Merge pull request #4393 from ROCm/docs_fix_arch

Docs: Fix gpu-arch-spec.rst

.azuredevops/components/MIOpen.yml

@@ -101,7 +101,7 @@ jobs:
         -DMIOPEN_BACKEND=HIP
         -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
         -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/miopen-deps
-        -DAMDGPU_TARGETS=$(JOB_GPU_TARGET)
+        -DGPU_TARGETS=$(JOB_GPU_TARGET)
         -DMIOPEN_ENABLE_AI_KERNEL_TUNING=OFF
         -DMIOPEN_ENABLE_AI_IMMED_MODE_FALLBACK=OFF
         -DCMAKE_BUILD_TYPE=Release
@@ -129,6 +129,8 @@ jobs:
   variables:
   - group: common
   - template: /.azuredevops/variables-global.yml
+  - name: ROCM_PATH
+    value: $(Agent.BuildDirectory)/rocm
   pool: $(JOB_TEST_POOL)
   workspace:
     clean: all

.azuredevops/components/ROCR-Runtime.yml

@@ -123,16 +123,13 @@ jobs:
       targetType: 'inline'
       workingDirectory: $(Build.SourcesDirectory)/rocrtst/suites/test_common
       script: |
-        sudo rm -rf $(Agent.BuildDirectory)/external/llvm-project
-        mkdir -p $(Agent.BuildDirectory)/external/llvm-project/clang/lib
-        sudo ln -s $(Agent.BuildDirectory)/rocm/llvm/lib/clang/20/include $(Agent.BuildDirectory)/external/llvm-project/clang/lib/Headers
         mkdir build && cd build
         cmake .. \
           -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm \
           -DTARGET_DEVICES=$(JOB_GPU_TARGET) \
           -DROCM_DIR=$(Agent.BuildDirectory)/rocm \
           -DLLVM_DIR=$(Agent.BuildDirectory)/rocm/llvm/bin \
-          -DOPENCL_DIR=$(Agent.BuildDirectory)/rocm/llvm/bin
+          -DOPENCL_INC_DIR=$(Agent.BuildDirectory)/rocm/llvm/lib/clang/21/include
         make
         make rocrtst_kernels
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml

.azuredevops/components/hipBLASLt.yml

@@ -59,14 +59,10 @@ jobs:
     value: $(Build.BinariesDirectory)/rocm
   - name: TENSILE_ROCM_ASSEMBLER_PATH
     value: $(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
-  - name: CMAKE_CXX_COMPILER
-    value: $(Agent.BuildDirectory)/rocm/bin/hipcc
   - name: TENSILE_ROCM_OFFLOAD_BUNDLER_PATH
     value: $(Agent.BuildDirectory)/rocm/llvm/bin/clang-offload-bundler
-  - name: TENSILE_ROCM_PATH
-    value: $(Agent.BuildDirectory)/rocm/bin/hipcc
-  - name: PATH
-    value: $(Agent.BuildDirectory)/rocm/llvm/bin:$(Agent.BuildDirectory)/rocm/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin
+  - name: ROCM_PATH
+    value: $(Agent.BuildDirectory)/rocm
   - name: DAY_STRING
     value: $[format('{0:ddMMyyyy}', pipeline.startTime)]
   pool: ${{ variables.ULTRA_BUILD_POOL }}
@@ -154,9 +150,8 @@ jobs:
       extraEnvVars:
         - HIP_ROCCLR_HOME:::/home/user/workspace/rocm
         - TENSILE_ROCM_ASSEMBLER_PATH:::/home/user/workspace/rocm/llvm/bin/amdclang
-        - CMAKE_CXX_COMPILER:::/home/user/workspace/rocm/bin/hipcc
         - TENSILE_ROCM_OFFLOAD_BUNDLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang-offload-bundler
-        - TENSILE_ROCM_PATH:::/home/user/workspace/rocm/bin/hipcc
+        - ROCM_PATH:::/home/user/workspace/rocm
       extraCopyDirectories:
         - deps
 

.azuredevops/components/rccl.yml

@@ -51,6 +51,7 @@ parameters:
 
 jobs:
 - job: rccl
+  timeoutInMinutes: 90
   variables:
   - group: common
   - template: /.azuredevops/variables-global.yml
@@ -78,7 +79,6 @@ jobs:
       checkoutRef: ${{ parameters.checkoutRef }}
       dependencyList: ${{ parameters.rocmDependencies }}
       gpuTarget: $(JOB_GPU_TARGET)
-  - script: chmod +x $(Agent.BuildDirectory)/rocm/bin/hipify-perl
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
     parameters:
       extraBuildFlags: >-
@@ -88,7 +88,7 @@ jobs:
         -DCMAKE_BUILD_TYPE=Release
         -DROCM_PATH=$(Agent.BuildDirectory)/rocm
         -DBUILD_TESTS=ON
-        -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/rocm/share/rocm/cmake/
+        -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/rocm/share/rocm/cmake;$(Agent.BuildDirectory)/rocm/libexec/hipify
         -DAMDGPU_TARGETS=$(JOB_GPU_TARGET)
         -GNinja
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml

.azuredevops/components/rocBLAS.yml

@@ -64,10 +64,10 @@ jobs:
     value: $(Build.BinariesDirectory)/rocm
   - name: TENSILE_ROCM_ASSEMBLER_PATH
     value: $(Agent.BuildDirectory)/rocm/llvm/bin/clang
-  - name: CMAKE_CXX_COMPILER
-    value: $(Agent.BuildDirectory)/rocm/bin/hipcc
   - name: TENSILE_ROCM_OFFLOAD_BUNDLER_PATH
     value: $(Agent.BuildDirectory)/rocm/llvm/bin/clang-offload-bundler
+  - name: ROCM_PATH
+    value: $(Agent.BuildDirectory)/rocm
   pool: ${{ variables.MEDIUM_BUILD_POOL }}
   workspace:
     clean: all
@@ -96,8 +96,8 @@ jobs:
         -DCMAKE_TOOLCHAIN_FILE=toolchain-linux.cmake
         -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm
         -DCMAKE_BUILD_TYPE=Release
-        -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/bin/hipcc
-        -DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/hipcc
+        -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang++
+        -DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang
         -DGPU_TARGETS=$(JOB_GPU_TARGET)
         -DTensile_CODE_OBJECT_VERSION=default
         -DTensile_LOGIC=asm_full
@@ -125,8 +125,8 @@ jobs:
       extraEnvVars:
         - HIP_ROCCLR_HOME:::/home/user/workspace/rocm
         - TENSILE_ROCM_ASSEMBLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang
-        - CMAKE_CXX_COMPILER:::/home/user/workspace/rocm/bin/hipcc
         - TENSILE_ROCM_OFFLOAD_BUNDLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang-offload-bundler
+        - ROCM_PATH:::/home/user/workspace/rocm
 
 - job: rocBLAS_testing
   dependsOn: rocBLAS

.azuredevops/components/rocDecode.yml

@@ -49,21 +49,10 @@ jobs:
   workspace:
     clean: all
   steps:
-  # Since mesa-amdgpu-multimedia-devel is not directly available from apt, register it
-  - task: Bash@3
-    displayName: 'Register ROCm packages'
-    inputs:
-      targetType: inline
-      script: |
-        sudo mkdir --parents --mode=0755 /etc/apt/keyrings
-        wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/amdgpu/${{ variables.KEYRING_VERSION }}/ubuntu jammy main" | sudo tee /etc/apt/sources.list.d/amdgpu.list
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/${{ variables.KEYRING_VERSION }} jammy main" | sudo tee --append /etc/apt/sources.list.d/rocm.list
-        echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
-        sudo apt update
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
+      registerROCmPackages: true
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
     parameters:
@@ -104,21 +93,10 @@ jobs:
         JOB_GPU_TARGET: gfx942
         JOB_TEST_POOL: ${{ variables.GFX942_TEST_POOL }}
   steps:
-  # Since mesa-amdgpu-multimedia-devel is not directly available from apt, register it
-  - task: Bash@3
-    displayName: 'Register ROCm packages'
-    inputs:
-      targetType: inline
-      script: |
-        sudo mkdir --parents --mode=0755 /etc/apt/keyrings
-        wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/amdgpu/${{ variables.KEYRING_VERSION }}/ubuntu jammy main" | sudo tee /etc/apt/sources.list.d/amdgpu.list
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/${{ variables.KEYRING_VERSION }} jammy main" | sudo tee --append /etc/apt/sources.list.d/rocm.list
-        echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
-        sudo apt update
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
+      registerROCmPackages: true
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml

.azuredevops/components/rocJPEG.yml

@@ -48,21 +48,10 @@ jobs:
       gfx942:
         JOB_GPU_TARGET: gfx942
   steps:
-  # Since mesa-amdgpu-multimedia-devel is not directly available from apt, register it
-  - task: Bash@3
-    displayName: 'Register ROCm packages'
-    inputs:
-      targetType: inline
-      script: |
-        sudo mkdir --parents --mode=0755 /etc/apt/keyrings
-        wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/amdgpu/${{ variables.KEYRING_VERSION }}/ubuntu jammy main" | sudo tee /etc/apt/sources.list.d/amdgpu.list
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/${{ variables.KEYRING_VERSION }} jammy main" | sudo tee --append /etc/apt/sources.list.d/rocm.list
-        echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
-        sudo apt update
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
+      registerROCmPackages: true
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
     parameters:
@@ -106,21 +95,10 @@ jobs:
         JOB_GPU_TARGET: gfx942
         JOB_TEST_POOL: ${{ variables.GFX942_TEST_POOL }}
   steps:
-  # Since mesa-amdgpu-multimedia-devel is not directly available from apt, register it
-  - task: Bash@3
-    displayName: 'Register ROCm packages'
-    inputs:
-      targetType: inline
-      script: |
-        sudo mkdir --parents --mode=0755 /etc/apt/keyrings
-        wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/amdgpu/${{ variables.KEYRING_VERSION }}/ubuntu jammy main" | sudo tee /etc/apt/sources.list.d/amdgpu.list
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/${{ variables.KEYRING_VERSION }} jammy main" | sudo tee --append /etc/apt/sources.list.d/rocm.list
-        echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
-        sudo apt update
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
+      registerROCmPackages: true
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml

.azuredevops/components/rocPyDecode.yml

@@ -84,6 +84,7 @@ jobs:
         echo "##vso[task.setvariable variable=PYBIND11_PATH;]$(python3 -c 'import pybind11; print(pybind11.get_cmake_dir())')"
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
     parameters:
+      installEnabled: false
       extraBuildFlags: >-
         -DROCM_PATH=$(Agent.BuildDirectory)/rocm
         -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(PYTHON_USER_SITE)/pybind11;$(PYTHON_DIST_PACKAGES)/pybind11;$(PYBIND11_PATH)
@@ -91,6 +92,14 @@ jobs:
         -DGPU_TARGETS=$(JOB_GPU_TARGET)
         -DCMAKE_INSTALL_PREFIX_PYTHON=$(Build.BinariesDirectory)
         -GNinja
+  - task: Bash@3
+    displayName: 'rocPyDecode install'
+    inputs:
+      targetType: inline
+      script: |
+        sudo cmake --build . --target install
+        sudo chown -R $(whoami):$(id -gn) $(Build.BinariesDirectory)
+      workingDirectory: $(Build.SourcesDirectory)/build
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
     parameters:
       gpuTarget: $(JOB_GPU_TARGET)
@@ -105,7 +114,8 @@ jobs:
       script: |
         export ROCM_PATH=$(Agent.BuildDirectory)/rocm
         export HIP_INCLUDE_DIRS=$(Agent.BuildDirectory)/rocm/include/hip
-        python3 setup.py bdist_wheel
+        sudo python3 setup.py bdist_wheel
+        sudo chown -R $(whoami):$(id -gn) $(find . -name "*.whl")
       workingDirectory: $(Build.SourcesDirectory)
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-prepare-package.yml
     parameters:

.azuredevops/components/rocWMMA.yml

@@ -80,6 +80,7 @@ jobs:
         -DROCWMMA_BUILD_SAMPLES=OFF
         -DGPU_TARGETS=$(JOB_GPU_TARGET)
         -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON
+        -DROCM_PLATFORM_VERSION=$(NEXT_RELEASE_VERSION)
         -GNinja
 # gfx1030 not supported in documentation
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml

.azuredevops/components/rocm-core.yml

@@ -36,7 +36,7 @@ jobs:
         -DCPACK_GENERATOR=DEB
         -DCPACK_DEBIAN_PACKAGE_RELEASE="local.9999~99.99"
         -DCPACK_RPM_PACKAGE_RELEASE="local.9999"
-        -DROCM_VERSION="$(next-release)"
+        -DROCM_VERSION="$(NEXT_RELEASE_VERSION)"
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml

.azuredevops/components/rocminfo.yml

@@ -35,7 +35,7 @@ jobs:
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
-      registerRadeon: true
+      registerROCmPackages: true
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
     parameters:
@@ -56,6 +56,7 @@ jobs:
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
+      registerROCmPackages: true
 
 - job: rocminfo_testing
   dependsOn: rocminfo
@@ -102,5 +103,6 @@ jobs:
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
+      registerROCmPackages: true
       environment: test
       gpuTarget: $(JOB_GPU_TARGET)

.azuredevops/components/rocprofiler-sdk.yml

@@ -37,6 +37,8 @@ parameters:
     - clr
     - llvm-project
     - rccl
+    - rocDecode
+    - rocJPEG
     - rocm-cmake
     - rocm-core
     - rocminfo
@@ -60,7 +62,7 @@ jobs:
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
       pipModules: ${{ parameters.pipModules }}
-      registerRadeon: true
+      registerROCmPackages: true
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
     parameters:
@@ -99,6 +101,7 @@ jobs:
       aptPackages: ${{ parameters.aptPackages }}
       pipModules: ${{ parameters.pipModules }}
       gpuTarget: $(JOB_GPU_TARGET)
+      registerROCmPackages: true
 
 - job: rocprofiler_sdk_testing
   dependsOn: rocprofiler_sdk
@@ -119,7 +122,7 @@ jobs:
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
       pipModules: ${{ parameters.pipModules }}
-      registerRadeon: true
+      registerROCmPackages: true
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
     parameters:
@@ -155,3 +158,4 @@ jobs:
       pipModules: ${{ parameters.pipModules }}
       environment: test
       gpuTarget: $(JOB_GPU_TARGET)
+      registerROCmPackages: true

.azuredevops/components/rocprofiler-systems.yml

@@ -46,17 +46,19 @@ parameters:
 - name: rocmDependencies
   type: object
   default:
+    - amdsmi
     - aomp
     - clr
     - llvm-project
     - rccl
     - rocDecode
+    - rocJPEG
     - rocm-core
-    - rocm_smi_lib
     - rocminfo
-    - ROCR-Runtime
+    - rocm_smi_lib
     - rocprofiler-register
     - rocprofiler-sdk
+    - ROCR-Runtime
 
 jobs:
 - job: rocprofiler_systems
@@ -75,7 +77,7 @@ jobs:
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
       pipModules: ${{ parameters.pipModules }}
-      registerRadeon: true
+      registerROCmPackages: true
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
     parameters:
@@ -159,7 +161,7 @@ jobs:
     parameters:
       aptPackages: ${{ parameters.aptPackages }}
       pipModules: ${{ parameters.pipModules }}
-      registerRadeon: true
+      registerROCmPackages: true
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
     parameters:

.azuredevops/components/rpp.yml

@@ -29,6 +29,7 @@ parameters:
     - clr
     - half
     - llvm-project
+    - rocm-cmake
     - rocminfo
     - ROCR-Runtime
 - name: rocmTestDependencies
@@ -79,6 +80,7 @@ jobs:
         -DHALF_INCLUDE_DIRS=$(Agent.BuildDirectory)/rocm/include
         -DCMAKE_BUILD_TYPE=Release
         -DGPU_TARGETS=$(JOB_GPU_TARGET)
+        -DROCM_PLATFORM_VERSION=$(NEXT_RELEASE_VERSION)
         -GNinja
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
     parameters:

.azuredevops/dependencies/half560.yml

@@ -19,7 +19,7 @@ jobs:
   pool:
     vmImage: ${{ variables.BASE_BUILD_POOL }}
   container:
-    image: ${{ variables.DOCKER_IMAGE_NAME }}:${{ variables.LATEST_DOCKER_VERSION }}
+    image: rocm/dev-ubuntu-22.04:${{ variables.LATEST_RELEASE_VERSION }}
   workspace:
     clean: all
   steps:

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

@@ -4,13 +4,13 @@ steps:
   inputs:
     targetType: inline
     script: |
-      export packageName=$(curl -s https://repo.radeon.com/rocm/apt/latest/pool/main/h/hsa-amd-aqlprofile/ | grep -oP "href=\"\K[^\"]*$(lsb_release -rs)[^\"]*\.deb")
+      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"
 - task: Bash@3
   displayName: 'Download aqlprofile'
   inputs:
     targetType: inline
-    script: wget -nv https://repo.radeon.com/rocm/apt/latest/pool/main/h/hsa-amd-aqlprofile/$(packageName)
+    script: wget -nv https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION)/pool/main/h/hsa-amd-aqlprofile/$(packageName)
     workingDirectory: '$(Pipeline.Workspace)'
 - task: Bash@3
   displayName: 'Extract aqlprofile'

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

@@ -6,21 +6,21 @@ parameters:
 - name: pipModules
   type: object
   default: []
-- name: registerRadeon
+- name: registerROCmPackages
   type: boolean
   default: false
 
 steps:
-- ${{ if eq(parameters.registerRadeon, true) }}:
+- ${{ if eq(parameters.registerROCmPackages, true) }}:
   - task: Bash@3
-    displayName: 'Register repo.radeon packages'
+    displayName: 'Register AMDGPU & ROCm repos'
     inputs:
       targetType: inline
       script: |
         sudo mkdir --parents --mode=0755 /etc/apt/keyrings
         wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/amdgpu/latest/ubuntu jammy main" | sudo tee /etc/apt/sources.list.d/amdgpu.list
-        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/latest jammy main" | sudo tee --append /etc/apt/sources.list.d/rocm.list
+        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/amdgpu/$(REPO_RADEON_VERSION)/ubuntu jammy main" | sudo tee /etc/apt/sources.list.d/amdgpu.list
+        echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION) jammy main" | sudo tee --append /etc/apt/sources.list.d/rocm.list
         echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
         sudo apt update
 # firefox takes time to upgrade and is not needed for CI workloads, hold version

.azuredevops/templates/steps/docker-container.yml

@@ -154,8 +154,8 @@ steps:
         script: |
           echo "RUN mkdir --parents --mode=0755 /etc/apt/keyrings" >> Dockerfile
           echo "RUN wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | tee /etc/apt/keyrings/rocm.gpg > /dev/null" >> Dockerfile
-          echo "RUN echo \"deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/amdgpu/latest/ubuntu jammy main\" | tee /etc/apt/sources.list.d/amdgpu.list" >> Dockerfile
-          echo "RUN echo \"deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/latest jammy main\" | tee --append /etc/apt/sources.list.d/rocm.list" >> Dockerfile
+          echo "RUN echo \"deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/amdgpu/$(REPO_RADEON_VERSION)/ubuntu jammy main\" | tee /etc/apt/sources.list.d/amdgpu.list" >> Dockerfile
+          echo "RUN echo \"deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION) jammy main\" | tee --append /etc/apt/sources.list.d/rocm.list" >> Dockerfile
           echo "RUN printf 'Package: *\\nPin: release o=repo.radeon.com\\nPin-Priority: 600' > /etc/apt/preferences.d/rocm-pin-600" >> Dockerfile
           echo "RUN DEBIAN_FRONTEND=noninteractive apt-get --yes update" >> Dockerfile
   - ${{ if eq(parameters.registerCUDAPackages, true) }}:

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

@@ -20,41 +20,37 @@ steps:
       ARTIFACT_NAME="composablekernel.${{ parameters.gpuTarget }}"
       EXIT_CODE=0
 
-      # The commits that MIOpen reference are all merge commits from CK/develop to CK/amd-develop
-      # These commits are present on CK/amd-develop but not on CK/develop
-      # Ex-CI only builds CK/develop, so we need to find a commit present on both CK/develop and CK/amd-develop
-
+      # Try to find an Azure build for the specific CK commit called out in MIOpen's requirements.txt
       CK_COMMIT=$(grep 'ROCm/composable_kernel' requirements.txt | sed -E 's/.*@([a-f0-9]{40}).*/\1/')
       echo "Fetching CK build ID for commit $CK_COMMIT"
-      CK_COMMIT_URL="$GH_API/composable_kernel/commits/${CK_COMMIT}"
-      PARENT_COMMIT=$(curl -s $CK_COMMIT_URL | jq '.parents[1].sha' | tr -d '"')
-      echo "Found parent commit: $PARENT_COMMIT"
-      PARENT_CHECKS_URL="$GH_API/composable_kernel/commits/${PARENT_COMMIT}/check-runs"
-      CK_BUILD_ID=$(curl -s $PARENT_CHECKS_URL | \
+      CK_CHECKS_URL="$GH_API/composable_kernel/commits/${CK_COMMIT}/check-runs"
+      CK_BUILD_ID=$(curl -s $CK_CHECKS_URL | \
         jq '.check_runs[] | select(.name == "composable_kernel" and .app.slug == "azure-pipelines") | .details_url' | \
         tr -d '"' | grep -oP 'buildId=\K\d+')
 
-      if [ -z "$CK_BUILD_ID" ]; then
+      # If none found, use latest successful CK build instead
+      if [[ -z "$CK_BUILD_ID" ]]; then
         echo "Did not find specific CK build ID"
         LATEST_BUILD_URL="$AZ_API/build/builds?definitions=$(COMPOSABLE_KERNEL_PIPELINE_ID)&statusFilter=completed&resultFilter=succeeded&\$top=1&api-version=7.1"
         CK_BUILD_ID=$(curl -s $LATEST_BUILD_URL | jq '.value[0].id')
         echo "Found latest CK build ID: $CK_BUILD_ID"
         EXIT_CODE=1
+      else
+        echo "Found specific CK build ID: $CK_BUILD_ID"
       fi
 
       AZURE_URL="$AZ_API/build/builds/$CK_BUILD_ID/artifacts?artifactName=$ARTIFACT_NAME&api-version=7.1"
       ARTIFACT_URL=$(curl -s $AZURE_URL | jq '.resource.downloadUrl' | tr -d '"')
 
-      if [ -z "$ARTIFACT_URL" ]; then
-        echo "Did not find specific CK build artifact"
-        LATEST_BUILD_URL="$AZ_API/build/builds?definitions=$(COMPOSABLE_KERNEL_PIPELINE_ID)&status=completed&result=succeeded&\$top=1&api-version=7.1"
+      # If using the specific CK commit and it doesn't have any valid artifacts, use latest successful CK build instead
+      if { [[ -z "$ARTIFACT_URL" ]] || [[ "$ARTIFACT_URL" == "null" ]]; } && [[ $EXIT_CODE -eq 0 ]]; then
+        echo "Did not find valid specific CK build artifact"
+        LATEST_BUILD_URL="$AZ_API/build/builds?definitions=$(COMPOSABLE_KERNEL_PIPELINE_ID)&statusFilter=completed&resultFilter=succeeded&\$top=1&api-version=7.1"
         CK_BUILD_ID=$(curl -s $LATEST_BUILD_URL | jq '.value[0].id')
         echo "Found latest CK build ID: $CK_BUILD_ID"
         AZURE_URL="$AZ_API/build/builds/$CK_BUILD_ID/artifacts?artifactName=$ARTIFACT_NAME&api-version=7.1"
         ARTIFACT_URL=$(curl -s $AZURE_URL | jq '.resource.downloadUrl' | tr -d '"')
         EXIT_CODE=2
-      elif [ $EXIT_CODE -eq 0 ]; then
-        echo "Found specific CK build ID: $CK_BUILD_ID"
       fi
 
       echo "Downloading CK artifact from $ARTIFACT_URL"
@@ -64,9 +60,13 @@ steps:
       tar -zxvf $(System.ArtifactsDirectory)/$ARTIFACT_NAME/*.tar.gz -C $(Agent.BuildDirectory)/rocm
       rm -r $(System.ArtifactsDirectory)/ck.zip $(System.ArtifactsDirectory)/$ARTIFACT_NAME
 
-      if [ $EXIT_CODE -ne 0 ]; then
+      if [[ $EXIT_CODE -ne 0 ]]; then
         BUILD_COMMIT=$(curl -s $AZ_API/build/builds/$CK_BUILD_ID | jq '.sourceVersion' | tr -d '"')
-        echo "WARNING: couldn't find a CK build for commit $CK_COMMIT"
+        if [[ $EXIT_CODE -eq 1 ]]; then
+          echo "WARNING: couldn't find a CK build for commit $CK_COMMIT"
+        elif [[ $EXIT_CODE -eq 2 ]]; then
+          echo "WARNING: couldn't find a valid CK artifact for commit $CK_COMMIT"
+        fi
         echo "Instead used latest CK build $CK_BUILD_ID for commit $BUILD_COMMIT"
       fi
       exit $EXIT_CODE

.azuredevops/variables-global.yml

@@ -27,14 +27,14 @@ variables:
   value: rocm-ci_larger_base_disk_pool
 - name: GFX942_TEST_POOL
   value: gfx942_test_pool
+- name: LATEST_RELEASE_VERSION
+  value: 6.3.2
+- name: REPO_RADEON_VERSION
+  value: 6.3.2
+- name: NEXT_RELEASE_VERSION
+  value: 6.4.0
 - name: LATEST_RELEASE_TAG
-  value: rocm-6.1.0
-- name: DOCKER_IMAGE_NAME
-  value: rocm/dev-ubuntu-22.04
-- name: LATEST_DOCKER_VERSION
-  value: 6.1
-- name: KEYRING_VERSION
-  value: 6.3
+  value: rocm-6.3.2
 - name: AMDMIGRAPHX_GFX942_TEST_PIPELINE_ID
   value: 197
 - name: AMDMIGRAPHX_PIPELINE_ID
@@ -151,8 +151,6 @@ variables:
   value: 105
 - name: HIPTENSOR_TAGGED_PIPELINE_ID
   value: 56
-- name: LAST_RELEASE
-  value: 6.1.0
 - name: LLVM_PROJECT_PIPELINE_ID
   value: 2
 - name: LLVM_PROJECT_TAGGED_PIPELINE_ID
@@ -183,10 +181,6 @@ variables:
   value: 100
 - name: RDC_TAGGED_PIPELINE_ID
   value: 59
-- name: REIMAGE_ORG
-  value: AGS-ROCm-CI
-- name: REIMAGE_REPO
-  value: cirrascale-reimage-automation
 - name: ROCAL_PIPELINE_ID
   value: 151
 - name: ROCALUTION_GFX942_TEST_PIPELINE_ID

.github/workflows/issue_retrieval.yml

@@ -2,7 +2,7 @@ name: Issue retrieval
 
 on:
   issues:
-    types: [opened]
+    types: [opened, edited]
 
 jobs:
   auto-retrieve:
@@ -15,7 +15,7 @@ jobs:
           app_id: ${{ secrets.ACTION_APP_ID }}
           private_key: ${{ secrets.ACTION_PEM }}
       - name: 'Retrieve Issue'
-        uses: abhimeda/rocm_issue_management@main
+        uses: harkgill-amd/rocm_issue_management@main
         with:
           authentication-token: ${{ steps.generate_token.outputs.token }}
           github-organization: 'ROCm'

.wordlist.txt

@@ -74,6 +74,7 @@ Conda
 ConnectX
 CuPy
 Dashboarding
+DBRX
 DDR
 DF
 DGEMM
@@ -92,6 +93,7 @@ DataFrame
 DataLoader
 DataParallel
 Debian
+DeepSeek
 DeepSpeed
 Dependabot
 Deprecations
@@ -115,6 +117,7 @@ FX
 Filesystem
 FindDb
 Flang
+FluxBenchmark
 Fortran
 Fuyu
 GALB
@@ -129,6 +132,8 @@ GDS
 GEMM
 GEMMs
 GFortran
+GFXIP
+Gemma
 GiB
 GIM
 GL
@@ -314,6 +319,7 @@ PipelineParallel
 PnP
 PowerEdge
 PowerShell
+Pretraining
 Profiler's
 PyPi
 Pytest
@@ -335,6 +341,7 @@ RNNs
 ROC
 ROCProfiler
 ROCT
+ROCTx
 ROCTracer
 ROCclr
 ROCdbgapi
@@ -712,6 +719,7 @@ preprocessing
 preprocessor
 prequantized
 prerequisites
+pretraining
 profiler
 profilers
 protobuf
@@ -764,6 +772,7 @@ rocm
 rocminfo
 rocprim
 rocprof
+rocprofv
 rocprofiler
 rocr
 rocrand

README.md

@@ -50,7 +50,7 @@ The following example shows how to use the repo tool to download the ROCm source
 ```bash
 mkdir -p ~/ROCm/
 cd ~/ROCm/
-export ROCM_VERSION=6.3.2
+export ROCM_VERSION=6.3.3
 ~/bin/repo init -u http://github.com/ROCm/ROCm.git -b roc-6.3.x -m tools/rocm-build/rocm-${ROCM_VERSION}.xml
 ~/bin/repo sync
 ```
@@ -77,8 +77,8 @@ The Build time will reduce significantly if we limit the GPU Architecture/s agai
 
 mkdir -p ~/WORKSPACE/      # Or any folder name other than WORKSPACE
 cd ~/WORKSPACE/
-export ROCM_VERSION=6.3.2
-~/bin/repo init -u http://github.com/ROCm/ROCm.git -b develop -m tools/rocm-build/rocm-${ROCM_VERSION}.xml
+export ROCM_VERSION=6.3.3
+~/bin/repo init -u http://github.com/ROCm/ROCm.git -b roc-6.3.x -m tools/rocm-build/rocm-${ROCM_VERSION}.xml
 ~/bin/repo sync
 
 # --------------------------------------

RELEASE.md

@@ -10,7 +10,7 @@
 <!-- markdownlint-disable reference-links-images            -->
 <!-- markdownlint-disable no-missing-space-atx              -->
 <!-- spellcheck-disable                                     -->
-# ROCm 6.3.2 release notes
+# ROCm 6.3.3 release notes
 
 The release notes provide a summary of notable changes since the previous ROCm release.
 
@@ -24,46 +24,51 @@ The release notes provide a summary of notable changes since the previous ROCm r
 
 - [ROCm known issues](#rocm-known-issues)
 
-- [ROCm resolved issues](#rocm-resolved-issues)
-
 - [ROCm upcoming changes](#rocm-upcoming-changes)
 
 ```{note}
-If you’re using Radeon™ PRO or Radeon GPUs in a workstation setting with a
-display connected, continue to use ROCm 6.2.3. See the [Use ROCm on Radeon GPUs](https://rocm.docs.amd.com/projects/radeon/en/latest/index.html)
+If you’re using Radeon™ PRO or Radeon GPUs in a workstation setting with a display connected, see the [Use ROCm on Radeon GPUs](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/compatibility/native_linux/native_linux_compatibility.html)
 documentation to verify compatibility and system requirements.
 ```
 ## Release highlights
 
-The following are notable improvements in ROCm 6.3.2. For changes to individual components, see
+The following are notable new features and improvements in ROCm 6.3.3. For changes to individual components, see
 [Detailed component changes](#detailed-component-changes).
 
+### ROCm Offline Installer Creator updates
+
+The ROCm Offline Installer Creator 6.3.3 adds a new Post-Install Options menu, which includes a new ``udev`` option for adding GPU resources access for all users. It also moves the user-specific GPU access option (for the ``video,render`` group) from the Driver Options menu to the Post-Install Options menu. See the [ROCm Offline Installer Creator](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/rocm-offline-installer.html#post-install-options-menu) documentation for more information.
+
 ### ROCm documentation updates
 
 ROCm documentation continues to be updated to provide clearer and more comprehensive guidance for a wider variety of user needs and use cases.
 
-* Documentation about ROCm compatibility with deep learning frameworks has been added. These topics outline ROCm-enabled features for each deep learning framework, key ROCm libraries that can influence the capabilities, validated Docker image tags, and features supported across the available ROCm and framework versions. For more information, see:
+* [Tutorials for AI developers](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/) have been added. These tutorials are Jupyter notebook-based, easy-to-follow documents. They are ideal for AI developers who want to learn about specific topics, including inference, fine-tuning, and training.
 
-    * [PyTorch compatibility](https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html)
+* The [LLM inference performance validation guide for AMD Instinct MI300X](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/vllm-benchmark.html)
+  now includes additional models for performance benchmarking. The accompanying ROCm vLLM Docker has been upgraded to ROCm 6.3.1.
+ 
+* The HIP documentation has been updated with new resources for developers. To learn more about concurrency, parallelism, and stream management on devices and multiple GPUs, see [Asynchronous concurrent execution](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_runtime_api/asynchronous.html)
 
-    * [TensorFlow compatibility](https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/tensorflow-compatibility.html)
-
-    * [JAX compatibility](https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/jax-compatibility.html)
-
-* The [HIP C++ language extensions](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_cpp_language_extensions.html) and [Kernel language C++ support](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/kernel_language_cpp_support.html) topics have been reorganized to make them easier to find and review. The topics have also been enhanced with new content.
+* The following HIP documentation topics have been updated:
+    - [Virtual memory management](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_runtime_api/memory_management/virtual_memory.html)
+    - [Programming for HIP runtime compiler (RTC)](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_rtc.html)
+    - [HIP porting guide](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_porting_guide.html)
+    - [Porting CUDA driver API](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_porting_driver_api.html)
+    - [CUDA to HIP API function comparison](https://rocm.docs.amd.com/projects/HIP/en/latest/reference/api_syntax.html)
 
 ## Operating system and hardware support changes
 
-ROCm 6.3.2 adds support for Azure Linux 3.0 (kernel: 6.6). Azure Linux is supported only on AMD Instinct accelerators. For more information, see [Azure Linux installation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html).
+Operating system and hardware support remain unchanged in this release.
 
 See the [Compatibility
-matrix](https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html)
+matrix](https://rocm.docs.amd.com/en/docs-6.3.3/compatibility/compatibility-matrix.html)
 for more information about operating system and hardware compatibility.
 
 ## ROCm components
 
-The following table lists the versions of ROCm components for ROCm 6.3.2, including any version
-changes from 6.3.1 to 6.3.2. Click the component's updated version to go to a list of its changes.
+The following table lists the versions of ROCm components for ROCm 6.3.3, including any version
+changes from 6.3.2 to 6.3.3. Click the component's updated version to go to a list of its changes.
 Click {fab}`github` to go to the component's source code on GitHub.
 
 <div class="pst-scrollable-table-container">
@@ -85,47 +90,47 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="9">Libraries</th>
                 <th rowspan="9">Machine learning and computer vision</th>
-                <td><a href="https://rocm.docs.amd.com/projects/composable_kernel/en/docs-6.3.2/index.html">Composable Kernel</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/composable_kernel/en/docs-6.3.3/index.html">Composable Kernel</a></td>
                 <td>1.1.0</td>
                 <td><a href="https://github.com/ROCm/composable_kernel"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/AMDMIGraphX/en/docs-6.3.2/index.html">MIGraphX</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/AMDMIGraphX/en/docs-6.3.3/index.html">MIGraphX</a></td>
                 <td>2.11.0</td>
                 <td><a href="https://github.com/ROCm/AMDMIGraphX"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/MIOpen/en/docs-6.3.2/index.html">MIOpen</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/MIOpen/en/docs-6.3.3/index.html">MIOpen</a></td>
                 <td>3.3.0</td>
                 <td><a href="https://github.com/ROCm/MIOpen"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/MIVisionX/en/docs-6.3.2/index.html">MIVisionX</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/MIVisionX/en/docs-6.3.3/index.html">MIVisionX</a></td>
                 <td>3.1.0</td>
                 <td><a href="https://github.com/ROCm/MIVisionX"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocAL/en/docs-6.3.2/index.html">rocAL</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocAL/en/docs-6.3.3/index.html">rocAL</a></td>
                 <td>2.1.0</td>
                 <td><a href="https://github.com/ROCm/rocAL"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocDecode/en/docs-6.3.2/index.html">rocDecode</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocDecode/en/docs-6.3.3/index.html">rocDecode</a></td>
                 <td>0.8.0</td>
                 <td><a href="https://github.com/ROCm/rocDecode"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocJPEG/en/docs-6.3.2/index.html">rocJPEG</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocJPEG/en/docs-6.3.3/index.html">rocJPEG</a></td>
                 <td>0.6.0</td>
                 <td><a href="https://github.com/ROCm/rocJPEG"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocPyDecode/en/docs-6.3.2/index.html">rocPyDecode</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocPyDecode/en/docs-6.3.3/index.html">rocPyDecode</a></td>
                 <td>0.2.0</td>
                 <td><a href="https://github.com/ROCm/rocPyDecode"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rpp/en/docs-6.3.2/index.html">RPP</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rpp/en/docs-6.3.3/index.html">RPP</a></td>
                 <td>1.9.1</td>
                 <td><a href="https://github.com/ROCm/rpp"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
@@ -134,7 +139,7 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="1"></th>
                 <th rowspan="1">Communication</th>
-                <td><a href="https://rocm.docs.amd.com/projects/rccl/en/docs-6.3.2/index.html">RCCL</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rccl/en/docs-6.3.3/index.html">RCCL</a></td>
                 <td>2.21.5</td>
                 <td><a href="https://github.com/ROCm/rccl"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
@@ -143,82 +148,82 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="16"></th>
                 <th rowspan="16">Math</th>
-                <td><a href="https://rocm.docs.amd.com/projects/hipBLAS/en/docs-6.3.2/index.html">hipBLAS</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipBLAS/en/docs-6.3.3/index.html">hipBLAS</a></td>
                 <td>2.3.0</td>
                 <td><a href="https://github.com/ROCm/hipBLAS"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/hipBLASLt/en/docs-6.3.2/index.html">hipBLASLt</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipBLASLt/en/docs-6.3.3/index.html">hipBLASLt</a></td>
                 <td>0.10.0</td>
                 <td><a href="https://github.com/ROCm/hipBLASLt"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/hipFFT/en/docs-6.3.2/index.html">hipFFT</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipFFT/en/docs-6.3.3/index.html">hipFFT</a></td>
                 <td>1.0.17</td>
                 <td><a href="https://github.com/ROCm/hipFFT"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/hipfort/en/docs-6.3.2/index.html">hipfort</a></td>
-                <td>0.5.0&nbsp;&Rightarrow;&nbsp;<a href="#hipfort-0-5-1">0.5.1</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipfort/en/docs-6.3.3/index.html">hipfort</a></td>
+                <td>0.5.1</td>
                 <td><a href="https://github.com/ROCm/hipfort"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/hipRAND/en/docs-6.3.2/index.html">hipRAND</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipRAND/en/docs-6.3.3/index.html">hipRAND</a></td>
                 <td>2.11.1</td>
                 <td><a href="https://github.com/ROCm/hipRAND"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/hipSOLVER/en/docs-6.3.2/index.html">hipSOLVER</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipSOLVER/en/docs-6.3.3/index.html">hipSOLVER</a></td>
                 <td>2.3.0</td>
                 <td><a href="https://github.com/ROCm/hipSOLVER"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/hipSPARSE/en/docs-6.3.2/index.html">hipSPARSE</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipSPARSE/en/docs-6.3.3/index.html">hipSPARSE</a></td>
                 <td>3.1.2</td>
                 <td><a href="https://github.com/ROCm/hipSPARSE"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/hipSPARSELt/en/docs-6.3.2/index.html">hipSPARSELt</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipSPARSELt/en/docs-6.3.3/index.html">hipSPARSELt</a></td>
                 <td>0.2.2</td>
                 <td><a href="https://github.com/ROCm/hipSPARSELt"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocALUTION/en/docs-6.3.2/index.html">rocALUTION</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocALUTION/en/docs-6.3.3/index.html">rocALUTION</a></td>
                 <td>3.2.1</td>
                 <td><a href="https://github.com/ROCm/rocALUTION"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocBLAS/en/docs-6.3.2/index.html">rocBLAS</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocBLAS/en/docs-6.3.3/index.html">rocBLAS</a></td>
                 <td>4.3.0</td>
                 <td><a href="https://github.com/ROCm/rocBLAS"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocFFT/en/docs-6.3.2/index.html">rocFFT</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocFFT/en/docs-6.3.3/index.html">rocFFT</a></td>
                 <td>1.0.31</td>
                 <td><a href="https://github.com/ROCm/rocFFT"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocRAND/en/docs-6.3.2/index.html">rocRAND</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocRAND/en/docs-6.3.3/index.html">rocRAND</a></td>
                 <td>3.2.0</td>
                 <td><a href="https://github.com/ROCm/rocRAND"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocSOLVER/en/docs-6.3.2/index.html">rocSOLVER</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocSOLVER/en/docs-6.3.3/index.html">rocSOLVER</a></td>
                 <td>3.27.0</td>
                 <td><a href="https://github.com/ROCm/rocSOLVER"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocSPARSE/en/docs-6.3.2/index.html">rocSPARSE</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocSPARSE/en/docs-6.3.3/index.html">rocSPARSE</a></td>
                 <td>3.3.0</td>
                 <td><a href="https://github.com/ROCm/rocSPARSE"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocWMMA/en/docs-6.3.2/index.html">rocWMMA</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocWMMA/en/docs-6.3.3/index.html">rocWMMA</a></td>
                 <td>1.6.0</td>
                 <td><a href="https://github.com/ROCm/rocWMMA"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/Tensile/en/docs-6.3.2/src/index.html">Tensile</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/Tensile/en/docs-6.3.3/src/index.html">Tensile</a></td>
                 <td>4.42.0</td>
                 <td><a href="https://github.com/ROCm/Tensile"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
@@ -227,22 +232,22 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="4"></th>
                 <th rowspan="4">Primitives</th>
-                <td><a href="https://rocm.docs.amd.com/projects/hipCUB/en/docs-6.3.2/index.html">hipCUB</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipCUB/en/docs-6.3.3/index.html">hipCUB</a></td>
                 <td>3.3.0</td>
                 <td><a href="https://github.com/ROCm/hipCUB"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/hipTensor/en/docs-6.3.2/index.html">hipTensor</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/hipTensor/en/docs-6.3.3/index.html">hipTensor</a></td>
                 <td>1.4.0</td>
                 <td><a href="https://github.com/ROCm/hipTensor"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocPRIM/en/docs-6.3.2/index.html">rocPRIM</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocPRIM/en/docs-6.3.3/index.html">rocPRIM</a></td>
                 <td>3.3.0</td>
                 <td><a href="https://github.com/ROCm/rocPRIM"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocThrust/en/docs-6.3.2/index.html">rocThrust</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocThrust/en/docs-6.3.3/index.html">rocThrust</a></td>
                 <td>3.3.0</td>
                 <td><a href="https://github.com/ROCm/rocThrust"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
@@ -251,27 +256,27 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="7">Tools</th>
                 <th rowspan="7">System management</th>
-                <td><a href="https://rocm.docs.amd.com/projects/amdsmi/en/docs-6.3.2/index.html">AMD SMI</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/amdsmi/en/docs-6.3.3/index.html">AMD SMI</a></td>
                 <td>24.7.1</td>
                 <td><a href="https://github.com/ROCm/amdsmi"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rdc/en/docs-6.3.2/index.html">ROCm Data Center Tool</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rdc/en/docs-6.3.3/index.html">ROCm Data Center Tool</a></td>
                 <td>0.3.0</td>
                 <td><a href="https://github.com/ROCm/rdc"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocminfo/en/docs-6.3.2/index.html">rocminfo</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocminfo/en/docs-6.3.3/index.html">rocminfo</a></td>
                 <td>1.0.0</td>
                 <td><a href="https://github.com/ROCm/rocminfo"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocm_smi_lib/en/docs-6.3.2/index.html">ROCm SMI</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocm_smi_lib/en/docs-6.3.3/index.html">ROCm SMI</a></td>
                 <td>7.4.0</td>
                 <td><a href="https://github.com/ROCm/rocm_smi_lib"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/ROCmValidationSuite/en/docs-6.3.2/index.html">ROCmValidationSuite</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/ROCmValidationSuite/en/docs-6.3.3/index.html">ROCmValidationSuite</a></td>
                 <td>1.1.0</td>
                 <td><a href="https://github.com/ROCm/ROCmValidationSuite"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
@@ -280,38 +285,38 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="6"></th>
                 <th rowspan="6">Performance</th>
-                <td><a href="https://rocm.docs.amd.com/projects/rocm_bandwidth_test/en/docs-6.3.2/index.html">ROCm Bandwidth
+                <td><a href="https://rocm.docs.amd.com/projects/rocm_bandwidth_test/en/docs-6.3.3/index.html">ROCm Bandwidth
                         Test</a></td>
                 <td>1.4.0</td>
                 <td><a href="https://github.com/ROCm/rocm_bandwidth_test/"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocprofiler-compute/en/docs-6.3.2/index.html">ROCm Compute Profiler</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocprofiler-compute/en/docs-6.3.3/index.html">ROCm Compute Profiler</a></td>
                 <td>3.0.0</td>
                 <td><a href="https://github.com/ROCm/rocprofiler-compute"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocprofiler-systems/en/docs-6.3.2/index.html">ROCm Systems Profiler</a></td>
-                <td>0.1.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-systems-profiler-0-1-1">0.1.1</td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocprofiler-systems/en/docs-6.3.3/index.html">ROCm Systems Profiler</a></td>
+                <td>0.1.1&nbsp;&Rightarrow;&nbsp;<a href="#rocm-systems-profiler-0-1-2">0.1.2</td>
                 <td><a href="https://github.com/ROCm/rocprofiler-systems"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocprofiler/en/docs-6.3.2/index.html">ROCProfiler</a></td>
-                <td>2.0.0&nbsp;&Rightarrow;&nbsp;<a href="#rocprofiler-2-0-0">2.0.0</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocprofiler/en/docs-6.3.3/index.html">ROCProfiler</a></td>
+                <td>2.0.0</td>
                 <td><a href="https://github.com/ROCm/ROCProfiler/"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/docs-6.3.2/index.html">ROCprofiler-SDK</a></td>
-                <td>0.5.0&nbsp;&Rightarrow;&nbsp;<a href="#rocprofiler-sdk-0-5-0">0.5.0</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/docs-6.3.3/index.html">ROCprofiler-SDK</a></td>
+                <td>0.5.0</td>
                 <td><a href="https://github.com/ROCm/rocprofiler-sdk/"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr >
-                <td><a href="https://rocm.docs.amd.com/projects/roctracer/en/docs-6.3.2/index.html">ROCTracer</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/roctracer/en/docs-6.3.3/index.html">ROCTracer</a></td>
                 <td>4.1.0</td>
                 <td><a href="https://github.com/ROCm/ROCTracer/"><i
                             class="fab fa-github fa-lg"></i></a></td>
@@ -321,32 +326,32 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="5"></th>
                 <th rowspan="5">Development</th>
-                <td><a href="https://rocm.docs.amd.com/projects/HIPIFY/en/docs-6.3.2/index.html">HIPIFY</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/HIPIFY/en/docs-6.3.3/index.html">HIPIFY</a></td>
                 <td>18.0.0</td>
                 <td><a href="https://github.com/ROCm/HIPIFY/"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/ROCdbgapi/en/docs-6.3.2/index.html">ROCdbgapi</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/ROCdbgapi/en/docs-6.3.3/index.html">ROCdbgapi</a></td>
                 <td>0.77.0</td>
                 <td><a href="https://github.com/ROCm/ROCdbgapi/"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/ROCmCMakeBuildTools/en/docs-6.3.2/index.html">ROCm CMake</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/ROCmCMakeBuildTools/en/docs-6.3.3/index.html">ROCm CMake</a></td>
                 <td>0.14.0</td>
                 <td><a href="https://github.com/ROCm/rocm-cmake/"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/ROCgdb/en/docs-6.3.2/index.html">ROCm Debugger (ROCgdb)</a>
+                <td><a href="https://rocm.docs.amd.com/projects/ROCgdb/en/docs-6.3.3/index.html">ROCm Debugger (ROCgdb)</a>
                 </td>
                 <td>15.2</td>
                 <td><a href="https://github.com/ROCm/ROCgdb/"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/rocr_debug_agent/en/docs-6.3.2/index.html">ROCr Debug Agent</a>
+                <td><a href="https://rocm.docs.amd.com/projects/rocr_debug_agent/en/docs-6.3.3/index.html">ROCr Debug Agent</a>
                 </td>
                 <td>2.0.3</td>
                 <td><a href="https://github.com/ROCm/rocr_debug_agent/"><i
@@ -356,13 +361,13 @@ Click {fab}`github` to go to the component's source code on GitHub.
         <tbody class="rocm-components-compilers">
             <tr>
                 <th rowspan="2" colspan="2">Compilers</th>
-                <td><a href="https://rocm.docs.amd.com/projects/HIPCC/en/docs-6.3.2/index.html">HIPCC</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/HIPCC/en/docs-6.3.3/index.html">HIPCC</a></td>
                 <td>1.1.1</td>
                 <td><a href="https://github.com/ROCm/llvm-project/"><i
                             class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/llvm-project/en/docs-6.3.2/index.html">llvm-project</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/llvm-project/en/docs-6.3.3/index.html">llvm-project</a></td>
                 <td>18.0.0</td>
                 <td><a href="https://github.com/ROCm/llvm-project/"><i
                             class="fab fa-github fa-lg"></i></a></td>
@@ -371,12 +376,12 @@ Click {fab}`github` to go to the component's source code on GitHub.
         <tbody class="rocm-components-runtimes">
             <tr>
                 <th rowspan="2" colspan="2">Runtimes</th>
-                <td><a href="https://rocm.docs.amd.com/projects/HIP/en/docs-6.3.2/index.html">HIP</a></td>
-                <td>6.3.1&nbsp;&Rightarrow;&nbsp;<a href="#hip-6-3-2">6.3.2</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/HIP/en/docs-6.3.3/index.html">HIP</a></td>
+                <td>6.3.2</td>
                 <td><a href="https://github.com/ROCm/HIP/"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
             <tr>
-                <td><a href="https://rocm.docs.amd.com/projects/ROCR-Runtime/en/docs-6.3.2/index.html">ROCr Runtime</a></td>
+                <td><a href="https://rocm.docs.amd.com/projects/ROCR-Runtime/en/docs-6.3.3/index.html">ROCr Runtime</a></td>
                 <td>1.14.0</td>
                 <td><a href="https://github.com/ROCm/ROCR-Runtime/"><i class="fab fa-github fa-lg"></i></a></td>
             </tr>
@@ -388,112 +393,34 @@ Click {fab}`github` to go to the component's source code on GitHub.
 
 The following sections describe key changes to ROCm components.
 
-### **HIP** (6.3.2)
-
-#### Added
-
-* Tracking of Heterogeneous System Architecture (HSA) handlers:
-    - Adds an atomic counter to track the outstanding HSA handlers.
-    - Waits on CPU for the callbacks if the number exceeds the defined value.
-* Codes to capture Architected Queueing Language (AQL) packets for HIP graph memory copy node between host and device. HIP enqueues AQL packets during graph launch.
-* Control to use system pool implementation in runtime commands handling. By default, it is disabled.
-* A new path to avoid `WaitAny` calls in `AsyncEventsLoop`. The new path is selected by default.
-* Runtime control on decrement counter only if the event is popped. There is a new way to restore dead signals cleanup for the old path.
-* A new logic in runtime to track the age of events from the kernel mode driver.
-
-#### Optimized
-
-* HSA callback performance. The HIP runtime creates and submits commands in the queue and interacts with HSA through a callback function. HIP waits for the CPU status from HSA to optimize the handling of events, profiling, commands, and HSA signals for higher performance.
-* Runtime optimization which combines all logic of `WaitAny` in a single processing loop and avoids extra memory allocations or reference counting. The runtime won't spin on the CPU if all events are busy.
-* Multi-threaded dispatches for performance improvement.
-* Command submissions and processing between CPU and GPU by introducing a way to limit the software batch size.
-* Switch to `std::shared_mutex` in book/keep logic in streams from multiple threads simultaneously, for performance improvement in specific customer applications.
-* `std::shared_mutex` is used in memory object mapping, for performance improvement.
+### **ROCm Systems Profiler** (0.1.2)
 
 #### Resolved issues
 
-* Race condition in multi-threaded producer/consumer scenario with `hipMallocFromPoolAsync`.
-* Segmentation fault with `hipStreamLegacy` while using the API `hipStreamWaitEvent`.
-* Usage of `hipStreamLegacy` in HIP event record.
-* A soft hang in graph execution process from HIP user object. The fix handles the release of graph execution object properly considering synchronization on the device/stream. The user application now behaves the same with `hipUserObject` on both the AMD ROCm and NVIDIA CUDA platforms.
-
-### **hipfort** (0.5.1)
-
-#### Added
-
-* Support for building with LLVM Flang.
-
-#### Resolved issues
-
-* Fixed the exported `hipfort::hipsparse` CMake target.
-
-### **ROCm Systems Profiler** (0.1.1)
-
-#### Resolved issues
-
-* Fixed an error when building from source on some SUSE and RHEL systems when using the `ROCPROFSYS_BUILD_DYNINST` option.
-
-### **ROCProfiler** (2.0.0)
-
-#### Changed
-
-* Replaced `CU_UTILIZATION` metric with `SIMD_UTILIZATION` for better accuracy.
-
-#### Resolved issues
-
-* Fixed the `VALUBusy` and `SALUBusy` activity metrics for accuracy on MI300.
-
-### **ROCprofiler-SDK** (0.5.0)
-
-#### Added
-
-* Support for system-wide collection of SQ counters across all HSA processes.
-
-#### Changed
-
-* `rocprofiler_sample_device_counting_service` API updated to return counter output immediately, when called in synchronous mode.
+* Fixed an error that prevented GPU hardware activity from being presented in certain workloads.  
 
 ## ROCm known issues
 
 ROCm known issues are noted on {fab}`github` [GitHub](https://github.com/ROCm/ROCm/labels/Verified%20Issue). For known
 issues related to individual components, review the [Detailed component changes](#detailed-component-changes).
 
-## ROCm resolved issues
+### Zero value is displayed in ROCTx aggregated statistics
 
-The following are previously known issues resolved in this release. For resolved issues related to
-individual components, review the [Detailed component changes](#detailed-component-changes).
-
-### TransferBench packages not functional
-
-Issue with TransferBench packages not being compiled properly has been fixed. For more information, See [GitHub issue #4081](https://github.com/ROCm/ROCm/issues/4081).
-
-### ROCm Compute Profiler CTest failure in CI
-
-When running the ROCm Compute Profiler (`rocprof-compute`) CTest in the Azure CI environment, the
-`rocprof-compute` execution test failed. This issue was due to an outdated test file that was not renamed
-(`omniperf` to `rocprof-compute`), and the `ROCM_PATH` environment variable not being set in
-the Azure CI environment, resulting in the tool being unable to extract chip information as expected.
-This issue has been fixed in the ROCm 6.3.2 release. See [GitHub issue #4085](https://github.com/ROCm/ROCm/issues/4085).
-
-### MIVisionX memory access fault in Canny edge detection
-
-An issue where Canny edge detection kernels accessed out-of-bounds memory locations while
-computing gradient intensities on edge pixels has been fixed. This issue was isolated to
-Canny-specific use cases on Instinct MI300 series accelerators. See [GitHub issue #4086](https://github.com/ROCm/ROCm/issues/4086).
-
-### AMD VCN instability with rocDecode
-
-A firmware crash on gfx942 devices when AMD Video Core Next (VCN) was used for rocDecode operations has been resolved.
+The ROCTx markers are standalone markers within the ROCProfiler-SDK library. Each marker reports only a single timestamp, which is recorded as the `start_timestamp` and `end_timestamp`. As a result, the value for aggregated statistics presented in `TotalDurationNs`, `maxNs`, and `minNs`, is zero. The zero value indicates that the actual execution time is not associated with the markers, which is an expected behavior. See [GitHub issue #4396](https://github.com/ROCm/ROCm/issues/4396).
 
 ## ROCm upcoming changes
 
 The following changes to the ROCm software stack are anticipated for future releases.
 
+### ROCTracer and ROCProfiler (rocprof and rocprofv2) deprecation
+
+Development and support for ROCTracer and ROCProfiler (`rocprof` and `rocprofv2`) will phase out in favor of ROCprofiler-SDK (`rocprofv3`) in upcoming ROCm releases. Going forward, only critical defect fixes will be addressed for older versions of profiling tools and libraries. Upgrade to the latest version of ROCprofiler-SDK (`rocprofv3`) library to ensure continued support and access to new features.
+
 ### AMDGPU wavefront size compiler macro deprecation
 
 The `__AMDGCN_WAVEFRONT_SIZE__` macro will be deprecated in an upcoming
 release. It is recommended to remove any use of this macro. For more information, see [AMDGPU
-support](https://rocm.docs.amd.com/projects/llvm-project/en/docs-6.3.2/LLVM/clang/html/AMDGPUSupport.html).
+support](https://rocm.docs.amd.com/projects/llvm-project/en/docs-6.3.3/LLVM/clang/html/AMDGPUSupport.html).
 
 ### HIPCC Perl scripts deprecation
 

default.xml

@@ -1,7 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <manifest>
     <remote name="rocm-org" fetch="https://github.com/ROCm/" />
-    <default revision="refs/tags/rocm-6.3.2"
+    <default revision="refs/tags/rocm-6.3.3"
      remote="rocm-org"
      sync-c="true"
      sync-j="4" />

docs/about/license.md

@@ -62,7 +62,7 @@ additional licenses. Please review individual repositories for more information.
 | [rocJPEG](https://github.com/ROCm/rocJPEG/) | [MIT](https://github.com/ROCm/rocJPEG/blob/develop/LICENSE) |
 | [ROCK-Kernel-Driver](https://github.com/ROCm/ROCK-Kernel-Driver/) | [GPL 2.0 WITH Linux-syscall-note](https://github.com/ROCm/ROCK-Kernel-Driver/blob/master/COPYING) |
 | [rocminfo](https://github.com/ROCm/rocminfo/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocminfo/blob/amd-staging/License.txt) |
-| [ROCm Bandwidth Test](https://github.com/ROCm/rocm_bandwidth_test/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocm_bandwidth_test/blob/master/LICENSE.txt) |
+| [ROCm Bandwidth Test](https://github.com/ROCm/rocm_bandwidth_test/) | [MIT](https://github.com/ROCm/rocm_bandwidth_test/blob/master/LICENSE.txt) |
 | [ROCm CMake](https://github.com/ROCm/rocm-cmake/) | [MIT](https://github.com/ROCm/rocm-cmake/blob/develop/LICENSE) |
 | [ROCm Communication Collectives Library (RCCL)](https://github.com/ROCm/rccl/) | [Custom](https://github.com/ROCm/rccl/blob/develop/LICENSE.txt) |
 | [ROCm-Core](https://github.com/ROCm/rocm-core) | [MIT](https://github.com/ROCm/rocm-core/blob/master/copyright) |

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

@@ -1,120 +1,120 @@
-ROCm Version,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.0.0
-      :ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,"Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04",Ubuntu 24.04,,,,,
-      ,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,"Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3, 22.04.2","Ubuntu 22.04.4, 22.04.3, 22.04.2"
-      ,,,,,,,,"Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5"
-      ,"RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.3, 9.2","RHEL 9.3, 9.2"
-      ,RHEL 8.10,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8"
-      ,"SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4"
-      ,,,,,,,,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9
-      ,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,,,
-,Debian 12 [#mi300x-past-60]_,Debian 12 [#mi300x-past-60]_,,,,,,,,,,
-,Azure Linux 3.0 [#mi300x-past-60]_,,,,,,,,,,,
-      ,.. _architecture-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3
-      ,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2
-      ,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA
-      ,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3
-      ,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2
-      ,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100
-      ,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030
-      ,gfx942,gfx942,gfx942,gfx942 [#mi300_624-past-60]_,gfx942 [#mi300_622-past-60]_,gfx942 [#mi300_621-past-60]_,gfx942 [#mi300_620-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_611-past-60]_, gfx942 [#mi300_610-past-60]_, gfx942 [#mi300_602-past-60]_, gfx942 [#mi300_600-past-60]_
-      ,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a
-      ,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
-,,,,,,,,,,,,
-      FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
-      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
-      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
-      ,,,,,,,,,,,,
-      THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      `UCC <https://github.com/ROCm/ucc>`_,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.2.0,>=1.2.0
-      `UCX <https://github.com/ROCm/ucx>`_,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1
-      ,,,,,,,,,,,,
-      THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      Thrust,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-      CUB,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-,,,,,,,,,,,,
-      KMD & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      Tested user space versions,"6.3.x, 6.2.x, 6.1.x","6.3.x, 6.2.x, 6.1.x","6.3.x, 6.2.x, 6.1.x","6.3.x, 6.2.x, 6.1.x, 6.0.x","6.3.x, 6.2.x, 6.1.x, 6.0.x","6.3.x, 6.2.x, 6.1.x, 6.0.x","6.3.x, 6.2.x, 6.1.x, 6.0.x","6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
-      ,,,,,,,,,,,,
-      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      :doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0
-      :doc:`MIGraphX <amdmigraphx:index>`,2.11.0,2.11.0,2.11.0,2.10.0,2.10.0,2.10.0,2.10.0,2.9.0,2.9.0,2.9.0,2.8.0,2.8.0
-      :doc:`MIOpen <miopen:index>`,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`MIVisionX <mivisionx:index>`,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
-      :doc:`rocAL <rocal:index>`,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      :doc:`rocDecode <rocdecode:index>`,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
-      :doc:`rocJPEG <rocjpeg:index>`,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`rocPyDecode <rocpydecode:index>`,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A
-      :doc:`RPP <rpp:index>`,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
-      ,,,,,,,,,,,,
-      COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      :doc:`RCCL <rccl:index>`,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
-      ,,,,,,,,,,,,
-      MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0
-      :doc:`hipBLAS <hipblas:index>`,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0
-      :doc:`hipBLASLt <hipblaslt:index>`,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
-      :doc:`hipFFT <hipfft:index>`,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
-      :doc:`hipfort <hipfort:index>`,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
-      :doc:`hipRAND <hiprand:index>`,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
-      :doc:`hipSOLVER <hipsolver:index>`,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
-      :doc:`hipSPARSE <hipsparse:index>`,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
-      :doc:`rocALUTION <rocalution:index>`,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
-      :doc:`rocBLAS <rocblas:index>`,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
-      :doc:`rocFFT <rocfft:index>`,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
-      :doc:`rocRAND <rocrand:index>`,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
-      :doc:`rocSOLVER <rocsolver:index>`,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
-      :doc:`rocSPARSE <rocsparse:index>`,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
-      :doc:`rocWMMA <rocwmma:index>`,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
-      :doc:`Tensile <tensile:src/index>`,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
-      ,,,,,,,,,,,,
-      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      :doc:`hipCUB <hipcub:index>`,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`hipTensor <hiptensor:index>`,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
-      :doc:`rocPRIM <rocprim:index>`,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`rocThrust <rocthrust:index>`,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      ,,,,,,,,,,,,
-      SUPPORT LIBS,,,,,,,,,,,,
-      `hipother <https://github.com/ROCm/hipother>`_,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,6.1.2,6.1.1,6.1.0,6.0.2,6.0.0
-      `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,20240607.5.7,20240607.5.7,20240607.4.05,20240607.1.4246,20240125.5.08,20240125.5.08,20240125.3.30,20231016.2.245,20231016.2.245
-      ,,,,,,,,,,,,
-      SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      :doc:`AMD SMI <amdsmi:index>`,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
-      :doc:`ROCm Data Center Tool <rdc:index>`,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0
-      :doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      :doc:`ROCm SMI <rocm_smi_lib:index>`,7.4.0,7.4.0,7.4.0,7.3.0,7.3.0,7.3.0,7.3.0,7.2.0,7.0.0,7.0.0,6.0.2,6.0.0
-      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
-      ,,,,,,,,,,,,
-      PERFORMANCE TOOLS,,,,,,,,,,,,
-      :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0
-      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.1,2.0.1,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
-      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCTracer <roctracer:index>`,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
-      ,,,,,,,,,,,,
-      DEVELOPMENT TOOLS,,,,,,,,,,,,
-      :doc:`HIPIFY <hipify:index>`,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
-      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
-      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
-      `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,N/A,N/A
-      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
-      ,,,,,,,,,,,,
-      COMPILERS,.. _compilers-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
-      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      `Flang <https://github.com/ROCm/flang>`_,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      :doc:`llvm-project <llvm-project:index>`,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-,,,,,,,,,,,,
-      RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,
-      :doc:`AMD CLR <hip:understand/amd_clr>`,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      :doc:`HIP <hip:index>`,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0
-      :doc:`ROCr Runtime <rocr-runtime:index>`,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.13.0,1.13.0,1.13.0,1.13.0,1.12.0,1.12.0
+ROCm Version,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.0.0
+      :ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,"Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04",Ubuntu 24.04,,,,,
+      ,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,"Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3, 22.04.2","Ubuntu 22.04.4, 22.04.3, 22.04.2"
+      ,,,,,,,,,"Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5"
+      ,"RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.3, 9.2","RHEL 9.3, 9.2"
+      ,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8"
+      ,"SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4"
+      ,,,,,,,,,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9
+      ,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,,,
+,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,,,,,,,,,,
+,Azure Linux 3.0 [#mi300x-past-60]_,Azure Linux 3.0 [#mi300x-past-60]_,,,,,,,,,,,
+      ,.. _architecture-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3
+      ,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2
+      ,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA
+      ,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3
+      ,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2
+      ,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100
+      ,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030
+      ,gfx942,gfx942,gfx942,gfx942,gfx942 [#mi300_624-past-60]_,gfx942 [#mi300_622-past-60]_,gfx942 [#mi300_621-past-60]_,gfx942 [#mi300_620-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_611-past-60]_, gfx942 [#mi300_610-past-60]_, gfx942 [#mi300_602-past-60]_, gfx942 [#mi300_600-past-60]_
+      ,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a
+      ,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
+,,,,,,,,,,,,,
+      FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
+      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
+      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
+      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
+      ,,,,,,,,,,,,,
+      THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      `UCC <https://github.com/ROCm/ucc>`_,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.2.0,>=1.2.0
+      `UCX <https://github.com/ROCm/ucx>`_,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1
+      ,,,,,,,,,,,,,
+      THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      Thrust,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
+      CUB,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
+,,,,,,,,,,,,,
+      KMD & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      Tested user space versions,"6.3.x, 6.2.x, 6.1.x","6.3.x, 6.2.x, 6.1.x","6.3.x, 6.2.x, 6.1.x","6.3.x, 6.2.x, 6.1.x","6.3.x, 6.2.x, 6.1.x, 6.0.x","6.3.x, 6.2.x, 6.1.x, 6.0.x","6.3.x, 6.2.x, 6.1.x, 6.0.x","6.3.x, 6.2.x, 6.1.x, 6.0.x","6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
+      ,,,,,,,,,,,,,
+      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      :doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0
+      :doc:`MIGraphX <amdmigraphx:index>`,2.11.0,2.11.0,2.11.0,2.11.0,2.10.0,2.10.0,2.10.0,2.10.0,2.9.0,2.9.0,2.9.0,2.8.0,2.8.0
+      :doc:`MIOpen <miopen:index>`,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
+      :doc:`MIVisionX <mivisionx:index>`,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
+      :doc:`rocAL <rocal:index>`,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
+      :doc:`rocDecode <rocdecode:index>`,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
+      :doc:`rocJPEG <rocjpeg:index>`,0.6.0,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`rocPyDecode <rocpydecode:index>`,0.2.0,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A
+      :doc:`RPP <rpp:index>`,1.9.1,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
+      ,,,,,,,,,,,,,
+      COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      :doc:`RCCL <rccl:index>`,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
+      ,,,,,,,,,,,,,
+      MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0
+      :doc:`hipBLAS <hipblas:index>`,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0
+      :doc:`hipBLASLt <hipblaslt:index>`,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
+      :doc:`hipFFT <hipfft:index>`,1.0.17,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
+      :doc:`hipfort <hipfort:index>`,0.5.1,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
+      :doc:`hipRAND <hiprand:index>`,2.11.1,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
+      :doc:`hipSOLVER <hipsolver:index>`,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
+      :doc:`hipSPARSE <hipsparse:index>`,3.1.2,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
+      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.2,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
+      :doc:`rocALUTION <rocalution:index>`,3.2.1,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
+      :doc:`rocBLAS <rocblas:index>`,4.3.0,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
+      :doc:`rocFFT <rocfft:index>`,1.0.31,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
+      :doc:`rocRAND <rocrand:index>`,3.2.0,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
+      :doc:`rocSOLVER <rocsolver:index>`,3.27.0,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
+      :doc:`rocSPARSE <rocsparse:index>`,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
+      :doc:`rocWMMA <rocwmma:index>`,1.6.0,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
+      :doc:`Tensile <tensile:src/index>`,4.42.0,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
+      ,,,,,,,,,,,,,
+      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      :doc:`hipCUB <hipcub:index>`,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
+      :doc:`hipTensor <hiptensor:index>`,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
+      :doc:`rocPRIM <rocprim:index>`,3.3.0,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
+      :doc:`rocThrust <rocthrust:index>`,3.3.0,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
+      ,,,,,,,,,,,,,
+      SUPPORT LIBS,,,,,,,,,,,,,
+      `hipother <https://github.com/ROCm/hipother>`_,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
+      `rocm-core <https://github.com/ROCm/rocm-core>`_,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,6.1.2,6.1.1,6.1.0,6.0.2,6.0.0
+      `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,20240607.5.7,20240607.5.7,20240607.4.05,20240607.1.4246,20240125.5.08,20240125.5.08,20240125.3.30,20231016.2.245,20231016.2.245
+      ,,,,,,,,,,,,,
+      SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      :doc:`AMD SMI <amdsmi:index>`,24.7.1,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
+      :doc:`ROCm Data Center Tool <rdc:index>`,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0
+      :doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
+      :doc:`ROCm SMI <rocm_smi_lib:index>`,7.4.0,7.4.0,7.4.0,7.4.0,7.3.0,7.3.0,7.3.0,7.3.0,7.2.0,7.0.0,7.0.0,6.0.2,6.0.0
+      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.1.0,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
+      ,,,,,,,,,,,,,
+      PERFORMANCE TOOLS,,,,,,,,,,,,,
+      :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0
+      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.0.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.1,2.0.1,N/A,N/A,N/A,N/A,N/A
+      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,0.1.2,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A
+      :doc:`ROCProfiler <rocprofiler:index>`,2.0.60303,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
+      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A
+      :doc:`ROCTracer <roctracer:index>`,4.1.60303,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
+      ,,,,,,,,,,,,,
+      DEVELOPMENT TOOLS,,,,,,,,,,,,,
+      :doc:`HIPIFY <hipify:index>`,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
+      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.0,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
+      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,15.2.0,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
+      `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,N/A,N/A
+      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
+      ,,,,,,,,,,,,,
+      COMPILERS,.. _compilers-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
+      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
+      `Flang <https://github.com/ROCm/flang>`_,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      :doc:`llvm-project <llvm-project:index>`,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+,,,,,,,,,,,,,
+      RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,,
+      :doc:`AMD CLR <hip:understand/amd_clr>`,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
+      :doc:`HIP <hip:index>`,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
+      `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0
+      :doc:`ROCr Runtime <rocr-runtime:index>`,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.13.0,1.13.0,1.13.0,1.13.0,1.12.0,1.12.0

docs/compatibility/compatibility-matrix.rst

@@ -23,7 +23,7 @@ compatibility and system requirements.
 .. container:: format-big-table
 
   .. csv-table::
-      :header: "ROCm Version", "6.3.2", "6.3.1", "6.2.0"
+      :header: "ROCm Version", "6.3.3", "6.3.2", "6.2.0"
       :stub-columns: 1
 
       :ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04
@@ -32,8 +32,8 @@ compatibility and system requirements.
       ,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9"
       ,"SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5"
       ,Oracle Linux 8.10 [#mi300x]_,Oracle Linux 8.10 [#mi300x]_,Oracle Linux 8.9 [#mi300x]_
-      ,Debian 12 [#mi300x]_,Debian 12 [#mi300x]_,
-      ,Azure Linux 3.0 [#mi300x]_,,
+      ,Debian 12 [#single-node]_,Debian 12 [#single-node]_,
+      ,Azure Linux 3.0 [#mi300x]_,Azure Linux 3.0 [#mi300x]_,
       ,.. _architecture-support-compatibility-matrix:,,
       :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA3,CDNA3,CDNA3
       ,CDNA2,CDNA2,CDNA2
@@ -83,7 +83,7 @@ compatibility and system requirements.
       :doc:`hipBLAS <hipblas:index>`,2.3.0,2.3.0,2.2.0
       :doc:`hipBLASLt <hipblaslt:index>`,0.10.0,0.10.0,0.8.0
       :doc:`hipFFT <hipfft:index>`,1.0.17,1.0.17,1.0.14
-      :doc:`hipfort <hipfort:index>`,0.5.1,0.5.0,0.4.0
+      :doc:`hipfort <hipfort:index>`,0.5.1,0.5.1,0.4.0
       :doc:`hipRAND <hiprand:index>`,2.11.1,2.11.1,2.11.0
       :doc:`hipSOLVER <hipsolver:index>`,2.3.0,2.3.0,2.2.0
       :doc:`hipSPARSE <hipsparse:index>`,3.1.2,3.1.2,3.1.1
@@ -104,8 +104,8 @@ compatibility and system requirements.
       :doc:`rocThrust <rocthrust:index>`,3.3.0,3.3.0,3.0.1
       ,,,
       SUPPORT LIBS,,,
-      `hipother <https://github.com/ROCm/hipother>`_,6.3.42134,6.3.42133,6.2.41133
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,6.3.2,6.3.1,6.2.0
+      `hipother <https://github.com/ROCm/hipother>`_,6.3.42134,6.3.42134,6.2.41133
+      `rocm-core <https://github.com/ROCm/rocm-core>`_,6.3.3,6.3.2,6.2.0
       `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_,20240607.1.4246
       ,,,
       SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix:,,
@@ -118,37 +118,39 @@ compatibility and system requirements.
       PERFORMANCE TOOLS,,,
       :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,1.4.0,1.4.0,1.4.0
       :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.0.0,3.0.0,2.0.1
-      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,0.1.1,0.1.0,1.11.2
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.60302,2.0.60301,2.0.60200
+      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,0.1.2,0.1.1,1.11.2
+      :doc:`ROCProfiler <rocprofiler:index>`,2.0.60303,2.0.60302,2.0.60200
       :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,0.5.0,0.5.0,0.4.0
-      :doc:`ROCTracer <roctracer:index>`,4.1.60302,4.1.60301,4.1.60200
+      :doc:`ROCTracer <roctracer:index>`,4.1.60303,4.1.60302,4.1.60200
       ,,,
       DEVELOPMENT TOOLS,,,
-      :doc:`HIPIFY <hipify:index>`,18.0.0.25012,18.0.0.24491,18.0.0.24232
+      :doc:`HIPIFY <hipify:index>`,18.0.0.25012,18.0.0.25012,18.0.0.24232
       :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.13.0
       :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.0,0.77.0,0.76.0
       :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,15.2.0,15.2.0,14.2.0
       `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.4.0,0.4.0,0.4.0
       :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.0.3,2.0.3,2.0.3
       ,,,
-      COMPILERS,.. _compilers-support-compatibility-matrix:,..
+      COMPILERS,.. _compilers-support-compatibility-matrix:,,
       `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A
       :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1
-      `Flang <https://github.com/ROCm/flang>`_,18.0.0.25012,18.0.0.24491,18.0.0.24232
-      :doc:`llvm-project <llvm-project:index>`,18.0.0.25012,18.0.0.24491,18.0.0.24232
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,18.0.0.25012,18.0.0.24491,18.0.0.24232
+      `Flang <https://github.com/ROCm/flang>`_,18.0.0.25012,18.0.0.25012,18.0.0.24232
+      :doc:`llvm-project <llvm-project:index>`,18.0.0.25012,18.0.0.25012,18.0.0.24232
+      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,18.0.0.25012,18.0.0.25012,18.0.0.24232
       ,,,
-      RUNTIMES,.. _runtime-support-compatibility-matrix:,..
-      :doc:`AMD CLR <hip:understand/amd_clr>`,6.3.42134,6.3.42133,6.2.41133
-      :doc:`HIP <hip:index>`,6.3.42134,6.3.42133,6.2.41133
+      RUNTIMES,.. _runtime-support-compatibility-matrix:,,
+      :doc:`AMD CLR <hip:understand/amd_clr>`,6.3.42134,6.3.42134,6.2.41133
+      :doc:`HIP <hip:index>`,6.3.42134,6.3.42134,6.2.41133
       `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0
       :doc:`ROCr Runtime <rocr-runtime:index>`,1.14.0,1.14.0,1.13.0
 
 
 
+
 .. rubric:: Footnotes
 
-.. [#mi300x] Oracle Linux, Debian, and Azure Linux are supported only on AMD Instinct MI300X.
+.. [#mi300x] Oracle Linux and Azure Linux are supported only on AMD Instinct MI300X.
+.. [#single-node] Debian 12 is supported only on AMD Instinct MI300X for single-node functionality. 
 .. [#mi300_620] **For ROCm 6.2.0** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
 .. [#kfd_support] ROCm provides forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software for +/- 2 releases. These are the compatibility combinations that are currently supported.
 .. [#ROCT-rocr] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.
@@ -215,7 +217,8 @@ Expand for full historical view of:
 
    .. rubric:: Footnotes
 
-   .. [#mi300x-past-60] Oracle Linux, Debian, and Azure Linux are supported only on AMD Instinct MI300X.
+   .. [#mi300x-past-60] Oracle Linux and Azure Linux are supported only on AMD Instinct MI300X.
+   .. [#single-node-past-60] Debian 12 is supported only on AMD Instinct MI300X for single-node functionality. 
    .. [#mi300_624-past-60] **For ROCm 6.2.4** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
    .. [#mi300_622-past-60] **For ROCm 6.2.2** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
    .. [#mi300_621-past-60] **For ROCm 6.2.1** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].

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

@@ -56,7 +56,7 @@ Docker image compatibility
 
 AMD validates and publishes ready-made `PyTorch images <https://hub.docker.com/r/rocm/pytorch>`_
 with ROCm backends on Docker Hub. The following Docker image tags and
-associated inventories are validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
+associated inventories are validated for `ROCm 6.3.3 <https://repo.radeon.com/rocm/apt/6.3.3/>`_.
 Click the |docker-icon| icon to view the image on Docker Hub.
 
 .. list-table:: PyTorch Docker image components
@@ -77,26 +77,26 @@ Click the |docker-icon| icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3_ubuntu24.04_py3.12_pytorch_release_2.4.0/images/sha256-98ddf20333bd01ff749b8092b1190ee369a75d3b8c71c2fac80ffdcb1a98d529?context=explore"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3.3_ubuntu24.04_py3.12_pytorch_release_2.4.0/images/sha256-6c798857b2c9526b44ba535710b93a1737546acea79b53a93c646195c272f1d5"><i class="fab fa-docker fa-lg"></i></a>
 
       - `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
       - 24.04
-      - `3.12 <https://www.python.org/downloads/release/python-3128/>`_
+      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
       - `1.4.0 <https://github.com/ROCm/apex/tree/release/1.4.0>`_
       - `0.19.0 <https://github.com/pytorch/vision/tree/v0.19.0>`_
       - `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
       - `master <https://bitbucket.org/icl/magma/src/master/>`_
       - `1.10.0 <https://github.com/openucx/ucx/tree/v1.10.0>`_
-      - `4.0.7 <https://github.com/open-mpi/ompi/tree/v4.0.7>`_
+      - `4.0.3 <https://github.com/open-mpi/ompi/tree/v4.0.3>`_
       - `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`_
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3_ubuntu22.04_py3.10_pytorch_release_2.4.0/images/sha256-402c9b4f1a6b5a81c634a1932b56cbe01abb699cfcc7463d226276997c6cf8ea?context=explore"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3.3_ubuntu22.04_py3.10_pytorch_release_2.4.0/images/sha256-a09b21248133876fc8912a5ff4e6ee2c8d62b14120313e426b3dadda5702713d"><i class="fab fa-docker fa-lg"></i></a>
 
       - `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
       - 22.04
-      - `3.10 <https://www.python.org/downloads/release/python-31016/>`_
+      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
       - `1.4.0 <https://github.com/ROCm/apex/tree/release/1.4.0>`_
       - `0.19.0 <https://github.com/pytorch/vision/tree/v0.19.0>`_
       - `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
@@ -107,11 +107,11 @@ Click the |docker-icon| icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3_ubuntu22.04_py3.9_pytorch_release_2.4.0/images/sha256-e0608b55d408c3bfe5c19fdd57a4ced3e0eb3a495b74c309980b60b156c526dd?context=explore"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3.3_ubuntu22.04_py3.9_pytorch_release_2.4.0/images/sha256-963187534467f0f9da77996762fc1d112a6faa5372277c348a505533e7876ec8"><i class="fab fa-docker fa-lg"></i></a>
 
       - `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
       - 22.04
-      - `3.9.18 <https://www.python.org/downloads/release/python-3918/>`_
+      - `3.9.21 <https://www.python.org/downloads/release/python-3921/>`_
       - `1.4.0 <https://github.com/ROCm/apex/tree/release/1.4.0>`_
       - `0.19.0 <https://github.com/pytorch/vision/tree/v0.19.0>`_
       - `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
@@ -122,11 +122,11 @@ Click the |docker-icon| icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3_ubuntu22.04_py3.10_pytorch_release_2.3.0/images/sha256-652cf25263d05b1de548222970aeb76e60b12de101de66751264709c0d0ff9d8?context=explore"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3.3_ubuntu22.04_py3.10_pytorch_release_2.3.0/images/sha256-952f2621bd2bf3078bef19061e05b209105a82a7908e7e6cdf85014938a4d93a"><i class="fab fa-docker fa-lg"></i></a>
 
       - `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`_
       - 22.04
-      - `3.10 <https://www.python.org/downloads/release/python-31016/>`_
+      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
       - `1.3.0 <https://github.com/ROCm/apex/tree/release/1.3.0>`_
       - `0.18.0 <https://github.com/pytorch/vision/tree/v0.18.0>`_
       - `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
@@ -137,7 +137,7 @@ Click the |docker-icon| icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3_ubuntu22.04_py3.10_pytorch_release_2.2.1/images/sha256-051976f26beab8f9aa65d999e3ad546c027b39240a0cc3ee81b114a9024f2912?context=explore"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3.3_ubuntu22.04_py3.10_pytorch_release_2.2.1/images/sha256-a2fe20e170feb9e05da3e5728bb98e40d08567e137be8e6ba797962ed2852608"><i class="fab fa-docker fa-lg"></i></a>
 
       - `2.2.1 <https://github.com/ROCm/pytorch/tree/release/2.2>`_
       - 22.04
@@ -152,7 +152,7 @@ Click the |docker-icon| icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3_ubuntu20.04_py3.9_pytorch_release_2.2.1/images/sha256-88c839a364d109d3748c100385bfa100d28090d25118cc723fd0406390ab2f7e?context=explore"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3.3_ubuntu20.04_py3.9_pytorch_release_2.2.1/images/sha256-7f231937c897cca5f89e360be33c70a2017d60f62d1fbe81292be48c15fe345b"><i class="fab fa-docker fa-lg"></i></a>
 
       - `2.2.1 <https://github.com/ROCm/pytorch/tree/release/2.2>`_
       - 20.04
@@ -167,14 +167,14 @@ Click the |docker-icon| icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3_ubuntu22.04_py3.9_pytorch_release_1.13.1/images/sha256-994424ed07a63113f79dd9aa72159124c00f5fbfe18127151e6658f7d0b6f821?context=explore"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3.3_ubuntu22.04_py3.9_pytorch_release_1.13.1/images/sha256-616a47758004f91951e2da6c1fe291f903de65a7b2318d4b18359b48fe3032f4"><i class="fab fa-docker fa-lg"></i></a>
 
       - `1.13.1 <https://github.com/ROCm/pytorch/tree/release/1.13>`_
       - 22.04
       - `3.9.21 <https://www.python.org/downloads/release/python-3921/>`_
       - `1.0.0 <https://github.com/ROCm/apex/tree/release/1.0.0>`_
       - `0.14.0 <https://github.com/pytorch/vision/tree/v0.14.0>`_
-      - `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18>`_
+      - `2.19.0 <https://github.com/tensorflow/tensorboard/tree/2.19>`_
       - `master <https://bitbucket.org/icl/magma/src/master/>`_
       - `1.14.1 <https://github.com/openucx/ucx/tree/v1.14.1>`_
       - `4.1.5 <https://github.com/open-mpi/ompi/tree/v4.1.5>`_
@@ -182,7 +182,7 @@ Click the |docker-icon| icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3_ubuntu20.04_py3.9_pytorch_release_1.13.1/images/sha256-7b8139fe40a9aeb4bca3aecd15c22c1fa96e867d93479fa3a24fdeeeeafa1219?context=explore"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.3.3_ubuntu20.04_py3.9_pytorch_release_1.13.1/images/sha256-a2cfb365aea58b84595e241ffdb0d5ef3e6566e98c10b5499f4aa29983a74ea2"><i class="fab fa-docker fa-lg"></i></a>
 
       - `1.13.1 <https://github.com/ROCm/pytorch/tree/release/1.13>`_
       - 20.04

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

@@ -54,7 +54,7 @@ Docker image compatibility
 AMD validates and publishes ready-made `TensorFlow images
 <https://hub.docker.com/r/rocm/tensorflow>`_ with ROCm backends on
 Docker Hub. The following Docker image tags and associated inventories are
-validated for `ROCm 6.3.1 <https://repo.radeon.com/rocm/apt/6.3.1/>`_. Click
+validated for `ROCm 6.3.3 <https://repo.radeon.com/rocm/apt/6.3.3/>`_. Click
 the |docker-icon| icon to view the image on Docker Hub.
 
 .. list-table:: TensorFlow Docker image components
@@ -68,47 +68,47 @@ the |docker-icon| icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.12-tf2.17.0-dev/images/sha256-804121ee4985718277ba7dcec53c57bdade130a1ef42f544b6c48090ad379c17"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.3-py3.12-tf2.17-dev/images/sha256-fd2653f436880366cc874aa24264ca9dabd892d76ccb63fb807debba459bcaaf"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
 
-      - `tensorflow-rocm 2.17.0 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.17.0-cp312-cp312-manylinux_2_28_x86_64.whl>`__
+      - `tensorflow-rocm 2.17.0 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3.3/tensorflow_rocm-2.17.0-cp312-cp312-manylinux_2_28_x86_64.whl>`__
       - dev
-      - `Python 3.12 <https://www.python.org/downloads/release/python-3124/>`_
+      - `Python 3.12.4 <https://www.python.org/downloads/release/python-3124/>`_
       - `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.10-tf2.17.0-dev/images/sha256-776837ffa945913f6c466bfe477810a11453d21d5b6afb200be1c36e48fbc08e"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.3-py3.10-tf2.17-dev/images/sha256-8a5eb7443798935dd269575e2abae847b702e1dfb06766ab84f081a6314d8b95"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
 
-      - `tensorflow-rocm 2.17.0 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.17.0-cp310-cp310-manylinux_2_28_x86_64.whl>`__
+      - `tensorflow-rocm 2.17.0 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3.3/tensorflow_rocm-2.17.0-cp310-cp310-manylinux_2_28_x86_64.whl>`__
       - dev
-      - `Python 3.10 <https://www.python.org/downloads/release/python-31012/>`_
-      - `TensorBoard 2.17.0 <https://github.com/tensorflow/tensorboard/tree/2.17.0>`_
+      - `Python 3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
+      - `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.12-tf2.16.2-dev/images/sha256-c793e1483e30809c3c28fc5d7805bedc033c73da224f839fff370717cb100944"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.3-py3.12-tf2.16-dev/images/sha256-8fc939b10cdd6d2b11407474880d4c8ab2b52ab6e2d1743c921fc2adbfd0422f"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
 
-      - `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
+      - `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3.3/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
       - dev
-      - `Python 3.12 <https://www.python.org/downloads/release/python-3124/>`_
+      - `Python 3.12.4 <https://www.python.org/downloads/release/python-3124/>`_
       - `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.10-tf2.16.0-dev/images/sha256-263e78414ae85d7bcd52a025a94131d0a279872a45ed632b9165336dfdcd4443"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.3-py3.10-tf2.16-dev/images/sha256-a4cc6ab23d59fdf5459ceac1f0a603e6c16ae7f885d30e42c0c2b3ac60c2ad10"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
 
-      - `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.16.2-cp310-cp310-manylinux_2_28_x86_64.whl>`__
+      - `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3.3/tensorflow_rocm-2.16.2-cp310-cp310-manylinux_2_28_x86_64.whl>`__
       - dev
-      - `Python 3.10 <https://www.python.org/downloads/release/python-31012/>`_
+      - `Python 3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
       - `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.10-tf2.15.0-dev/images/sha256-479046a8477ca701a9494a813ab17e8ab4f6baa54641e65dc8d07629f1e6a880"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.3-py3.10-tf2.15-dev/images/sha256-60887c488421184adcb60b9ed4f72a8bd7bdb64d238e50943ca7cbde38e4aa48"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
 
-      - `tensorflow-rocm 2.15.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.15.1-cp310-cp310-manylinux_2_28_x86_64.whl>`_
+      - `tensorflow-rocm 2.15.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3.3/tensorflow_rocm-2.15.1-cp310-cp310-manylinux_2_28_x86_64.whl>`_
       - dev
-      - `Python 3.10 <https://www.python.org/downloads/release/python-31012/>`_
+      - `Python 3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
       - `TensorBoard 2.15.2 <https://github.com/tensorflow/tensorboard/tree/2.15.2>`_
 
 Critical ROCm libraries for TensorFlow

docs/conf.py

@@ -30,15 +30,15 @@ if os.environ.get("READTHEDOCS", "") == "True":
 project = "ROCm Documentation"
 author = "Advanced Micro Devices, Inc."
 copyright = "Copyright (c) 2025 Advanced Micro Devices, Inc. All rights reserved."
-version = "6.3.2"
-release = "6.3.2"
+version = "6.3.3"
+release = "6.3.3"
 setting_all_article_info = True
 all_article_info_os = ["linux", "windows"]
 all_article_info_author = ""
 
 # pages with specific settings
 article_pages = [
-    {"file": "about/release-notes", "os": ["linux"], "date": "2025-01-28"},
+    {"file": "about/release-notes", "os": ["linux"], "date": "2025-02-19"},
     {"file": "compatibility/compatibility-matrix", "os": ["linux"]},
     {"file": "compatibility/ml-compatibility/pytorch-compatibility", "os": ["linux"]},
     {"file": "compatibility/ml-compatibility/tensorflow-compatibility", "os": ["linux"]},
@@ -49,6 +49,9 @@ article_pages = [
 
     {"file": "how-to/rocm-for-ai/training/index", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/train-a-model", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/prerequisite-system-validation", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/train-a-model/benchmark-docker/megatron-lm", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/train-a-model/benchmark-docker/pytorch-training", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/scale-model-training", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/fine-tuning/index", "os": ["linux"]},

docs/how-to/rocm-for-ai/fine-tuning/index.rst

@@ -16,6 +16,9 @@ Throughout the following topics, this guide discusses the goals and :ref:`challe
 model <fine-tuning-llms-concept-challenge>` like Llama 2. In the
 sections that follow, you'll find practical guides on libraries and tools to accelerate your fine-tuning.
 
+The AI Developer Hub contains `AMD ROCm tutorials <https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/>`_ for
+training, fine-tuning, and inference. It leverages popular machine learning frameworks on AMD GPUs.
+
 - :doc:`Conceptual overview of fine-tuning LLMs <overview>`
 
 - :doc:`Fine-tuning and inference <fine-tuning-and-inference>` using a

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

@@ -12,6 +12,9 @@ You can use ROCm to perform distributed training, which enables you to train mod
  
 Overall, ROCm can be used to improve the performance and efficiency of your AI applications. With its training, fine-tuning, and inference support, ROCm provides a complete solution for optimizing AI workflows and achieving the optimum results possible on AMD GPUs. 
 
+The AI Developer Hub contains `AMD ROCm tutorials <https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/>`_ for
+training, fine-tuning, and inference. It leverages popular machine learning frameworks on AMD GPUs.
+
 In this guide, you'll learn how to use ROCm for AI:
 
 - :doc:`Training <training/index>`

docs/how-to/rocm-for-ai/inference-optimization/model-acceleration-libraries.rst

@@ -277,7 +277,7 @@ Installing FBGEMM_GPU
 Installing FBGEMM_GPU consists of the following steps:
 
 *  Set up an isolated Miniconda environment
-*  Install ROCm using Docker or the :doc:`package manager <rocm-install-on-linux:install/native-install/index>`
+*  Install ROCm using Docker or the :doc:`package manager <rocm-install-on-linux:install/install-methods/package-manager-index>`
 *  Install the nightly `PyTorch <https://pytorch.org/>`_ build
 *  Complete the pre-build and build tasks
   

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

@@ -11,6 +11,9 @@ Understanding the ROCm™ software platform’s architecture and capabilities is
 
 Throughout the following topics, this section provides a comprehensive guide to setting up and deploying AI inference on AMD GPUs. This includes instructions on how to install ROCm, how to use Hugging Face Transformers to manage pre-trained models for natural language processing (NLP) tasks, how to validate vLLM on AMD Instinct™ MI300X accelerators and illustrate how to deploy trained models in production environments. 
 
+The AI Developer Hub contains `AMD ROCm tutorials <https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/>`_ for
+training, fine-tuning, and inference. It leverages popular machine learning frameworks on AMD GPUs.
+
 - :doc:`Installing ROCm and machine learning frameworks <install>`
 
 - :doc:`Running models from Hugging Face <hugging-face-models>`

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

@@ -26,7 +26,7 @@ If you’re using a Radeon GPU for graphics-accelerated applications, refer to t
 
 ROCm supports multiple :doc:`installation methods <rocm-install-on-linux:install/install-overview>`:
 
-* :doc:`Using your Linux distribution's package manager <rocm-install-on-linux:install/native-install/index>`
+* :doc:`Using your Linux distribution's package manager <rocm-install-on-linux:install/install-methods/package-manager-index>`
 
 * :doc:`Using the AMDGPU installer <rocm-install-on-linux:install/amdgpu-install>`
 

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

@@ -10,49 +10,22 @@ LLM inference performance validation on AMD Instinct MI300X
 .. _vllm-benchmark-unified-docker:
 
 The `ROCm vLLM Docker <https://hub.docker.com/r/rocm/vllm/tags>`_ image offers
-a prebuilt, optimized environment designed for validating large language model
-(LLM) inference performance on the AMD Instinct™ MI300X accelerator. This
-ROCm vLLM Docker image integrates vLLM and PyTorch tailored specifically for the
-MI300X accelerator and includes the following components:
+a prebuilt, optimized environment for validating large language model (LLM)
+inference performance on the AMD Instinct™ MI300X accelerator. This ROCm vLLM
+Docker image integrates vLLM and PyTorch tailored specifically for the MI300X
+accelerator and includes the following components:
 
-* `ROCm 6.2.1 <https://github.com/ROCm/ROCm>`_
+* `ROCm 6.3.1 <https://github.com/ROCm/ROCm>`_
 
-* `vLLM 0.6.4 <https://docs.vllm.ai/en/latest>`_
+* `vLLM 0.6.6 <https://docs.vllm.ai/en/latest>`_
 
-* `PyTorch 2.5.0 <https://github.com/pytorch/pytorch>`_
-
-* Tuning files (in CSV format)
+* `PyTorch 2.7.0 (2.7.0a0+git3a58512) <https://github.com/pytorch/pytorch>`_
 
 With this Docker image, you can quickly validate the expected inference
-performance numbers on the MI300X accelerator. This topic also provides tips on
-optimizing performance with popular AI models.
-
-.. hlist::
-   :columns: 6
-
-   * Llama 3.1 8B
-
-   * Llama 3.1 70B
-
-   * Llama 3.1 405B
-
-   * Llama 2 7B
-
-   * Llama 2 70B
-
-   * Mixtral 8x7B
-
-   * Mixtral 8x22B
-
-   * Mixtral 7B
-
-   * Qwen2 7B
-
-   * Qwen2 72B
-
-   * JAIS 13B
-
-   * JAIS 30B
+performance numbers for the MI300X accelerator. This topic also provides tips on
+optimizing performance with popular AI models. For more information, see the lists of
+:ref:`available models for MAD-integrated benchmarking <vllm-benchmark-mad-models>`
+and :ref:`standalone benchmarking <vllm-benchmark-standalone-options>`.
 
 .. _vllm-benchmark-vllm:
 
@@ -91,9 +64,9 @@ MI300X accelerator with the prebuilt vLLM Docker image.
 
    .. code-block:: shell
 
-      docker pull rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
+      docker pull rocm/vllm:rocm6.3.1_mi300_ubuntu22.04_py3.12_vllm_0.6.6
 
-Once setup is complete, you can choose between two options to reproduce the
+Once the setup is complete, choose between two options to reproduce the
 benchmark results:
 
 -  :ref:`MAD-integrated benchmarking <vllm-benchmark-mad>`
@@ -130,45 +103,89 @@ Although the following models are preconfigured to collect latency and
 throughput performance data, you can also change the benchmarking parameters.
 Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone>` section.
 
+.. _vllm-benchmark-mad-models:
+
 Available models
 ----------------
 
-.. hlist::
-   :columns: 3
+.. list-table::
+   :header-rows: 1
+   :widths: 2, 3
 
-   * ``pyt_vllm_llama-3.1-8b``
+   * - Model name
+     - Tag
 
-   * ``pyt_vllm_llama-3.1-70b``
+   * - `Llama 3.1 8B <https://huggingface.co/meta-llama/Llama-3.1-8B>`_
+     - ``pyt_vllm_llama-3.1-8b``
 
-   * ``pyt_vllm_llama-3.1-405b``
+   * - `Llama 3.1 70B <https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct>`_
+     - ``pyt_vllm_llama-3.1-70b``
 
-   * ``pyt_vllm_llama-2-7b``
+   * - `Llama 3.1 405B <https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct>`_
+     - ``pyt_vllm_llama-3.1-405b``
 
-   * ``pyt_vllm_llama-2-70b``
+   * - `Llama 3.2 11B Vision <https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct>`_
+     - ``pyt_vllm_llama-3.2-11b-vision-instruct``
 
-   * ``pyt_vllm_mixtral-8x7b``
+   * - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`_
+     - ``pyt_vllm_llama-2-7b``
 
-   * ``pyt_vllm_mixtral-8x22b``
+   * - `Llama 2 70B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`_
+     - ``pyt_vllm_llama-2-70b``
 
-   * ``pyt_vllm_mistral-7b``
+   * - `Mixtral MoE 8x7B <https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1>`_
+     - ``pyt_vllm_mixtral-8x7b``
 
-   * ``pyt_vllm_qwen2-7b``
+   * - `Mixtral MoE 8x22B <https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1>`_
+     - ``pyt_vllm_mixtral-8x22b``
 
-   * ``pyt_vllm_qwen2-72b``
+   * - `Mistral 7B <https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3>`_
+     - ``pyt_vllm_mistral-7b``
 
-   * ``pyt_vllm_jais-13b``
+   * - `Qwen2 7B <https://huggingface.co/Qwen/Qwen2-7B-Instruct>`_
+     - ``pyt_vllm_qwen2-7b``
 
-   * ``pyt_vllm_jais-30b``
+   * - `Qwen2 72B <https://huggingface.co/Qwen/Qwen2-72B-Instruct>`_
+     - ``pyt_vllm_qwen2-72b``
 
-   * ``pyt_vllm_llama-3.1-8b_fp8``
+   * - `JAIS 13B <https://huggingface.co/core42/jais-13b-chat>`_
+     - ``pyt_vllm_jais-13b``
 
-   * ``pyt_vllm_llama-3.1-70b_fp8``
+   * - `JAIS 30B <https://huggingface.co/core42/jais-30b-chat-v3>`_
+     - ``pyt_vllm_jais-30b``
 
-   * ``pyt_vllm_llama-3.1-405b_fp8``
+   * - `DBRX Instruct <https://huggingface.co/databricks/dbrx-instruct>`_
+     - ``pyt_vllm_dbrx-instruct``
 
-   * ``pyt_vllm_mixtral-8x7b_fp8``
+   * - `Gemma 2 27B <https://huggingface.co/google/gemma-2-27b>`_
+     - ``pyt_vllm_gemma-2-27b``
 
-   * ``pyt_vllm_mixtral-8x22b_fp8``
+   * - `C4AI Command R+ 08-2024 <https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024>`_
+     - ``pyt_vllm_c4ai-command-r-plus-08-2024``
+
+   * - `DeepSeek MoE 16B <https://huggingface.co/deepseek-ai/deepseek-moe-16b-chat>`_
+     - ``pyt_vllm_deepseek-moe-16b-chat``
+
+   * - `Llama 3.1 70B FP8 <https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV>`_
+     - ``pyt_vllm_llama-3.1-70b_fp8``
+
+   * - `Llama 3.1 405B FP8 <https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV>`_
+     - ``pyt_vllm_llama-3.1-405b_fp8``
+
+   * - `Mixtral MoE 8x7B FP8 <https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV>`_
+     - ``pyt_vllm_mixtral-8x7b_fp8``
+
+   * - `Mixtral MoE 8x22B FP8 <https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV>`_
+     - ``pyt_vllm_mixtral-8x22b_fp8``
+
+   * - `Mistral 7B FP8 <https://huggingface.co/amd/Mistral-7B-v0.1-FP8-KV>`_
+     - ``pyt_vllm_mistral-7b_fp8``
+
+   * - `DBRX Instruct FP8 <https://huggingface.co/amd/dbrx-instruct-FP8-KV>`_
+     - ``pyt_vllm_dbrx_fp8``
+
+   * - `C4AI Command R+ 08-2024 FP8 <https://huggingface.co/amd/c4ai-command-r-plus-FP8-KV>`_
+     - ``pyt_vllm_command-r-plus_fp8``
 
 .. _vllm-benchmark-standalone:
 
@@ -176,13 +193,13 @@ Standalone benchmarking
 =======================
 
 You can run the vLLM benchmark tool independently by starting the
-:ref:`Docker container <vllm-benchmark-get-started>` as shown in the following
-snippet.
+`Docker container <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_mi300_ubuntu22.04_py3.12_vllm_0.6.6/images/sha256-9a12ef62bbbeb5a4c30a01f702c8e025061f575aa129f291a49fbd02d6b4d6c9>`_
+as shown in the following snippet.
 
 .. code-block::
 
-   docker pull rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
-   docker run -it --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128G --security-opt seccomp=unconfined --security-opt apparmor=unconfined --cap-add=SYS_PTRACE -v $(pwd):/workspace --env HUGGINGFACE_HUB_CACHE=/workspace --name vllm_v0.6.4 rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
+   docker pull rocm/vllm:rocm6.3.1_mi300_ubuntu22.04_py3.12_vllm_0.6.6
+   docker run -it --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 16G --security-opt seccomp=unconfined --security-opt apparmor=unconfined --cap-add=SYS_PTRACE -v $(pwd):/workspace --env HUGGINGFACE_HUB_CACHE=/workspace --name vllm_v0.6.6 rocm/vllm:rocm6.3.1_mi300_ubuntu22.04_py3.12_vllm_0.6.6
 
 In the Docker container, clone the ROCm MAD repository and navigate to the
 benchmark scripts directory at ``~/MAD/scripts/vllm``.
@@ -224,8 +241,8 @@ See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
 
 .. _vllm-benchmark-standalone-options:
 
-Options
--------
+Options and available models
+----------------------------
 
 .. list-table::
    :header-rows: 1
@@ -248,72 +265,100 @@ Options
      - Measure both throughput and latency
 
    * - ``$model_repo``
-     - ``meta-llama/Meta-Llama-3.1-8B-Instruct``
-     - Llama 3.1 8B
+     - ``meta-llama/Llama-3.1-8B-Instruct``
+     - `Llama 3.1 8B <https://huggingface.co/meta-llama/Llama-3.1-8B>`_
 
    * - (``float16``)
-     - ``meta-llama/Meta-Llama-3.1-70B-Instruct``
-     - Llama 3.1 70B
+     - ``meta-llama/Llama-3.1-70B-Instruct``
+     - `Llama 3.1 70B <https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct>`_
 
    * -
-     - ``meta-llama/Meta-Llama-3.1-405B-Instruct``
-     - Llama 3.1 405B
+     - ``meta-llama/Llama-3.1-405B-Instruct``
+     - `Llama 3.1 405B <https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct>`_
+
+   * -
+     - ``meta-llama/Llama-3.2-11B-Vision-Instruct``
+     - `Llama 3.2 11B Vision <https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct>`_
 
    * -
      - ``meta-llama/Llama-2-7b-chat-hf``
-     - Llama 2 7B
+     - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`_
 
    * -
      - ``meta-llama/Llama-2-70b-chat-hf``
-     - Llama 2 70B
+     - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`_
 
    * -
      - ``mistralai/Mixtral-8x7B-Instruct-v0.1``
-     - Mixtral 8x7B
+     - `Mixtral MoE 8x7B <https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1>`_
 
    * -
      - ``mistralai/Mixtral-8x22B-Instruct-v0.1``
-     - Mixtral 8x22B
+     - `Mixtral MoE 8x22B <https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1>`_
 
    * -
      - ``mistralai/Mistral-7B-Instruct-v0.3``
-     - Mixtral 7B
+     - `Mistral 7B <https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3>`_
 
    * -
      - ``Qwen/Qwen2-7B-Instruct``
-     - Qwen2 7B
+     - `Qwen2 7B <https://huggingface.co/Qwen/Qwen2-7B-Instruct>`_
 
    * -
      - ``Qwen/Qwen2-72B-Instruct``
-     - Qwen2 72B
+     - `Qwen2 72B <https://huggingface.co/Qwen/Qwen2-72B-Instruct>`_
 
    * -
      - ``core42/jais-13b-chat``
-     - JAIS 13B
+     - `JAIS 13B <https://huggingface.co/core42/jais-13b-chat>`_
 
    * -
      - ``core42/jais-30b-chat-v3``
-     - JAIS 30B
-
-   * - ``$model_repo``
-     - ``amd/Meta-Llama-3.1-8B-Instruct-FP8-KV``
-     - Llama 3.1 8B
-
-   * - (``float8``)
-     - ``amd/Meta-Llama-3.1-70B-Instruct-FP8-KV``
-     - Llama 3.1 70B
+     - `JAIS 30B <https://huggingface.co/core42/jais-30b-chat-v3>`_
 
    * -
-     - ``amd/Meta-Llama-3.1-405B-Instruct-FP8-KV``
-     - Llama 3.1 405B
+     - ``databricks/dbrx-instruct``
+     - `DBRX Instruct <https://huggingface.co/databricks/dbrx-instruct>`_
+
+   * -
+     - ``google/gemma-2-27b``
+     - `Gemma 2 27B <https://huggingface.co/google/gemma-2-27b>`_
+
+   * -
+     - ``CohereForAI/c4ai-command-r-plus-08-2024``
+     - `C4AI Command R+ 08-2024 <https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024>`_
+
+   * -
+     - ``deepseek-ai/deepseek-moe-16b-chat``
+     - `DeepSeek MoE 16B <https://huggingface.co/deepseek-ai/deepseek-moe-16b-chat>`_
+
+   * - ``$model_repo``
+     - ``amd/Llama-3.1-70B-Instruct-FP8-KV``
+     - `Llama 3.1 70B FP8 <https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV>`_
+
+   * - (``float8``)
+     - ``amd/Llama-3.1-405B-Instruct-FP8-KV``
+     - `Llama 3.1 405B FP8 <https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV>`_
 
    * -
      - ``amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV``
-     - Mixtral 8x7B
+     - `Mixtral MoE 8x7B FP8 <https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV>`_
 
    * -
      - ``amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV``
-     - Mixtral 8x22B
+     - `Mixtral MoE 8x22B FP8 <https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV>`_
+
+   * -
+     - ``amd/Mistral-7B-v0.1-FP8-KV``
+     - `Mistral 7B FP8 <https://huggingface.co/amd/Mistral-7B-v0.1-FP8-KV>`_
+
+   * -
+     - ``amd/dbrx-instruct-FP8-KV``
+     - `DBRX Instruct FP8 <https://huggingface.co/amd/dbrx-instruct-FP8-KV>`_
+
+   * -
+     - ``amd/c4ai-command-r-plus-FP8-KV``
+     - `C4AI Command R+ 08-2024 FP8 <https://huggingface.co/amd/c4ai-command-r-plus-FP8-KV>`_
 
    * - ``$num_gpu``
      - 1 or 8
@@ -335,34 +380,34 @@ options and their descriptions.
 Example 1: latency benchmark
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
-Use this command to benchmark the latency of the Llama 3.1 8B model on one GPU with the ``float16`` and ``float8`` data types.
+Use this command to benchmark the latency of the Llama 3.1 70B model on eight GPUs with the ``float16`` and ``float8`` data types.
 
 .. code-block::
 
-   ./vllm_benchmark_report.sh -s latency -m meta-llama/Meta-Llama-3.1-8B-Instruct -g 1 -d float16
-   ./vllm_benchmark_report.sh -s latency -m amd/Meta-Llama-3.1-8B-Instruct-FP8-KV -g 1 -d float8
+   ./vllm_benchmark_report.sh -s latency -m meta-llama/Llama-3.1-70B-Instruct -g 8 -d float16
+   ./vllm_benchmark_report.sh -s latency -m amd/Llama-3.1-70B-Instruct-FP8-KV -g 8 -d float8
 
 Find the latency reports at:
 
-- ``./reports_float16/summary/Meta-Llama-3.1-8B-Instruct_latency_report.csv``
+- ``./reports_float16/summary/Llama-3.1-70B-Instruct_latency_report.csv``
 
-- ``./reports_float8/summary/Meta-Llama-3.1-8B-Instruct-FP8-KV_latency_report.csv``
+- ``./reports_float8/summary/Llama-3.1-70B-Instruct-FP8-KV_latency_report.csv``
 
 Example 2: throughput benchmark
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Use this command to benchmark the throughput of the Llama 3.1 8B model on one GPU with the ``float16`` and ``float8`` data types.
+Use this command to benchmark the throughput of the Llama 3.1 70B model on eight GPUs with the ``float16`` and ``float8`` data types.
 
 .. code-block:: shell
 
-   ./vllm_benchmark_report.sh -s throughput -m meta-llama/Meta-Llama-3.1-8B-Instruct -g 1 -d float16
-   ./vllm_benchmark_report.sh -s throughput -m amd/Meta-Llama-3.1-8B-Instruct-FP8-KV -g 1 -d float8
+   ./vllm_benchmark_report.sh -s throughput -m meta-llama/Llama-3.1-70B-Instruct -g 8 -d float16
+   ./vllm_benchmark_report.sh -s throughput -m amd/Llama-3.1-70B-Instruct-FP8-KV -g 8 -d float8
 
 Find the throughput reports at:
 
-- ``./reports_float16/summary/Meta-Llama-3.1-8B-Instruct_throughput_report.csv``
+- ``./reports_float16/summary/Llama-3.1-70B-Instruct_throughput_report.csv``
 
-- ``./reports_float8/summary/Meta-Llama-3.1-8B-Instruct-FP8-KV_throughput_report.csv``
+- ``./reports_float8/summary/Llama-3.1-70B-Instruct-FP8-KV_throughput_report.csv``
 
 .. raw:: html
 
@@ -394,17 +439,40 @@ Further reading
   MI300X accelerators, see :doc:`../../system-optimization/mi300x`.
 
 - To learn how to run LLM models from Hugging Face or your own model, see
-  :doc:`Using ROCm for AI <../index>`.
+  :doc:`Running models from Hugging Face <hugging-face-models>`.
 
 - To learn how to optimize inference on LLMs, see
   :doc:`Inference optimization <../inference-optimization/index>`.
 
-<<<<<<< HEAD:docs/how-to/performance-validation/mi300x/vllm-benchmark.rst
-=======
 - To learn how to fine-tune LLMs, see
   :doc:`Fine-tuning LLMs <../fine-tuning/index>`.
 
->>>>>>> develop:docs/how-to/rocm-for-ai/inference/vllm-benchmark.rst
-- To compare with the previous version of the ROCm vLLM Docker image for performance validation, refer to
-  `LLM inference performance validation on AMD Instinct MI300X (ROCm 6.2.0) <https://rocm.docs.amd.com/en/docs-6.2.0/how-to/performance-validation/mi300x/vllm-benchmark.html>`_.
+Previous versions
+=================
 
+This table lists previous versions of the ROCm vLLM Docker image for inference
+performance validation. For detailed information about available models for
+benchmarking, see the version-specific documentation.
+
+.. list-table::
+   :header-rows: 1
+   :stub-columns: 1
+
+   * - ROCm version
+     - vLLM version
+     - PyTorch version
+     - Resources
+
+   * - 6.2.1
+     - 0.6.4
+     - 2.5.0
+     - 
+       * `Documentation <https://rocm.docs.amd.com/en/docs-6.3.0/how-to/performance-validation/mi300x/vllm-benchmark.html>`_
+       * `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4/images/sha256-ccbb74cc9e7adecb8f7bdab9555f7ac6fc73adb580836c2a35ca96ff471890d8>`_
+
+   * - 6.2.0
+     - 0.4.3
+     - 2.4.0
+     -
+       * `Documentation <https://rocm.docs.amd.com/en/docs-6.2.0/how-to/performance-validation/mi300x/vllm-benchmark.html>`_
+       * `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.2_mi300_ubuntu22.04_py3.9_vllm_7c5fd50/images/sha256-9e4dd4788a794c3d346d7d0ba452ae5e92d39b8dfac438b2af8efdc7f15d22c0>`_

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

@@ -0,0 +1,547 @@
+: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
+
+******************************************
+Training a model with Megatron-LM for ROCm
+******************************************
+
+The Megatron-LM framework for ROCm 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 series accelerators, Megatron-LM delivers
+enhanced scalability, performance, and resource utilization for AI workloads.
+It is purpose-built to support models like Llama 2, Llama 3, Llama 3.1, and
+DeepSeek, enabling developers to train next-generation AI models more
+efficiently. See the GitHub repository at `<https://github.com/ROCm/Megatron-LM>`__.
+
+AMD provides a ready-to-use Docker image for MI300X accelerators containing
+essential components, including PyTorch, ROCm libraries, and Megatron-LM
+utilities. It contains the following software components to accelerate training
+workloads:
+
++--------------------------+--------------------------------+
+| Software component       | Version                        |
++==========================+================================+
+| ROCm                     | 6.3.0                          |
++--------------------------+--------------------------------+
+| PyTorch                  | 2.7.0a0+git637433              |
++--------------------------+--------------------------------+
+| Python                   | 3.10                           |
++--------------------------+--------------------------------+
+| Transformer Engine       | 1.11                           |
++--------------------------+--------------------------------+
+| Flash Attention          | 3.0.0                          |
++--------------------------+--------------------------------+
+| hipBLASLt                | git258a2162                    |
++--------------------------+--------------------------------+
+| Triton                   | 3.1                            |
++--------------------------+--------------------------------+
+
+Supported features and models
+=============================
+
+Megatron-LM provides the following key features to train large language models efficiently:
+
+- Transformer Engine (TE)
+
+- APEX
+
+- GEMM tuning
+
+- Torch.compile
+
+- 3D parallelism: TP + SP + CP
+
+- Distributed optimizer
+
+- Flash Attention (FA) 3
+
+- Fused kernels
+
+- Pre-training
+
+.. _amd-megatron-lm-model-support:
+
+The following models are pre-optimized for performance on the AMD Instinct MI300X accelerator.
+
+* Llama 2 7B
+
+* Llama 2 70B
+
+* Llama 3 8B
+
+* Llama 3 70B
+
+* Llama 3.1 8B
+
+* Llama 3.1 70B
+
+* DeepSeek-V2-Lite
+
+.. note::
+
+   Some models, such as Llama 3, require an external license agreement through
+   a third party (for example, Meta).
+
+System validation
+=================
+
+If you have already validated your system settings, skip this step. Otherwise,
+complete the :ref:`system validation and optimization steps <train-a-model-system-validation>`
+to set up your system before starting training.
+
+Disable NUMA auto-balancing
+---------------------------
+
+Generally, application performance can benefit from disabling NUMA auto-balancing. However,
+it might be detrimental to performance with certain types of workloads.
+
+Run the command ``cat /proc/sys/kernel/numa_balancing`` to check your current NUMA (Non-Uniform
+Memory Access) settings. Output ``0`` indicates this setting is disabled. If there is no output or
+the output is ``1``, run the following command to disable NUMA auto-balancing.
+
+.. code-block:: shell
+
+   sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
+
+See :ref:`mi300x-disable-numa` for more information.
+
+.. _mi300x-amd-megatron-lm-training:
+
+Environment setup
+=================
+
+The pre-built ROCm Megatron-LM environment allows users to quickly validate system performance, conduct
+training benchmarks, and achieve superior performance for models like Llama 3.1, Llama 2, and DeepSeek V2.
+
+Use the following instructions to set up the environment, configure the script to train models, and
+reproduce the benchmark results on the MI300X accelerators with the AMD Megatron-LM Docker
+image.
+
+.. _amd-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 rocm/megatron-lm:v25.3
+
+2. Launch 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 megatron_training_env rocm/megatron-lm:v25.3
+
+3. Use these commands if you exit the ``megatron_training_env`` container and need to return to it.
+
+   .. code-block:: shell
+
+      docker start megatron_training_env
+      docker exec -it megatron_training_env bash
+
+The Docker container includes a pre-installed, verified version of Megatron-LM from the `release branch <https://github.com/ROCm/Megatron-LM/tree/megatron_release_v25.3>`_.
+
+.. _amd-megatron-lm-environment-setup:
+
+Configuration scripts
+---------------------
+
+.. tab-set::
+
+   .. tab-item:: Llama
+      :sync: llama
+
+      If you're working with Llama 2 7B or Llama 2 70 B, use the ``train_llama2.sh`` configuration
+      script in the ``examples/llama`` directory of
+      `<https://github.com/ROCm/Megatron-LM/tree/megatron_release_v25.3/examples/llama>`__.
+      Likewise, if you're working with Llama 3 or Llama 3.1, then use ``train_llama3.sh`` and update
+      the configuration script accordingly.
+
+   .. tab-item:: DeepSeek V2
+      :sync: deepseek
+
+      Use the ``train_deepseek_v2.sh`` configuration script in the ``examples/deepseek_v2``
+      directory of
+      `<https://github.com/ROCm/Megatron-LM/tree/megatron_release_v25.3/examples/deepseek_v2>`__
+      and update the configuration script accordingly.
+
+Network interface
+^^^^^^^^^^^^^^^^^
+
+.. tab-set::
+
+   .. tab-item:: Llama
+      :sync: llama
+
+      To avoid connectivity issues in multi-node deployments, ensure the correct network interface
+      is set in your training scripts.
+
+      1. Run the following command (outside the container) to find the active network interface on your system.
+
+         .. code-block:: shell
+
+            ip a
+
+      2. Update the ``NCCL_SOCKET_IFNAME`` and ``GLOO_SOCKET_IFNAME`` variables with your system’s network interface. For
+         example:
+
+         .. code-block:: shell
+
+            export NCCL_SOCKET_IFNAME=ens50f0np0
+
+            export GLOO_SOCKET_IFNAME=ens50f0np0
+
+Dataset options
+^^^^^^^^^^^^^^^
+
+.. tab-set::
+
+   .. tab-item:: Llama
+      :sync: llama
+
+      You can use either mock data or real data for training.
+
+      * Mock data can be useful for testing and validation. Use the ``MOCK_DATA`` variable to toggle between mock and real data. The default
+        value is ``1`` for enabled.
+
+        .. code-block:: bash
+
+           MOCK_DATA=1
+
+      * If you're using a real dataset, update the ``DATA_PATH`` variable to point to the location of your dataset.
+
+        .. code-block:: bash
+
+           MOCK_DATA=0
+
+           DATA_PATH=${DATA_PATH:-"/data/bookcorpus_text_sentence"}  # Change to where your dataset is stored
+
+        Ensure that the files are accessible inside the Docker container.
+
+   .. tab-item:: DeepSeek V2
+      :sync: deepseek
+
+      If you don't already have the dataset, download the DeepSeek dataset using the following
+      commands:
+
+      .. code-block:: shell
+
+         mkdir deepseek-datasets
+         cd deepseek-datasets
+         wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/SlimPajama.json
+         wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-train.json
+         wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-valid.json
+         wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.bin
+         wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.idx
+
+      You can use either mock data or real data for training.
+
+      * Mock data can be useful for testing and validation. Use the ``MOCK_DATA`` variable to toggle between mock and real data. The default
+        value is ``1`` for enabled.
+
+        .. code-block:: bash
+
+           MOCK_DATA=1
+
+      * If you're using a real dataset, update the ``DATA_DIR`` variable to point to the location of your dataset.
+
+        .. code-block:: bash
+
+           MOCK_DATA=0
+
+           DATA_DIR="/root/data/deepseek-datasets"  # Change to where your dataset is stored
+
+        Ensure that the files are accessible inside the Docker container.
+
+Tokenizer
+^^^^^^^^^
+
+Tokenization is the process of converting raw text into tokens that can be processed by the model. For Llama
+models, this typically involves sub-word tokenization, where words are broken down into smaller units based on
+a fixed vocabulary. The tokenizer is trained along with the model on a large corpus of text, and it learns a
+fixed vocabulary that can represent a wide range of text from different domains. This allows Llama models to
+handle a variety of input sequences, including unseen words or domain-specific terms.
+
+.. tab-set::
+
+   .. 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 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.
+
+      For example, if you're using the Llama 3.1 8B model:
+
+      .. code-block:: shell
+
+         TOKENIZER_MODEL=meta-llama/Llama-3.1-8B
+
+   .. 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``.
+
+Multi-node training
+^^^^^^^^^^^^^^^^^^^
+
+.. tab-set::
+
+   .. tab-item:: Llama
+      :sync: llama
+
+      If you're running multi-node training, update the following environment variables. They can
+      also be passed as command line arguments.
+
+      * Change ``localhost`` to the master node's hostname:
+
+        .. code-block:: shell
+
+           MASTER_ADDR="${MASTER_ADDR:-localhost}"
+
+      * Set the number of nodes you want to train on (for instance, ``2``, ``4``, ``8``):
+
+        .. code-block:: shell
+
+           NNODES="${NNODES:-1}"
+
+      * Set the rank of each node (0 for master, 1 for the first worker node, and so on):
+
+        .. code-block:: shell
+
+           NODE_RANK="${NODE_RANK:-0}"
+
+      * Set ``DATA_CACHE_PATH`` to a common directory accessible by all the nodes (for example, an
+        NFS directory) for multi-node runs:
+
+        .. code-block:: shell
+
+           DATA_CACHE_PATH=/root/cache # Set to a common directory for multi-node runs
+
+      * For multi-node runs, make sure the 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 the Docker container.
+
+Start training on AMD Instinct accelerators
+===========================================
+
+The prebuilt Megatron-LM with ROCm training environment allows users to quickly validate
+system performance, conduct training benchmarks, and achieve superior
+performance for models like Llama 3.1 and Llama 2. This container should not be
+expected to provide generalized performance across all training workloads. You
+can expect the container to perform in the model configurations described in
+the following section, but other configurations are not validated by AMD.
+
+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 AMD Megatron-LM Docker image.
+
+.. tab-set::
+
+   .. tab-item:: Llama
+      :sync: llama
+
+      .. tab-set::
+
+         .. tab-item:: Single node training
+            :sync: single-node
+
+            To run training on a single node, navigate to the Megatron-LM folder and use the
+            following command:
+
+            .. code-block:: shell
+
+               TEE_OUTPUT=1 MBS=2 BS=128 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 bash examples/llama/train_llama3.sh
+
+         .. tab-item:: Multi-node training
+            :sync: multi-node
+
+            To run training on multiple nodes, launch the Docker container on each node. For example, for a two node setup (``NODE0`` as the master node), use these commands.
+
+            * On the master node ``NODE0``:
+
+              .. code-block:: shell
+
+                 TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=0 bash examples/llama/train_llama3.sh
+
+            * On the worker node ``NODE1``:
+
+              .. code-block:: shell
+
+                 TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=1 bash examples/llama/train_llama3.sh
+
+
+   .. tab-item:: DeepSeek V2
+      :sync: deepseek
+
+      To run the training on a single node, go to ``/Megatron-LM`` folder and use the following command:
+
+      .. code-block:: shell
+
+         cd /workspace/Megatron-LM
+         GEMM_TUNING=1 PR=bf16 MBS=4 AC=none bash examples/deepseek_v2/train_deepseekv2.sh
+
+Key options
+-----------
+
+.. _amd-megatron-lm-benchmark-test-vars:
+
+The benchmark tests support the following sets of variables:
+
+.. tab-set::
+
+   .. tab-item:: Llama
+      :sync: llama
+
+      ``TEE_OUTPUT``
+        ``1`` to enable training logs or ``0`` to disable.
+
+      ``TE_FP8``
+        ``0`` for BP16 (default) or ``1`` for FP8 GEMMs.
+
+      ``GEMM_TUNING``
+        ``1`` to enable GEMM tuning, which boosts performance by using the best GEMM kernels.
+
+      ``USE_FLASH_ATTN``
+        ``1`` to enable Flash Attention.
+
+      ``ENABLE_PROFILING``
+        ``1`` to enable PyTorch profiling for performance analysis.
+
+      ``transformer-impl``
+        ``transformer_engine`` to use the Transformer Engine (TE) or ``local`` to disable TE.
+
+      ``MODEL_SIZE``
+        ``8B`` or ``70B`` for Llama 3 and 3.1. ``7B`` or ``70B`` for Llama 2.
+
+      ``TOTAL_ITERS``
+        The total number of iterations -- ``10`` by default.
+
+      ``MOCK_DATA``
+        ``1`` to use mock data or ``0`` to use real data provided by you.
+
+      ``MBS``
+        Micro batch size.
+
+      ``BS``
+        Global batch size.
+
+      ``TP``
+        Tensor parallel (``1``, ``2``, ``4``, ``8``).
+
+      ``SEQ_LENGTH``
+        Input sequence length.
+
+   .. tab-item:: DeepSeek V2
+      :sync: deepseek
+
+      ``PR``
+        Precision for training. ``bf16`` for BF16 (default) or ``fp8`` for FP8 GEMMs.
+
+      ``GEMM_TUNING``
+        ``1`` to enable GEMM tuning, which boosts performance by using the best GEMM kernels.
+
+      ``TOTAL_ITERS``
+        The total number of iterations -- ``10`` by default.
+
+      ``MOCK_DATA``
+        ``1`` to use mock data or ``0`` to use real data provided by you.
+
+      ``MBS``
+        Micro batch size.
+
+      ``GBS``
+        Global batch size.
+
+Benchmarking examples
+---------------------
+
+.. tab-set::
+
+   .. tab-item:: Llama
+      :sync: llama
+
+      .. tab-set::
+
+         .. tab-item:: Single node training
+            :sync: single-node
+
+            Use this command to run training with Llama 2 7B model on a single node. You can specify MBS, BS, FP,
+            datatype, and so on.
+
+            .. code-block:: bash
+
+               TEE_OUTPUT=1 MBS=5 BS=120 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
+               SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
+
+            You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
+
+            See the sample output:
+
+            .. image:: ../../../../data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
+               :width: 800
+
+         .. tab-item:: Multi-node training
+            :sync: multi-node
+
+            Launch the Docker container on each node.
+
+            In this example, run training with Llama 2 7B model on 2 nodes with specific MBS, BS, FP, datatype, and
+            so on.
+
+            On the master node:
+
+            .. code-block:: bash
+
+               TEE_OUTPUT=1 MBS=4 BS=64 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
+               SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
+
+            On the worker node:
+
+            .. code-block:: bash
+
+               TEE_OUTPUT=1 MBS=4 BS=64 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
+               SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
+
+            You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
+
+            Sample output for 2-node training:
+
+            Master node:
+
+            .. image:: ../../../../data/how-to/rocm-for-ai/2-node-training-master.png
+               :width: 800
+
+            Worker node:
+
+            .. image:: ../../../../data/how-to/rocm-for-ai/2-node-training-worker.png
+               :width: 800
+
+Previous versions
+=================
+
+This table lists previous versions of the ROCm Megatron-LM Docker image for training
+performance validation. For detailed information about available models for
+benchmarking, see the version-specific documentation.
+
+.. list-table::
+   :header-rows: 1
+   :stub-columns: 1
+
+   * - ROCm version
+     - Megatron-LM version
+     - PyTorch version
+     - Resources
+
+   * - 6.1
+     - 24.12-dev
+     - 2.4.0
+     - 
+       * `Documentation <https://rocm.docs.amd.com/en/docs-6.3.0/how-to/rocm-for-ai/train-a-model.html>`_
+       * `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/24.12-dev/images/sha256-5818c50334ce3d69deeeb8f589d83ec29003817da34158ebc9e2d112b929bf2e>`_

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

@@ -0,0 +1,341 @@
+: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
+**************************************
+
+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 (``rocm/pytorch-training:v25.3``) 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                     | 6.3.0                          |
++--------------------------+--------------------------------+
+| PyTorch                  | 2.7.0a0+git637433              |
++--------------------------+--------------------------------+
+| Python                   | 3.10                           |
++--------------------------+--------------------------------+
+| Transformer Engine       | 1.11                           |
++--------------------------+--------------------------------+
+| Flash Attention          | 3.0.0                          |
++--------------------------+--------------------------------+
+| hipBLASLt                | git258a2162                    |
++--------------------------+--------------------------------+
+| Triton                   | 3.1                            |
++--------------------------+--------------------------------+
+
+.. _amd-pytorch-training-model-support:
+
+Supported models
+================
+
+The following models are pre-optimized for performance on the AMD Instinct MI300X accelerator.
+
+* Llama 3.1 8B
+
+* Llama 3.1 70B
+
+* FLUX.1-dev
+
+.. note::
+
+   Only these models are supported in the following steps.
+
+   Some models, such as Llama 3, require an external license agreement through
+   a third party (for example, Meta).
+
+System validation
+=================
+
+If you have already validated your system settings, skip this step. Otherwise,
+complete the :ref:`system validation and optimization steps <train-a-model-system-validation>`
+to set up your system before starting training.
+
+Disable NUMA auto-balancing
+---------------------------
+
+Generally, application performance can benefit from disabling NUMA auto-balancing. However,
+it might be detrimental to performance with certain types of workloads.
+
+Run the command ``cat /proc/sys/kernel/numa_balancing`` to check your current NUMA (Non-Uniform
+Memory Access) settings. Output ``0`` indicates this setting is disabled. If there is no output or
+the output is ``1``, run the following command to disable NUMA auto-balancing.
+
+.. code-block:: shell
+
+   sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
+
+See :ref:`mi300x-disable-numa` for more information.
+
+Environment setup
+=================
+
+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.
+
+Download the Docker image
+-------------------------
+
+1. Use the following command to pull the Docker image from Docker Hub.
+
+   .. code-block:: shell
+
+      docker pull rocm/pytorch-training:v25.3
+
+2. 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 rocm/pytorch-training:v25.3
+
+3. 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
+
+4. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__ repository and navigate to the benchmark scripts directory.
+
+   .. code-block:: shell
+
+      git clone https://github.com/ROCm/MAD
+      cd MAD/scripts/pytorch-train
+
+Prepare training datasets and dependencies
+------------------------------------------
+
+The following benchmarking examples may require downloading models and datasets
+from Hugging Face. To ensure successful access to gated repos, set your
+``HF_TOKEN``.
+
+Run the setup script to install libraries and datasets needed for benchmarking.
+
+.. code-block:: shell
+
+   ./pytorch_benchmark_setup.sh
+
+``pytorch_benchmark_setup.sh`` installs the following libraries:
+
+.. list-table::
+   :header-rows: 1
+
+   * - Library
+     - Benchmark model
+     - Reference
+
+   * - ``accelerate``
+     - Llama 3.1 8B, FLUX
+     - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
+
+   * - ``datasets``
+     - Llama 3.1 8B, 70B, FLUX
+     - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+
+   * - ``torchdata``
+     - Llama 3.1 70B
+     - `TorchData <https://pytorch.org/data/beta/index.html>`_
+
+   * - ``tomli``
+     - Llama 3.1 70B
+     - `Tomli <https://pypi.org/project/tomli/>`_
+
+   * - ``tiktoken``
+     - Llama 3.1 70B
+     - `tiktoken <https://github.com/openai/tiktoken>`_
+
+   * - ``blobfile``
+     - Llama 3.1 70B
+     - `blobfile <https://pypi.org/project/blobfile/>`_
+
+   * - ``tabulate``
+     - Llama 3.1 70B
+     - `tabulate <https://pypi.org/project/tabulate/>`_
+
+   * - ``wandb``
+     - Llama 3.1 70B
+     - `Weights & Biases <https://github.com/wandb/wandb>`_
+
+   * - ``sentencepiece``
+     - Llama 3.1 70B, FLUX
+     - `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
+
+   * - ``tensorboard``
+     - Llama 3.1 70 B, FLUX
+     - `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
+
+   * - ``csvkit``
+     - FLUX
+     - `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
+
+   * - ``deepspeed``
+     - FLUX
+     - `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
+
+   * - ``diffusers``
+     - FLUX
+     - `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`_ 0.31.0
+
+   * - ``GitPython``
+     - FLUX
+     - `GitPython <https://github.com/gitpython-developers/GitPython>`_ 3.1.44
+
+   * - ``opencv-python-headless``
+     - FLUX
+     - `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`_ 4.10.0.84
+
+   * - ``peft``
+     - FLUX
+     - `PEFT <https://huggingface.co/docs/peft/en/index>`_ 0.14.0
+
+   * - ``protobuf``
+     - FLUX
+     - `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
+
+   * - ``pytest``
+     - FLUX
+     - `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
+
+   * - ``python-dotenv``
+     - FLUX
+     - `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
+
+   * - ``seaborn``
+     - FLUX
+     - `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
+
+   * - ``transformers``
+     - FLUX
+     - `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
+
+``pytorch_benchmark_setup.sh`` downloads the following models from Hugging Face:
+
+* `meta-llama/Llama-3.1-70B-Instruct <https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct>`_
+
+* `black-forest-labs/FLUX.1-dev <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
+
+Along with the following datasets:
+
+* `WikiText <https://huggingface.co/datasets/Salesforce/wikitext>`_
+
+* `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
+
+Start training on AMD Instinct accelerators
+===========================================
+
+The prebuilt PyTorch with ROCm training environment allows users to quickly validate
+system performance, conduct training benchmarks, and achieve superior
+performance for models like Llama 3.1 and Llama 2. This container should not be
+expected to provide generalized performance across all training workloads. You
+can expect the container to perform in the model configurations described in
+the following section, but other configurations are not validated by AMD.
+
+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 AMD PyTorch training Docker image.
+
+Once your environment is set up, use the following commands and examples to start benchmarking.
+
+Pretraining
+-----------
+
+To start the pretraining 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_repo -p $datatype -s $sequence_length
+
+Options and available models
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. list-table::
+   :header-rows: 1
+
+   * - Name
+     - Options
+     - Description
+
+   * - ``$training_mode``
+     - ``pretrain``
+     - Benchmark pretraining
+
+   * -
+     - ``finetune_fw``
+     - Benchmark full weight fine-tuning (Llama 3.1 70B with BF16)
+
+   * -
+     - ``finetune_lora``
+     - Benchmark LoRA fine-tuning (Llama 3.1 70B with BF16)
+
+   * - ``$datatype``
+     - FP8 or BF16
+     - Only Llama 3.1 8B supports FP8 precision.
+
+   * - ``$model_repo``
+     - Llama-3.1-8B
+     - `Llama 3.1 8B <https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct>`_
+
+   * - 
+     - Llama-3.1-70B
+     - `Llama 3.1 70B <https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct>`_
+
+   * - 
+     - Flux
+     - `FLUX.1 [dev] <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
+
+Fine-tuning
+-----------
+
+To start the fine-tuning benchmark, use the following command. It will run the benchmarking example of Llama 2 70B
+with the WikiText dataset using the AMD fork of `torchtune <https://github.com/AMD-AIG-AIMA/torchtune>`_.
+
+.. code-block:: shell
+
+   ./pytorch_benchmark_report.sh -t {finetune_fw, finetune_lora} -p BF16 -m Llama-3.1-70B
+
+Benchmarking examples
+---------------------
+
+Here are some examples of how to use the command.
+
+* Example 1: Llama 3.1 70B with BF16 precision with `torchtitan <https://github.com/ROCm/torchtitan>`_.
+
+  .. code-block:: shell
+
+     ./pytorch_benchmark_report.sh -t pretrain -p BF16 -m Llama-3.1-70B -s 8192
+
+* Example 2: Llama 3.1 8B with FP8 precision using Transformer Engine (TE) and Hugging Face Accelerator.
+
+  .. code-block:: shell
+
+     ./pytorch_benchmark_report.sh -t pretrain -p FP8 -m Llama-3.1-70B -s 8192
+
+* Example 3: FLUX.1-dev with BF16 precision with FluxBenchmark.
+
+  .. code-block:: shell
+
+     ./pytorch_benchmark_report.sh -t pretrain -p BF16 -m Flux
+
+* Example 4: Torchtune full weight fine-tuning with Llama 3.1 70B
+
+  .. code-block:: shell
+
+     ./pytorch_benchmark_report.sh -t finetune_fw -p BF16 -m Llama-3.1-70B
+
+* Example 5: Torchtune LoRA fine-tuning with Llama 3.1 70B
+
+  .. code-block:: shell
+
+     ./pytorch_benchmark_report.sh -t finetune_lora -p BF16 -m Llama-3.1-70B

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

@@ -14,8 +14,15 @@ Training models on AMD GPUs with the ROCm™ software platform allows you to use
  
 The ROCm software platform makes it easier to train models on AMD GPUs while maintaining compatibility with existing code and tools. The platform also provides features like multi-GPU support, allowing for scaling and parallelization of model training across multiple GPUs to enhance performance. 
 
+The AI Developer Hub contains `AMD ROCm tutorials <https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/>`_ for
+training, fine-tuning, and inference. It leverages popular machine learning frameworks on AMD GPUs.
+
 In this guide, you'll learn about:
 
-- :doc:`Training a model <train-a-model>`
+- Training a model
 
-- :doc:`Scale model training <scale-model-training>`
+  - :doc:`Train a model with Megatron-LM <benchmark-docker/megatron-lm>`
+
+  - :doc:`Train a model with PyTorch <benchmark-docker/pytorch-training>`
+
+- :doc:`Scaling model training <scale-model-training>`

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

@@ -0,0 +1,130 @@
+:orphan:
+
+.. meta::
+   :description: Prerequisite system validation before using ROCm for AI.
+   :keywords: ROCm, AI, LLM, train, megatron, Llama, tutorial, docker, torch, pytorch, jax
+
+.. _train-a-model-system-validation:
+
+**********************************************
+Prerequisite system validation before training
+**********************************************
+
+Complete the following system validation and optimization steps to set up your system before starting training.
+
+Disable NUMA auto-balancing
+---------------------------
+
+Generally, application performance can benefit from disabling NUMA auto-balancing. However,
+it might be detrimental to performance with certain types of workloads.
+
+Run the command ``cat /proc/sys/kernel/numa_balancing`` to check your current NUMA (Non-Uniform
+Memory Access) settings. Output ``0`` indicates this setting is disabled. If there is no output or
+the output is ``1``, run the following command to disable NUMA auto-balancing.
+
+.. code-block:: shell
+
+   sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
+
+See :ref:`mi300x-disable-numa` for more information.
+
+Hardware verification with ROCm
+-------------------------------
+
+Use the command ``rocm-smi --setperfdeterminism 1900`` to set the max clock speed up to 1900 MHz
+instead of the default 2100 MHz. This can reduce the chance of a PCC event lowering the attainable
+GPU clocks. This setting will not be required for new IFWI releases with the production PRC feature.
+You can restore this setting to its default value with the ``rocm-smi -r`` command.
+
+Run the command:
+
+.. code-block:: shell
+
+   rocm-smi --setperfdeterminism 1900
+
+See :ref:`mi300x-hardware-verification-with-rocm` for more information.
+
+RCCL Bandwidth Test for multi-node setups
+-----------------------------------------
+
+ROCm Collective Communications Library (RCCL) is a standalone library of standard collective communication
+routines for GPUs. See the :doc:`RCCL documentation <rccl:index>` for more information. Before starting
+pretraining, running a RCCL bandwidth test helps ensure that the multi-GPU or multi-node setup is optimized
+for efficient distributed training.
+
+Running the RCCL bandwidth test helps verify that:
+
+- The GPUs can communicate across nodes or within a single node.
+
+- The interconnect (such as InfiniBand, Ethernet, or Infinite fabric) is functioning as expected and
+  provides adequate bandwidth for communication.
+
+- No hardware setup or cabling issues could affect the communication between GPUs
+
+Tuning and optimizing hyperparameters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In distributed training, specific hyperparameters related to distributed communication can be tuned based on
+the results of the RCCL bandwidth test. These variables are already set in the Docker image:
+
+.. code-block:: shell
+
+   # force all RCCL streams to be high priority
+   export TORCH_NCCL_HIGH_PRIORITY=1
+
+   # specify which RDMA interfaces to use for communication
+   export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
+
+   # define the Global ID index used in RoCE mode
+   export NCCL_IB_GID_INDEX=3
+
+   # avoid data corruption/mismatch issue that existed in past releases
+   export RCCL_MSCCL_ENABLE=0
+
+Running the RCCL Bandwidth Test
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+It's recommended you run the RCCL bandwidth test before launching training. It ensures system
+performance is sufficient to launch training. RCCL is not included in the AMD Megatron-LM Docker
+image; follow the instructions in `<https://github.com/ROCm/rccl-tests>`__ to get started.
+See :ref:`mi300x-rccl` for more information.
+
+Run on 8 GPUs (``-g 8``), scanning from 8 bytes to 10 GB:
+
+.. code-block:: shell
+
+   ./build/all_reduce_perf -b 8 -e 10G -f 2 -g 8
+
+.. image:: ../../../data/how-to/rocm-for-ai/rccl-tests-8-gpu.png
+   :width: 800
+
+Using one MPI process per GPU and ``-g 1`` for performance-oriented runs on both single-node and multi-node is
+recommended. So, a run on 8 GPUs looks something like:
+
+.. code-block:: shell
+
+   mpirun -np 8 --bind-to numa ./build/all_reduce_perf -b 8 -e 10G -f 2 -g 1
+
+.. image:: ../../../data/how-to/rocm-for-ai/rccl-tests-1-mpi-process-per-gpu.png
+   :width: 800
+
+Running with one MPI process per GPU ensures a one-to-one mapping for CPUs and GPUs, which can be beneficial
+for smaller message sizes. This better represents the real-world use of RCCL in deep learning frameworks like
+PyTorch and TensorFlow.
+
+Use the following script to run the RCCL test for four MI300X GPU nodes. Modify paths and node addresses as needed.
+
+.. code-block::
+
+   /home/$USER/ompi_for_gpu/ompi/bin/mpirun -np 32 -H tw022:8,tw024:8,tw010:8, tw015:8 \
+   --mca pml ucx \
+   --mca btl ^openib \
+   -x NCCL_SOCKET_IFNAME=ens50f0np0 \
+   -x NCCL_IB_HCA=rdma0:1,rdma1:1,rdma2:1,rdma3:1,rdma4:1,rdma5:1,rdma6:1,rdma7:1 \
+   -x NCCL_IB_GID_INDEX=3 \
+   -x NCCL_MIN_NCHANNELS=40 \
+   -x NCCL_DEBUG=version \
+   $HOME/rccl-tests/build/all_reduce_perf -b 8 -e 8g -f 2 -g 1
+
+.. image:: ../../../data/how-to/rocm-for-ai/rccl-tests-4-mi300x-gpu-nodes.png
+   :width: 800

docs/how-to/rocm-for-ai/training/train-a-model.rst

@@ -1,503 +0,0 @@
-.. meta::
-   :description: How to train a model using ROCm Megatron-LM
-   :keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
-
-**************************************
-Training a model with ROCm Megatron-LM
-**************************************
-
-.. _amd-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>`
-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>`__.
-
-For ease of use, AMD provides a ready-to-use Docker image for MI300X accelerators containing essential
-components, including PyTorch, PyTorch Lightning, ROCm libraries, and Megatron-LM utilities. It contains the
-following software to accelerate training workloads:
-
-+--------------------------+--------------------------------+
-| Software component       | Version                        |
-+==========================+================================+
-| ROCm                     | 6.1                            |
-+--------------------------+--------------------------------+
-| PyTorch                  | 2.4.0                          |
-+--------------------------+--------------------------------+
-| PyTorch Lightning        | 2.4.0                          |
-+--------------------------+--------------------------------+
-| Megatron Core            | 0.9.0                          |
-+--------------------------+--------------------------------+
-| Transformer Engine       | 1.5.0                          |
-+--------------------------+--------------------------------+
-| Flash Attention          | v2.6                           |
-+--------------------------+--------------------------------+
-| Transformers             | 4.44.0                         |
-+--------------------------+--------------------------------+
-
-Supported features and models
-=============================
-
-Megatron-LM provides the following key features to train large language models efficiently:
-
-- Transformer Engine (TE)
-
-- APEX
-
-- GEMM tuning
-
-- Torch.compile
-
-- 3D parallelism: TP + SP + CP
-
-- Distributed optimizer
-
-- Flash Attention (FA) 2
-
-- Fused kernels
-
-- Pre-training
-
-.. _amd-megatron-lm-model-support:
-
-The following models are pre-optimized for performance on the AMD Instinct MI300X accelerator.
-
-* Llama 2 7B
-
-* Llama 2 70B
-
-* Llama 3 8B
-
-* Llama 3 70B
-
-* Llama 3.1 8B
-
-* Llama 3.1 70B
-
-Prerequisite system validation steps
-====================================
-
-Complete the following system validation and optimization steps to set up your system before starting training.
-
-Disable NUMA auto-balancing
----------------------------
-
-Generally, application performance can benefit from disabling NUMA auto-balancing. However,
-it might be detrimental to performance with certain types of workloads.
-
-Run the command ``cat /proc/sys/kernel/numa_balancing`` to check your current NUMA (Non-Uniform
-Memory Access) settings. Output ``0`` indicates this setting is disabled. If there is no output or
-the output is ``1``, run the following command to disable NUMA auto-balancing.
-
-.. code-block:: shell
-
-   sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
-
-See :ref:`mi300x-disable-numa` for more information.
-
-Hardware verification with ROCm
--------------------------------
-
-Use the command ``rocm-smi --setperfdeterminism 1900`` to set the max clock speed up to 1900 MHz
-instead of the default 2100 MHz. This can reduce the chance of a PCC event lowering the attainable
-GPU clocks. This setting will not be required for new IFWI releases with the production PRC feature.
-You can restore this setting to its default value with the ``rocm-smi -r`` command.
-
-Run the command:
-
-.. code-block:: shell
-
-   rocm-smi --setperfdeterminism 1900
-
-See :ref:`mi300x-hardware-verification-with-rocm` for more information.
-
-RCCL Bandwidth Test
--------------------
-
-ROCm Collective Communications Library (RCCL) is a standalone library of standard collective communication
-routines for GPUs. See the :doc:`RCCL documentation <rccl:index>` for more information. Before starting
-pre-training, running a RCCL bandwidth test helps ensure that the multi-GPU or multi-node setup is optimized
-for efficient distributed training.
-
-Running the RCCL bandwidth test helps verify that:
-
-- The GPUs can communicate across nodes or within a single node.
-
-- The interconnect (such as InfiniBand, Ethernet, or Infinite fabric) is functioning as expected and
-  provides adequate bandwidth for communication.
-
-- No hardware setup or cabling issues could affect the communication between GPUs
-
-Tuning and optimizing hyperparameters
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In distributed training, specific hyperparameters related to distributed communication can be tuned based on
-the results of the RCCL bandwidth test. These variables are already set in the Docker image:
-
-.. code-block:: shell
-
-   # force all RCCL streams to be high priority
-   export TORCH_NCCL_HIGH_PRIORITY=1
-
-   # specify which RDMA interfaces to use for communication
-   export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
-
-   # define the Global ID index used in RoCE mode
-   export NCCL_IB_GID_INDEX=3
-
-   # avoid data corruption/mismatch issue that existed in past releases
-   export RCCL_MSCCL_ENABLE=0
-
-Running the RCCL Bandwidth Test
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-It's recommended you run the RCCL bandwidth test before launching training. It ensures system
-performance is sufficient to launch training. RCCL is not included in the AMD Megatron-LM Docker
-image; follow the instructions in `<https://github.com/ROCm/rccl-tests>`__ to get started.
-See :ref:`mi300x-rccl` for more information.
-
-Run on 8 GPUs (``-g 8``), scanning from 8 bytes to 10 GB:
-
-.. code-block:: shell
-
-   ./build/all_reduce_perf -b 8 -e 10G -f 2 -g 8
-
-.. image:: ../../../data/how-to/rocm-for-ai/rccl-tests-8-gpu.png
-   :width: 800
-
-Using one MPI process per GPU and ``-g 1`` for performance-oriented runs on both single-node and multi-node is
-recommended. So, a run on 8 GPUs looks something like:
-
-.. code-block:: shell
-
-   mpirun -np 8 --bind-to numa ./build/all_reduce_perf -b 8 -e 10G -f 2 -g 1
-
-.. image:: ../../../data/how-to/rocm-for-ai/rccl-tests-1-mpi-process-per-gpu.png
-   :width: 800
-
-Running with one MPI process per GPU ensures a one-to-one mapping for CPUs and GPUs, which can be beneficial
-for smaller message sizes. This better represents the real-world use of RCCL in deep learning frameworks like
-PyTorch and TensorFlow.
-
-Use the following script to run the RCCL test for four MI300X GPU nodes. Modify paths and node addresses as needed.
-
-.. code-block::
-
-   /home/$USER/ompi_for_gpu/ompi/bin/mpirun -np 32 -H tw022:8,tw024:8,tw010:8, tw015:8 \
-   --mca pml ucx \
-   --mca btl ^openib \
-   -x NCCL_SOCKET_IFNAME=ens50f0np0 \
-   -x NCCL_IB_HCA=rdma0:1,rdma1:1,rdma2:1,rdma3:1,rdma4:1,rdma5:1,rdma6:1,rdma7:1 \
-   -x NCCL_IB_GID_INDEX=3 \
-   -x NCCL_MIN_NCHANNELS=40 \
-   -x NCCL_DEBUG=version \
-   $HOME/rccl-tests/build/all_reduce_perf -b 8 -e 8g -f 2 -g 1
-
-.. image:: ../../../data/how-to/rocm-for-ai/rccl-tests-4-mi300x-gpu-nodes.png
-   :width: 800
-
-.. _mi300x-amd-megatron-lm-training:
-
-Start training on MI300X accelerators
-=====================================
-
-The pre-built ROCm Megatron-LM environment allows users to quickly validate system performance, conduct
-training benchmarks, and achieve superior performance for models like Llama 2 and Llama 3.1.
-
-Use the following instructions to set up the environment, configure the script to train models, and
-reproduce the benchmark results on the MI300X accelerators with the AMD Megatron-LM Docker
-image.
-
-.. _amd-megatron-lm-requirements:
-
-Download the Docker image and required packages
------------------------------------------------
-
-1. Use the following command to pull the Docker image from Docker Hub.
-
-   .. code-block:: shell
-
-      docker pull rocm/megatron-lm:24.12-dev
-
-2. Launch 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 $CACHE_DIR:/root/.cache --name megatron-dev-env rocm/megatron-lm:24.12-dev /bin/bash
-
-3. Clone the ROCm Megatron-LM repository to a local directory and install the required packages on the host machine.
-
-   .. code-block:: shell
-
-      git clone https://github.com/ROCm/Megatron-LM
-      cd Megatron-LM
-
-   .. note::
-
-      This release is validated with ``ROCm/Megatron-LM`` commit `bb93ccb <https://github.com/ROCm/Megatron-LM/tree/bb93ccbfeae6363c67b361a97a27c74ab86e7e92>`_.
-      Checking out this specific commit is recommended for a stable and reproducible environment.
-
-      .. code-block:: shell
-         
-         git checkout bb93ccbfeae6363c67b361a97a27c74ab86e7e92
-
-Prepare training datasets
--------------------------
-
-If you already have the preprocessed data, you can skip this section.
-
-Use the following command to process datasets. We use GPT data as an example. You may change the merge table, use an
-end-of-document token, remove sentence splitting, and use the tokenizer type.
-
-.. code-block:: shell
-
-   python tools/preprocess_data.py \
-       --input my-corpus.json \
-       --output-prefix my-gpt2 \
-       --vocab-file gpt2-vocab.json \
-       --tokenizer-type GPT2BPETokenizer \
-       --merge-file gpt2-merges.txt \
-       --append-eod
-
-In this case, the automatically generated output files are named ``my-gpt2_text_document.bin`` and
-``my-gpt2_text_document.idx``.
-
-.. image:: ../../../data/how-to/rocm-for-ai/prep-training-datasets-my-gpt2-text-document.png
-   :width: 800
-
-.. _amd-megatron-lm-environment-setup:
-
-Environment setup
------------------
-
-In the ``examples/llama`` directory of Megatron-LM, if you're working with Llama 2 7B or Llama 2 70 B, use the
-``train_llama2.sh`` configuration script. Likewise, if you're working with Llama 3 or Llama 3.1, then use
-``train_llama3.sh`` and update the configuration script accordingly.
-
-Network interface
-^^^^^^^^^^^^^^^^^
-
-To avoid connectivity issues, ensure the correct network interface is set in your training scripts.
-
-1. Run the following command to find the active network interface on your system.
-
-   .. code-block:: shell
-
-      ip a
-
-2. Update the ``NCCL_SOCKET_IFNAME`` and ``GLOO_SOCKET_IFNAME`` variables with your system’s network interface. For
-   example:
-
-   .. code-block:: shell
-
-      export NCCL_SOCKET_IFNAME=ens50f0np0
-
-      export GLOO_SOCKET_IFNAME=ens50f0np0
-
-Dataset options
-^^^^^^^^^^^^^^^
-
-You can use either mock data or real data for training.
-
-* If you're using a real dataset, update the ``DATA_PATH`` variable to point to the location of your dataset.
-
-  .. code-block:: shell
-
-     DATA_DIR="/root/.cache/data" # Change to where your dataset is stored
-
-     DATA_PATH=${DATA_DIR}/bookcorpus_text_sentence
-
-  .. code-block:: shell
-
-     --data-path $DATA_PATH
-
-  Ensure that the files are accessible inside the Docker container.
-
-* Mock data can be useful for testing and validation. If you're using mock data, replace ``--data-path $DATA_PATH`` with the ``--mock-data`` option.
-
-  .. code-block:: shell
-
-     --mock-data
-
-Tokenizer
-^^^^^^^^^
-
-Tokenization is the process of converting raw text into tokens that can be processed by the model. For Llama
-models, this typically involves sub-word tokenization, where words are broken down into smaller units based on
-a fixed vocabulary. The tokenizer is trained along with the model on a large corpus of text, and it learns a
-fixed vocabulary that can represent a wide range of text from different domains. This allows Llama models to
-handle a variety of input sequences, including unseen words or domain-specific terms.
-
-To train any of the Llama 2 models that this Docker image supports, use the ``Llama2Tokenizer``.
-
-To train any of Llama 3 and Llama 3.1 models that this Docker image supports, use the ``HuggingFaceTokenizer``.
-Set the Hugging Face model link in the ``TOKENIZER_MODEL`` variable.
-
-For example, if you're using the Llama 3.1 8B model:
-
-.. code-block:: shell
-
-   TOKENIZER_MODEL=meta-llama/Llama-3.1-8B
-
-Run benchmark tests
--------------------
-
-.. note::
-
-   If you're running **multi node training**, update the following environment variables. They can
-   also be passed as command line arguments.
-
-   * Change ``localhost`` to the master node's hostname:
-
-     .. code-block:: shell
-
-        MASTER_ADDR="${MASTER_ADDR:-localhost}"
-
-   * Set the number of nodes you want to train on (for instance, ``2``, ``4``, ``8``):
-
-     .. code-block:: shell
-
-        NNODES="${NNODES:-1}"
-
-   * Set the rank of each node (0 for master, 1 for the first worker node, and so on):
-
-     .. code-block:: shell
-
-        NODE_RANK="${NODE_RANK:-0}"
-
-* Use this command to run a performance benchmark test of any of the Llama 2 models that this Docker image supports (see :ref:`variables <amd-megatron-lm-benchmark-test-vars>`).
-
-  .. code-block:: shell
-
-     {variables} bash examples/llama/train_llama2.sh
-
-* Use this command to run a performance benchmark test of any of the Llama 3 and Llama 3.1 models that this Docker image supports (see :ref:`variables <amd-megatron-lm-benchmark-test-vars>`).
-
-  .. code-block:: shell
-
-     {variables} bash examples/llama/train_llama3.sh
-
-.. _amd-megatron-lm-benchmark-test-vars:
-
-The benchmark tests support the same set of variables:
-
-+--------------------------+-----------------------+-----------------------+
-| Name                     | Options               | Description           |
-+==========================+=======================+=======================+
-| ``TEE_OUTPUT``           | 0 or 1                | 0: disable training   |
-|                          |                       | log                   |
-|                          |                       |                       |
-|                          |                       | 1: enable training    |
-|                          |                       | log                   |
-+--------------------------+-----------------------+-----------------------+
-| ``MBS``                  |                       | Micro batch size      |
-+--------------------------+-----------------------+-----------------------+
-| ``BS``                   |                       | Batch size            |
-+--------------------------+-----------------------+-----------------------+
-| ``TP``                   | 1, 2, 4, 8            | Tensor parallel       |
-+--------------------------+-----------------------+-----------------------+
-| ``TE_FP8``               | 0 or 1                | Datatype.             |
-|                          |                       | If it is set to 1,    |
-|                          |                       | FP8.                  |
-|                          |                       |                       |
-|                          |                       | If it is set to 0.    |
-|                          |                       | BP16                  |
-+--------------------------+-----------------------+-----------------------+
-| ``NO_TORCH_COMPILE``     | 0 or 1                | If it is set to 1,    |
-|                          |                       | enable torch.compile. |
-|                          |                       |                       |
-|                          |                       | If it is set to 0.    |
-|                          |                       | Disable torch.compile |
-|                          |                       | (default)             |
-+--------------------------+-----------------------+-----------------------+
-| ``SEQ_LENGTH``           |                       | Input sequence length |
-+--------------------------+-----------------------+-----------------------+
-| ``GEMM_TUNING``          | 0 or 1                | If it is set to 1,    |
-|                          |                       | enable gemm tuning.   |
-|                          |                       |                       |
-|                          |                       | If it is set to 0,    |
-|                          |                       | disable gemm tuning   |
-+--------------------------+-----------------------+-----------------------+
-| ``USE_FLASH_ATTN``       | 0 or 1                | 0: disable flash      |
-|                          |                       | attention             |
-|                          |                       |                       |
-|                          |                       | 1: enable flash       |
-|                          |                       | attention             |
-+--------------------------+-----------------------+-----------------------+
-| ``ENABLE_PROFILING``     | 0 or 1                | 0: disable torch      |
-|                          |                       | profiling             |
-|                          |                       |                       |
-|                          |                       | 1: enable torch       |
-|                          |                       | profiling             |
-+--------------------------+-----------------------+-----------------------+
-| ``MODEL_SIZE``           |                       | The size of the mode: |
-|                          |                       | 7B/70B, etc.          |
-+--------------------------+-----------------------+-----------------------+
-| ``TOTAL_ITERS``          |                       | Total number of       |
-|                          |                       | iterations            |
-+--------------------------+-----------------------+-----------------------+
-| ``transformer-impl``     | transformer_engine or | Enable transformer    |
-|                          | local                 | engine by default     |
-+--------------------------+-----------------------+-----------------------+
-
-Benchmarking examples
-^^^^^^^^^^^^^^^^^^^^^
-
-.. tab-set::
-
-   .. tab-item:: Single node training
-      :sync: single
-
-      Use this command to run training with Llama 2 7B model on a single node. You can specify MBS, BS, FP,
-      datatype, and so on.
-
-      .. code-block:: bash
-
-         TEE_OUTPUT=1 MBS=5 BS=120 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
-         SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
-
-      You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
-
-      See the sample output:
-
-      .. image:: ../../../data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
-         :width: 800
-
-   .. tab-item:: Multi node training
-      :sync: multi
-
-      Launch the Docker container on each node.
-
-      In this example, run training with Llama 2 7B model on 2 nodes with specific MBS, BS, FP, datatype, and
-      so on.
-
-      On the master node:
-
-      .. code-block:: bash
-
-         TEE_OUTPUT=1 MBS=4 BS=64 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
-         SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
-
-      On the worker node:
-
-      .. code-block:: bash
-
-         TEE_OUTPUT=1 MBS=4 BS=64 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
-         SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
-
-      You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
-
-      Sample output for 2-node training:
-
-      Master node:
-
-      .. image:: ../../../data/how-to/rocm-for-ai/2-node-training-master.png
-         :width: 800
-
-      Worker node:
-
-      .. image:: ../../../data/how-to/rocm-for-ai/2-node-training-worker.png
-         :width: 800
-

docs/how-to/system-optimization/w6000-v620.md

@@ -12,7 +12,7 @@ myst:
 This chapter reviews system settings that are required to configure the system
 for ROCm virtualization on RDNA2-based AMD Radeon™ PRO GPUs. Installing ROCm on
 Bare Metal follows the routine ROCm
-{doc}`installation procedure<rocm-install-on-linux:install/native-install/index>`.
+{doc}`installation procedure<rocm-install-on-linux:install/install-methods/package-manager-index>`.
 
 To enable ROCm virtualization on V620, one has to setup Single Root I/O
 Virtualization (SR-IOV) in the BIOS via setting found in the following
@@ -166,4 +166,4 @@ First, assign GPU virtual function (VF) to VM using the following steps.
 Then start the VM.
 
 Finally install ROCm on the virtual machine (VM). For detailed instructions,
-refer to the {doc}`Linux install guide<rocm-install-on-linux:install/native-install/index>`.
+refer to the {doc}`Linux install guide<rocm-install-on-linux:install/install-methods/package-manager-index>`.

docs/index.md

@@ -38,6 +38,7 @@ ROCm documentation is organized into the following categories:
 :class-body: rocm-card-banner rocm-hue-12
 
 * [Use ROCm for AI](./how-to/rocm-for-ai/index.rst)
+* [AI tutorials](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/)
 * [Use ROCm for HPC](./how-to/rocm-for-hpc/index.rst)
 * [System optimization](./how-to/system-optimization/index.rst)
 * [AMD Instinct MI300X performance validation and tuning](./how-to/tuning-guides/mi300x/index.rst)

docs/reference/gpu-arch-specs.rst

@@ -32,6 +32,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - L1 Instruction Cache (KiB)
           - VGPR File (KiB)
           - SGPR File (KiB)
+          - GFXIP Major version
+          - GFXIP Minor version
         *
           - MI325X
           - CDNA3
@@ -47,6 +49,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 64 per 2 CUs
           - 512
           - 12.5
+          - 9
+          - 4
         *
           - MI300X
           - CDNA3
@@ -62,6 +66,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 64 per 2 CUs
           - 512
           - 12.5
+          - 9
+          - 4
         *
           - MI300A
           - CDNA3
@@ -77,6 +83,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 64 per 2 CUs
           - 512
           - 12.5
+          - 9
+          - 4
         *
           - MI250X
           - CDNA2
@@ -92,6 +100,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 2 CUs
           - 512
           - 12.5
+          - 9
+          - 0
         *
           - MI250
           - CDNA2
@@ -107,6 +117,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 2 CUs
           - 512
           - 12.5
+          - 9
+          - 0
         *
           - MI210
           - CDNA2
@@ -122,6 +134,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 2 CUs
           - 512
           - 12.5
+          - 9
+          - 0
         *
           - MI100
           - CDNA
@@ -137,6 +151,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 3 CUs
           - 256 VGPR and 256 AccVGPR
           - 12.5
+          - 9
+          - 0
         *
           - MI60
           - GCN5.1
@@ -152,6 +168,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 3 CUs
           - 256
           - 12.5
+          - 9
+          - 0
         *
           - MI50 (32GB)
           - GCN5.1
@@ -167,6 +185,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 3 CUs
           - 256
           - 12.5
+          - 9
+          - 0
         *
           - MI50 (16GB)
           - GCN5.1
@@ -182,6 +202,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 3 CUs
           - 256
           - 12.5
+          - 9
+          - 0
         *
           - MI25
           - GCN5.0
@@ -197,6 +219,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 3 CUs
           - 256
           - 12.5
+          - 9
+          - 0
         *
           - MI8
           - GCN3.0
@@ -212,6 +236,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 4 CUs
           - 256
           - 12.5
+          - 8
+          - 0
         *
           - MI6
           - GCN4.0
@@ -227,6 +253,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 4 CUs
           - 256
           - 12.5
+          - 8
+          - 0
 
   .. tab-item:: AMD Radeon PRO GPUs
 
@@ -238,6 +266,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - Model
           - Architecture
           - LLVM target name
+
           - VRAM (GiB)
           - Compute Units
           - Wavefront Size
@@ -250,6 +279,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - L0 Instruction Cache (KiB)
           - VGPR File (KiB)
           - SGPR File (KiB)
+          - GFXIP Major version
+          - GFXIP Minor version
         *
           - Radeon PRO V710
           - RDNA3
@@ -266,6 +297,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon PRO W7900 Dual Slot
           - RDNA3
@@ -282,6 +315,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon PRO W7900
           - RDNA3
@@ -298,6 +333,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon PRO W7800
           - RDNA3
@@ -314,6 +351,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon PRO W7700
           - RDNA3
@@ -330,6 +369,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon PRO W6800
           - RDNA2
@@ -346,6 +387,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon PRO W6600
           - RDNA2
@@ -362,6 +405,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon PRO V620
           - RDNA2
@@ -378,6 +423,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon Pro W5500
           - RDNA
@@ -394,6 +441,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 20
+          - 10
+          - 1
         *
           - Radeon Pro VII
           - GCN5.1
@@ -410,6 +459,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 3 CUs
           - 256
           - 12.5
+          - 9
+          - 0
 
   .. tab-item:: AMD Radeon GPUs
 
@@ -433,6 +484,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - L0 Instruction Cache (KiB)
           - VGPR File (KiB)
           - SGPR File (KiB)
+          - GFXIP Major version
+          - GFXIP Minor version
         *
           - Radeon RX 7900 XTX
           - RDNA3
@@ -449,6 +502,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon RX 7900 XT
           - RDNA3
@@ -465,6 +520,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon RX 7900 GRE
           - RDNA3
@@ -481,6 +538,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon RX 7800 XT
           - RDNA3
@@ -497,6 +556,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon RX 7700 XT
           - RDNA3
@@ -513,6 +574,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 768
           - 16
+          - 11
+          - 0
         *
           - Radeon RX 7600
           - RDNA3
@@ -529,6 +592,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 11
+          - 0
         *
           - Radeon RX 6950 XT
           - RDNA2
@@ -545,6 +610,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon RX 6900 XT
           - RDNA2
@@ -561,6 +628,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon RX 6800 XT
           - RDNA2
@@ -577,6 +646,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon RX 6800
           - RDNA2
@@ -593,6 +664,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon RX 6750 XT
           - RDNA2
@@ -609,6 +682,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon RX 6700 XT
           - RDNA2
@@ -625,6 +700,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon RX 6700
           - RDNA2
@@ -641,6 +718,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon RX 6650 XT
           - RDNA2
@@ -657,6 +736,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon RX 6600 XT
           - RDNA2
@@ -673,6 +754,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon RX 6600
           - RDNA2
@@ -689,6 +772,8 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32
           - 512
           - 16
+          - 10
+          - 3
         *
           - Radeon VII
           - GCN5.1
@@ -705,12 +790,14 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 32 per 3 CUs
           - 256
           - 12.5
+          - 9
+          - 0
 
 Glossary
 ========
 
 For more information about the terms used, see the
-:ref:`specific documents and guides <gpu-arch-documentation>`, or 
+:ref:`specific documents and guides <gpu-arch-documentation>`, or
 :doc:`Understanding the HIP programming model<hip:understand/programming_model>`.
 
 **LLVM target name**
@@ -800,6 +887,26 @@ Purpose Vector Registers, used specifically in matrix instructions.
 Size of the Scalar General Purpose Register (SGPR) file. Holds data used in
 scalar instructions.
 
+**GFXIP**
+
+GFXIP (Graphics IP) is a versioning system used by AMD to identify the GPU
+architecture and its instruction set. It helps categorize different generations
+of GPUs and their feature sets.
+
+**GFXIP major version**
+
+Defines the GPU's core instruction set and architecture, which determines
+compatibility with software stacks such as HIP and OpenCL. For example, a GFXIP
+11 major version corresponds to the RDNA 3 (Navi 3x) architecture, influencing
+driver support and available compute features.
+
+**GFXIP minor version**
+
+Represents specific variations within a GFXIP major version and affects feature sets,
+optimizations, and driver behavior in software stacks such as HIP and OpenCL. Different
+GPU models within the same major version can have unique capabilities, impacting
+performance and supported instructions.
+
 **GCD**
 
 Graphics Compute Die.

docs/reference/precision-support.rst

@@ -1,19 +1,23 @@
 .. meta::
-  :description: Supported data types in ROCm
-  :keywords: int8, float8, float8 (E4M3), float8 (E5M2), bfloat8, float16, half, bfloat16, tensorfloat32, float,
-   float32, float64, double, AMD, ROCm, AMDGPU
+  :description: Supported data types of AMD GPUs and libraries in ROCm.
+  :keywords: precision, data types, HIP types, int8, float8, float8 (E4M3),
+             float8 (E5M2), bfloat8, float16, half, bfloat16, tensorfloat32,
+             float, float32, float64, double, AMD data types, HIP data types,
+             ROCm precision, ROCm data types
 
 *************************************************************
-Precision support
+Data types and precision support
 *************************************************************
 
-Use the following sections to identify data types and HIP types ROCm™ supports.
+This topic lists the supported data types of AMD GPUs and ROCm libraries.
+Corresponding :doc:`HIP <hip:index>` data types are also noted. 
 
 Integral types
 ==========================================
 
-The signed and unsigned integral types that are supported by ROCm are listed in the following table,
-together with their corresponding HIP type and a short description.
+The signed and unsigned integral types supported by ROCm are listed in
+the following table, along with their corresponding HIP type and a short
+description.
 
 
 .. list-table::
@@ -46,8 +50,8 @@ together with their corresponding HIP type and a short description.
 Floating-point types
 ==========================================
 
-The floating-point types that are supported by ROCm are listed in the following table, together with
-their corresponding HIP type and a short description.
+The floating-point types supported by ROCm are listed in the following
+table, along with their corresponding HIP type and a short description.
 
 .. image:: ../data/about/compatibility/floating-point-data-types.png
     :alt: Supported floating-point types
@@ -63,43 +67,62 @@ their corresponding HIP type and a short description.
     *
       - float8 (E4M3)
       - ``-``
-      - An 8-bit floating-point number that mostly follows IEEE-754 conventions and **S1E4M3** bit layout, as described in `8-bit Numerical Formats for Deep Neural Networks <https://arxiv.org/abs/2206.02915>`_ , with expanded range and with no infinity or signed zero. NaN is represented as negative zero.
+      - An 8-bit floating-point number that mostly follows IEEE-754 conventions
+        and **S1E4M3** bit layout, as described in `8-bit Numerical Formats for Deep Neural Networks <https://arxiv.org/abs/2206.02915>`_ ,
+        with expanded range and no infinity or signed zero. NaN is
+        represented as negative zero.
     *
       - float8 (E5M2)
       - ``-``
-      - An 8-bit floating-point number mostly following IEEE-754 conventions and **S1E5M2** bit layout, as described in `8-bit Numerical Formats for Deep Neural Networks <https://arxiv.org/abs/2206.02915>`_ , with expanded range and with no infinity or signed zero. NaN is represented as negative zero.
+      - An 8-bit floating-point number mostly following IEEE-754 conventions and
+        **S1E5M2** bit layout, as described in `8-bit Numerical Formats for Deep Neural Networks <https://arxiv.org/abs/2206.02915>`_ ,
+        with expanded range and no infinity or signed zero. NaN is
+        represented as negative zero.
     *
       - float16
       - ``half``
-      - A 16-bit floating-point number that conforms to the IEEE 754-2008 half-precision storage format.
+      - A 16-bit floating-point number that conforms to the IEEE 754-2008
+        half-precision storage format.
     *
       - bfloat16
       - ``bfloat16``
-      - A shortened 16-bit version of the IEEE 754 single-precision storage format.
+      - A shortened 16-bit version of the IEEE 754 single-precision storage
+        format.
     *
       - tensorfloat32
       - ``-``
-      - A floating-point number that occupies 32 bits or less of storage, providing improved range compared to half (16-bit) format, at (potentially) greater throughput than single-precision (32-bit) formats.
+      - A floating-point number that occupies 32 bits or less of storage,
+        providing improved range compared to half (16-bit) format, at
+        (potentially) greater throughput than single-precision (32-bit) formats.
     *
       - float32
       - ``float``
-      - A 32-bit floating-point number that conforms to the IEEE 754 single-precision storage format.
+      - A 32-bit floating-point number that conforms to the IEEE 754
+        single-precision storage format.
     *
       - float64
       - ``double``
-      - A 64-bit floating-point number that conforms to the IEEE 754 double-precision storage format.
+      - A 64-bit floating-point number that conforms to the IEEE 754
+        double-precision storage format.
 
 .. note::
 
-  * The float8 and tensorfloat32 types are internal types used in calculations in Matrix Cores and can be stored in any type of the same size.
-  * The encodings for FP8 (E5M2) and FP8 (E4M3) that are natively supported by MI300 differ from the FP8 (E5M2) and FP8 (E4M3) encodings used in H100 (`FP8 Formats for Deep Learning <https://arxiv.org/abs/2209.05433>`_).
+  * The float8 and tensorfloat32 types are internal types used in calculations
+    in Matrix Cores and can be stored in any type of the same size.
+
+  * The encodings for FP8 (E5M2) and FP8 (E4M3) that the
+    MI300 series natively supports differ from the FP8 (E5M2) and FP8 (E4M3)
+    encodings used in NVIDIA H100
+    (`FP8 Formats for Deep Learning <https://arxiv.org/abs/2209.05433>`_).
+
   * In some AMD documents and articles, float8 (E5M2) is referred to as bfloat8.
 
 ROCm support icons
 ==========================================
 
-In the following sections, we use icons to represent the level of support. These icons, described in the
-following table, are also used on the library data type support pages.
+In the following sections, icons represent the level of support. These
+icons, described in the following table, are also used in the library data type
+support pages.
 
 .. list-table::
     :header-rows: 1
@@ -121,14 +144,27 @@ following table, are also used on the library data type support pages.
 
 .. note::
 
-  * Full support means that the type is supported natively or with hardware emulation.
-  * Native support means that the operations for that type are implemented in hardware. Types that are not natively supported are emulated with the available hardware. The performance of non-natively supported types can differ from the full instruction throughput rate. For example, 16-bit integer operations can be performed on the 32-bit integer ALUs at full rate; however, 64-bit integer operations might need several instructions on the 32-bit integer ALUs.
-  * Any type can be emulated by software, but this page does not cover such cases.
+  * Full support means that the type is supported natively or with hardware
+    emulation.
 
-Hardware type support
+  * Native support means that the operations for that type are implemented in
+    hardware. Types that are not natively supported are emulated with the
+    available hardware. The performance of non-natively supported types can
+    differ from the full instruction throughput rate. For example, 16-bit
+    integer operations can be performed on the 32-bit integer ALUs at full rate;
+    however, 64-bit integer operations might need several instructions on the
+    32-bit integer ALUs.
+
+  * Any type can be emulated by software, but this page does not cover such
+    cases.
+
+Hardware data type support
 ==========================================
 
-AMD GPU hardware support for data types is listed in the following tables.
+The following tables provide information about AMD Instinct accelerators support
+for various data types. The MI200 series GPUs, which include MI210, MI250, and
+MI250X, are based on the CDNA2 architecture. The MI300 series GPUs, consisting
+of MI300A, MI300X, and MI325X, are built on the CDNA3 architecture.
 
 Compute units support
 -------------------------------------------------------------------------------
@@ -375,21 +411,23 @@ The following table lists data type support for atomic operations.
 
 .. note::
 
-  For cases that are not natively supported, you can emulate atomic operations using software.
-  Software-emulated atomic operations have high negative performance impact when they frequently
-  access the same memory address.
+  You can emulate atomic operations using software for cases that are not
+  natively supported. Software-emulated atomic operations have a high negative
+  performance impact when they frequently access the same memory address.
 
-Data Type support in ROCm Libraries
+Data type support in ROCm libraries
 ==========================================
 
-ROCm library support for int8, float8 (E4M3), float8 (E5M2), int16, float16, bfloat16, int32,
-tensorfloat32, float32, int64, and float64 is listed in the following tables.
+ROCm library support for int8, float8 (E4M3), float8 (E5M2), int16, float16,
+bfloat16, int32, tensorfloat32, float32, int64, and float64 is listed in the
+following tables.
 
 Libraries input/output type support
 -------------------------------------------------------------------------------
 
-The following tables list ROCm library support for specific input and output data types. For a detailed
-description, refer to the corresponding library data type support page.
+The following tables list ROCm library support for specific input and output
+data types. Refer to the corresponding library data type support page for a
+detailed description.
 
 .. tab-set::
 
@@ -516,8 +554,9 @@ description, refer to the corresponding library data type support page.
 Libraries internal calculations type support
 -------------------------------------------------------------------------------
 
-The following tables list ROCm library support for specific internal data types. For a detailed
-description, refer to the corresponding library data type support page.
+The following tables list ROCm library support for specific internal data types.
+Refer to the corresponding library data type support page for a detailed
+description.
 
 .. tab-set::
 

docs/release/versions.md

@@ -10,6 +10,7 @@
 
 | Version | Release date |
 | ------- | ------------ |
+| [6.3.3](https://rocm.docs.amd.com/en/docs-6.3.3/) | February 19, 2025 |
 | [6.3.2](https://rocm.docs.amd.com/en/docs-6.3.2/) | January 28, 2025 |
 | [6.3.1](https://rocm.docs.amd.com/en/docs-6.3.1/) | December 20, 2024 |
 | [6.3.0](https://rocm.docs.amd.com/en/docs-6.3.0/) | December 3, 2024 |

docs/sphinx/_toc.yml.in

@@ -40,11 +40,13 @@ subtrees:
         title: Training
         subtrees:
         - entries:
-          - file: how-to/rocm-for-ai/training/train-a-model.rst
-            title: Train a model
+          - file: how-to/rocm-for-ai/training/benchmark-docker/megatron-lm
+            title: Train a model with Megatron-LM
+          - file: how-to/rocm-for-ai/training/benchmark-docker/pytorch-training
+            title: Train a model with PyTorch
           - file: how-to/rocm-for-ai/training/scale-model-training.rst
             title: Scale model training
-      
+
       - file: how-to/rocm-for-ai/fine-tuning/index.rst
         title: Fine-tuning LLMs
         subtrees:
@@ -89,7 +91,10 @@ subtrees:
             title: Profile and debug
           - file: how-to/rocm-for-ai/inference-optimization/workload.rst
             title: Workload tuning
-  
+
+      - url: https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/
+        title: AI tutorials
+
   - file: how-to/rocm-for-hpc/index.rst
     title: Use ROCm for HPC
   - file: how-to/system-optimization/index.rst
@@ -126,6 +131,7 @@ subtrees:
   - url: https://github.com/amd/rocm-examples
     title: ROCm examples
 
+
 - caption: Conceptual
   entries:
   - file: conceptual/gpu-arch.md

docs/sphinx/requirements.in

@@ -1,3 +1,3 @@
-rocm-docs-core==1.13.0
+rocm-docs-core==1.17.0
 sphinx-reredirects
 sphinx-sitemap

docs/sphinx/requirements.txt

@@ -1,5 +1,5 @@
 #
-# This file is autogenerated by pip-compile with Python 3.10
+# This file is autogenerated by pip-compile with Python 3.11
 # by the following command:
 #
 #    pip-compile requirements.in
@@ -8,6 +8,15 @@ accessible-pygments==0.0.5
     # via pydata-sphinx-theme
 alabaster==1.0.0
     # via sphinx
+appnope==0.1.4
+    # via ipykernel
+asttokens==3.0.0
+    # via stack-data
+attrs==25.1.0
+    # via
+    #   jsonschema
+    #   jupyter-cache
+    #   referencing
 babel==2.16.0
     # via
     #   pydata-sphinx-theme
@@ -16,7 +25,7 @@ beautifulsoup4==4.12.3
     # via pydata-sphinx-theme
 breathe==4.35.0
     # via rocm-docs-core
-certifi==2024.8.30
+certifi==2024.12.14
     # via requests
 cffi==1.17.1
     # via
@@ -25,9 +34,17 @@ cffi==1.17.1
 charset-normalizer==3.4.0
     # via requests
 click==8.1.7
-    # via sphinx-external-toc
-cryptography==43.0.3
+    # via
+    #   jupyter-cache
+    #   sphinx-external-toc
+comm==0.2.2
+    # via ipykernel
+cryptography==44.0.0
     # via pyjwt
+debugpy==1.8.12
+    # via ipykernel
+decorator==5.1.1
+    # via ipython
 deprecated==1.2.15
     # via pygithub
 docutils==0.21.2
@@ -36,8 +53,12 @@ docutils==0.21.2
     #   myst-parser
     #   pydata-sphinx-theme
     #   sphinx
-fastjsonschema==2.20.0
-    # via rocm-docs-core
+executing==2.2.0
+    # via stack-data
+fastjsonschema==2.21.1
+    # via
+    #   nbformat
+    #   rocm-docs-core
 gitdb==4.0.11
     # via gitpython
 gitpython==3.1.43
@@ -46,27 +67,88 @@ idna==3.10
     # via requests
 imagesize==1.4.1
     # via sphinx
-jinja2==3.1.5
+importlib-metadata==8.6.1
+    # via
+    #   jupyter-cache
+    #   myst-nb
+ipykernel==6.29.5
+    # via myst-nb
+ipython==8.32.0
+    # via
+    #   ipykernel
+    #   myst-nb
+jedi==0.19.2
+    # via ipython
+jinja2==3.1.4
     # via
     #   myst-parser
     #   sphinx
+jsonschema==4.23.0
+    # via nbformat
+jsonschema-specifications==2024.10.1
+    # via jsonschema
+jupyter-cache==1.0.1
+    # via myst-nb
+jupyter-client==8.6.3
+    # via
+    #   ipykernel
+    #   nbclient
+jupyter-core==5.7.2
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   nbclient
+    #   nbformat
 markdown-it-py==3.0.0
     # via
     #   mdit-py-plugins
     #   myst-parser
 markupsafe==3.0.2
     # via jinja2
+matplotlib-inline==0.1.7
+    # via
+    #   ipykernel
+    #   ipython
 mdit-py-plugins==0.4.2
     # via myst-parser
 mdurl==0.1.2
     # via markdown-it-py
-myst-parser==4.0.0
+myst-nb==1.2.0
     # via rocm-docs-core
+myst-parser==4.0.0
+    # via myst-nb
+nbclient==0.10.2
+    # via
+    #   jupyter-cache
+    #   myst-nb
+nbformat==5.10.4
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   nbclient
+nest-asyncio==1.6.0
+    # via ipykernel
 packaging==24.2
-    # via sphinx
+    # via
+    #   ipykernel
+    #   sphinx
+parso==0.8.4
+    # via jedi
+pexpect==4.9.0
+    # via ipython
+platformdirs==4.3.6
+    # via jupyter-core
+prompt-toolkit==3.0.50
+    # via ipython
+psutil==7.0.0
+    # via ipykernel
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.3
+    # via stack-data
 pycparser==2.22
     # via cffi
-pydata-sphinx-theme==0.16.0
+pydata-sphinx-theme==0.16.1
     # via
     #   rocm-docs-core
     #   sphinx-book-theme
@@ -75,23 +157,42 @@ pygithub==2.5.0
 pygments==2.18.0
     # via
     #   accessible-pygments
+    #   ipython
     #   pydata-sphinx-theme
     #   sphinx
-pyjwt[crypto]==2.10.0
+pyjwt[crypto]==2.10.1
     # via pygithub
 pynacl==1.5.0
     # via pygithub
+python-dateutil==2.9.0.post0
+    # via jupyter-client
 pyyaml==6.0.2
     # via
+    #   jupyter-cache
+    #   myst-nb
     #   myst-parser
     #   rocm-docs-core
     #   sphinx-external-toc
+pyzmq==26.2.1
+    # via
+    #   ipykernel
+    #   jupyter-client
+referencing==0.36.2
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
 requests==2.32.3
     # via
     #   pygithub
     #   sphinx
-rocm-docs-core==1.13.0
+rocm-docs-core==1.17.0
     # via -r requirements.in
+rpds-py==0.22.3
+    # via
+    #   jsonschema
+    #   referencing
+six==1.17.0
+    # via python-dateutil
 smmap==5.0.1
     # via gitdb
 snowballstemmer==2.2.0
@@ -101,6 +202,7 @@ soupsieve==2.6
 sphinx==8.1.3
     # via
     #   breathe
+    #   myst-nb
     #   myst-parser
     #   pydata-sphinx-theme
     #   rocm-docs-core
@@ -137,15 +239,41 @@ sphinxcontrib-qthelp==2.0.0
     # via sphinx
 sphinxcontrib-serializinghtml==2.0.0
     # via sphinx
-tomli==2.1.0
-    # via sphinx
+sqlalchemy==2.0.38
+    # via jupyter-cache
+stack-data==0.6.3
+    # via ipython
+tabulate==0.9.0
+    # via jupyter-cache
+tornado==6.4.2
+    # via
+    #   ipykernel
+    #   jupyter-client
+traitlets==5.14.3
+    # via
+    #   comm
+    #   ipykernel
+    #   ipython
+    #   jupyter-client
+    #   jupyter-core
+    #   matplotlib-inline
+    #   nbclient
+    #   nbformat
 typing-extensions==4.12.2
     # via
+    #   ipython
+    #   myst-nb
     #   pydata-sphinx-theme
     #   pygithub
+    #   referencing
+    #   sqlalchemy
 urllib3==2.2.3
     # via
     #   pygithub
     #   requests
+wcwidth==0.2.13
+    # via prompt-toolkit
 wrapt==1.17.0
     # via deprecated
+zipp==3.21.0
+    # via importlib-metadata

tools/autotag/components.xml

@@ -1,7 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <manifest>
     <remote name="rocm-org" fetch="https://github.com/ROCm/" />
-    <default revision="refs/tags/rocm-6.3.2"
+    <default revision="refs/tags/rocm-6.3.3"
      remote="rocm-org"
      sync-c="true"
      sync-j="4" />

tools/autotag/templates/highlights/6.3.3.md

@@ -0,0 +1,47 @@
+# ROCm 6.3.3 release notes
+
+The release notes provide a summary of notable changes since the previous ROCm release.
+
+- [Release highlights](#release-highlights)
+
+- [Operating system and hardware support changes](#operating-system-and-hardware-support-changes)
+
+- [ROCm components versioning](#rocm-components)
+
+- [Detailed component changes](#detailed-component-changes)
+
+- [ROCm known issues](#rocm-known-issues)
+
+- [ROCm upcoming changes](#rocm-upcoming-changes)
+
+```{note}
+If you’re using Radeon™ PRO or Radeon GPUs in a workstation setting with a display connected, see the [Use ROCm on Radeon GPUs](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/compatibility/native_linux/native_linux_compatibility.html)
+documentation to verify compatibility and system requirements.
+```
+## Release highlights
+
+The following are notable new features and improvements in ROCm 6.3.3. For changes to individual components, see
+[Detailed component changes](#detailed-component-changes).
+
+### ROCm Offline Installer Creator updates
+
+The ROCm Offline Installer Creator 6.3.3 adds a new Post-Install Options menu, which includes a new ``udev`` option for adding GPU resources access for all users. It also moves the user-specific GPU access option (for the ``video,render`` group) from the Driver Options menu to the Post-Install Options menu. See the [ROCm Offline Installer Creator](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/rocm-offline-installer.html#post-install-options-menu) documentation for more information.
+
+### ROCm documentation updates
+
+ROCm documentation continues to be updated to provide clearer and more comprehensive guidance for a wider variety of user needs and use cases.
+
+* [Tutorials for AI developers](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/) have been added. These tutorials are Jupyter notebook-based, easy-to-follow documents. They are ideal for AI developers who want to learn about specific topics, including inference, fine-tuning, and training.
+
+* The [LLM inference performance validation guide for AMD Instinct MI300X](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/vllm-benchmark.html)
+  now includes additional models for performance benchmarking. The accompanying ROCm vLLM Docker has been upgraded to ROCm 6.3.1.
+ 
+* The HIP documentation has been updated with new resources for developers. To learn more about concurrency, parallelism, and stream management on devices and multiple GPUs, see [Asynchronous concurrent execution](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_runtime_api/asynchronous.html)
+
+* The following HIP documentation topics have been updated:
+    - [Virtual memory management](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_runtime_api/memory_management/virtual_memory.html)
+    - [Programming for HIP runtime compiler (RTC)](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_rtc.html)
+    - [HIP porting guide](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_porting_guide.html)
+    - [Porting CUDA driver API](https://rocm.docs.amd.com/projects/HIP/en/latest/how-to/hip_porting_driver_api.html)
+    - [CUDA to HIP API function comparison](https://rocm.docs.amd.com/projects/HIP/en/latest/reference/api_syntax.html)
+

tools/autotag/templates/known_issues/6.3.3.md

@@ -0,0 +1,8 @@
+## ROCm known issues
+
+ROCm known issues are noted on {fab}`github` [GitHub](https://github.com/ROCm/ROCm/labels/Verified%20Issue). For known
+issues related to individual components, review the [Detailed component changes](#detailed-component-changes).
+
+### Zero value is displayed in ROCTx aggregated statistics
+
+The ROCTx markers are standalone markers within the ROCProfiler-SDK library. Each marker reports only a single timestamp, which is recorded as the `start_timestamp` and `end_timestamp`. As a result, the value for aggregated statistics presented in `TotalDurationNs`, `maxNs`, and `minNs`, is zero. The zero value indicates that the actual execution time is not associated with the markers, which is an expected behavior.

tools/autotag/templates/support/6.3.3.md

@@ -0,0 +1,7 @@
+## Operating system and hardware support changes
+
+Operating system and hardware support remain unchanged in this release.
+
+See the [Compatibility
+matrix](https://rocm.docs.amd.com/en/docs-6.3.3/compatibility/compatibility-matrix.html)
+for more information about operating system and hardware compatibility.

tools/autotag/templates/upcoming_changes/6.3.3.md

@@ -0,0 +1,17 @@
+## ROCm upcoming changes
+
+The following changes to the ROCm software stack are anticipated for future releases.
+
+### ROCTracer and ROCProfiler (rocprof and rocprofv2) deprecation
+
+Development and support for ROCTracer and ROCProfiler (`rocprof` and `rocprofv2`) will phase out in favor of ROCprofiler-SDK (`rocprofv3`) in upcoming ROCm releases. Going forward, only critical defect fixes will be addressed for older versions of profiling tools and libraries. Upgrade to the latest version of ROCprofiler-SDK (`rocprofv3`) library to ensure continued support and access to new features.
+
+### AMDGPU wavefront size compiler macro deprecation
+
+The `__AMDGCN_WAVEFRONT_SIZE__` macro will be deprecated in an upcoming
+release. It is recommended to remove any use of this macro. For more information, see [AMDGPU
+support](https://rocm.docs.amd.com/projects/llvm-project/en/docs-6.3.3/LLVM/clang/html/AMDGPUSupport.html).
+
+### HIPCC Perl scripts deprecation
+
+The HIPCC Perl scripts (`hipcc.pl` and `hipconfig.pl`) will be removed in an upcoming release.

tools/rocm-build/rocm-6.3.3.xml

@@ -0,0 +1,77 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<manifest>
+    <remote name="rocm-org" fetch="https://github.com/ROCm/" />
+    <default revision="refs/tags/rocm-6.3.3"
+     remote="rocm-org"
+     sync-c="true"
+     sync-j="4" />
+<!--list of projects for ROCm-->
+    <project name="ROCm" revision="roc-6.3.x" />
+    <project name="ROCK-Kernel-Driver" />
+    <project name="ROCR-Runtime" />
+    <project name="amdsmi" />
+    <project name="rdc" />
+    <project name="rocm_bandwidth_test" />
+    <project name="rocm_smi_lib" />
+    <project name="rocm-core" />
+    <project name="rocm-examples" />
+    <project name="rocminfo" />
+    <project name="rocprofiler" />
+    <project name="rocprofiler-register" />
+    <project name="rocprofiler-sdk" />
+    <project name="rocprofiler-compute" />
+    <project name="rocprofiler-systems" />
+    <project name="roctracer" />
+<!--HIP Projects-->
+    <project name="HIP" />
+    <project name="hip-tests" />
+    <project name="HIPIFY" />
+    <project name="clr" />
+    <project name="hipother" />
+<!-- The following projects are all associated with the AMDGPU LLVM compiler -->
+    <project name="half" />
+    <project name="llvm-project" />
+<!-- gdb projects -->
+    <project name="ROCdbgapi" />
+    <project name="ROCgdb" />
+    <project name="rocr_debug_agent" />
+<!-- ROCm Libraries -->
+    <project groups="mathlibs" name="AMDMIGraphX" />
+    <project groups="mathlibs" name="MIOpen" />
+    <project groups="mathlibs" name="MIVisionX" />
+    <project groups="mathlibs" name="ROCmValidationSuite" />
+    <project groups="mathlibs" name="Tensile" />
+    <project groups="mathlibs" name="composable_kernel" />
+    <project groups="mathlibs" name="hipBLAS-common" />
+    <project groups="mathlibs" name="hipBLAS" />
+    <project groups="mathlibs" name="hipBLASLt" />
+    <project groups="mathlibs" name="hipCUB" />
+    <project groups="mathlibs" name="hipFFT" />
+    <project groups="mathlibs" name="hipRAND" />
+    <project groups="mathlibs" name="hipSOLVER" />
+    <project groups="mathlibs" name="hipSPARSE" />
+    <project groups="mathlibs" name="hipSPARSELt" />
+    <project groups="mathlibs" name="hipTensor" />
+    <project groups="mathlibs" name="hipfort" />
+    <project groups="mathlibs" name="rccl" />
+    <project groups="mathlibs" name="rocAL" />
+    <project groups="mathlibs" name="rocALUTION" />
+    <project groups="mathlibs" name="rocBLAS" />
+    <project groups="mathlibs" name="rocDecode" />
+    <project groups="mathlibs" name="rocJPEG" />
+    <project groups="mathlibs" name="rocPyDecode" />
+    <project groups="mathlibs" name="rocFFT" />
+    <project groups="mathlibs" name="rocPRIM" />
+    <project groups="mathlibs" name="rocRAND" />
+    <project groups="mathlibs" name="rocSOLVER" />
+    <project groups="mathlibs" name="rocSPARSE" />
+    <project groups="mathlibs" name="rocThrust" />
+    <project groups="mathlibs" name="rocWMMA" />
+    <project groups="mathlibs" name="rocm-cmake" />
+    <project groups="mathlibs" name="rpp" />
+    <project groups="mathlibs" name="TransferBench" />
+<!-- Projects for OpenMP-Extras -->
+    <project name="aomp" path="openmp-extras/aomp" />
+    <project name="aomp-extras" path="openmp-extras/aomp-extras" />
+    <project name="flang" path="openmp-extras/flang" />
+</manifest>