: how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
             title: Train a model with PyTorch
           - file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst