Adding hipDNN links

docs/about/license.md

@@ -39,6 +39,7 @@ additional licenses. Please review individual repositories for more information.
 | [hipBLASLt](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipblaslt/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipblaslt/LICENSE.md) |
 | [HIPCC](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/hipcc) | [MIT](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/hipcc/LICENSE.txt) |
 | [hipCUB](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipcub/) | [Custom](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipcub/LICENSE.txt) |
+| [hipDNN](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipdnn/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipdnn/LICENSE.md) |
 | [hipFFT](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipfft/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipfft/LICENSE.md) |
 | [hipfort](https://github.com/ROCm/hipfort/) | [MIT](https://github.com/ROCm/hipfort/blob/develop/LICENSE) |
 | [HIPIFY](https://github.com/ROCm/HIPIFY/) | [MIT](https://github.com/ROCm/HIPIFY/blob/amd-staging/LICENSE.txt) |

docs/conf.py

@@ -138,14 +138,12 @@ article_pages = [
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.8", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.9", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.10", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.11", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-primus-migration-guide", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-megatron", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.7", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.8", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.9", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.10", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.11", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-history", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.3", "os": ["linux"]},
@@ -156,12 +154,10 @@ article_pages = [
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.8", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.9", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.10", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.11", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.8", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.9", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.10", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.11", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4", "os": ["linux"]},

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

@@ -1,13 +1,15 @@
 docker:
-  pull_tag: rocm/primus:v26.1
+  pull_tag: rocm/primus:v25.10
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
   components:
     ROCm: 7.1.0
+    Primus: 0.3.0
+    Primus Turbo: 0.1.1
     PyTorch: 2.10.0.dev20251112+rocm7.1
     Python: "3.10"
-    Transformer Engine: 2.6.0.dev0+f141f34b
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
     Flash Attention: 2.8.3
-    hipBLASLt: 34459f66ea
+    hipBLASLt: 1.2.0-09ab7153e2
     Triton: 3.4.0
     RCCL: 2.27.7
 model_groups:

docs/data/how-to/rocm-for-ai/training/previous-versions/megatron-lm-v25.11-benchmark-models.yaml

@@ -1,47 +0,0 @@
-docker:
-  pull_tag: rocm/primus:v25.11
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3
-  components:
-    ROCm: 7.1.0
-    PyTorch: 2.10.0.dev20251112+rocm7.1
-    Python: "3.10"
-    Transformer Engine: 2.4.0.dev0+32e2d1d4
-    Flash Attention: 2.8.3
-    hipBLASLt: 1.2.0-09ab7153e2
-    Triton: 3.4.0
-    RCCL: 2.27.7
-model_groups:
-  - group: Meta Llama
-    tag: llama
-    models:
-      - model: Llama 3.3 70B
-        mad_tag: pyt_megatron_lm_train_llama-3.3-70b
-      - model: Llama 3.1 8B
-        mad_tag: pyt_megatron_lm_train_llama-3.1-8b
-      - model: Llama 3.1 70B
-        mad_tag: pyt_megatron_lm_train_llama-3.1-70b
-      - model: Llama 2 7B
-        mad_tag: pyt_megatron_lm_train_llama-2-7b
-      - model: Llama 2 70B
-        mad_tag: pyt_megatron_lm_train_llama-2-70b
-  - group: DeepSeek
-    tag: deepseek
-    models:
-      - model: DeepSeek-V3 (proxy)
-        mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
-      - model: DeepSeek-V2-Lite
-        mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
-  - group: Mistral AI
-    tag: mistral
-    models:
-      - model: Mixtral 8x7B
-        mad_tag: pyt_megatron_lm_train_mixtral-8x7b
-      - model: Mixtral 8x22B (proxy)
-        mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy
-  - group: Qwen
-    tag: qwen
-    models:
-      - model: Qwen 2.5 7B
-        mad_tag: pyt_megatron_lm_train_qwen2.5-7b
-      - model: Qwen 2.5 72B
-        mad_tag: pyt_megatron_lm_train_qwen2.5-72b

docs/data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.11-benchmark-models.yaml

@@ -1,58 +0,0 @@
-docker:
-  pull_tag: rocm/primus:v25.11
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3
-  components:
-    ROCm: 7.1.0
-    PyTorch: 2.10.0.dev20251112+rocm7.1
-    Python: "3.10"
-    Transformer Engine: 2.4.0.dev0+32e2d1d4
-    Flash Attention: 2.8.3
-    hipBLASLt: 1.2.0-09ab7153e2
-    Triton: 3.4.0
-    RCCL: 2.27.7
-model_groups:
-  - group: Meta Llama
-    tag: llama
-    models:
-      - model: Llama 3.3 70B
-        mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
-        config_name: llama3.3_70B-pretrain.yaml
-      - model: Llama 3.1 70B
-        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
-        config_name: llama3.1_70B-pretrain.yaml
-      - model: Llama 3.1 8B
-        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
-        config_name: llama3.1_8B-pretrain.yaml
-      - model: Llama 2 7B
-        mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
-        config_name: llama2_7B-pretrain.yaml
-      - model: Llama 2 70B
-        mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
-        config_name: llama2_70B-pretrain.yaml
-  - group: DeepSeek
-    tag: deepseek
-    models:
-      - model: DeepSeek-V3 (proxy)
-        mad_tag: primus_pyt_megatron_lm_train_deepseek-v3-proxy
-        config_name: deepseek_v3-pretrain.yaml
-      - model: DeepSeek-V2-Lite
-        mad_tag: primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
-        config_name: deepseek_v2_lite-pretrain.yaml
-  - group: Mistral AI
-    tag: mistral
-    models:
-      - model: Mixtral 8x7B
-        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x7b
-        config_name: mixtral_8x7B_v0.1-pretrain.yaml
-      - model: Mixtral 8x22B (proxy)
-        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
-        config_name: mixtral_8x22B_v0.1-pretrain.yaml
-  - group: Qwen
-    tag: qwen
-    models:
-      - model: Qwen 2.5 7B
-        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
-        config_name: primus_qwen2.5_7B-pretrain.yaml
-      - model: Qwen 2.5 72B
-        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-72b
-        config_name: qwen2.5_72B-pretrain.yaml

docs/data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.11-benchmark-models.yaml

@@ -1,32 +0,0 @@
-docker:
-  pull_tag: rocm/primus:v25.11
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3
-  components:
-    ROCm: 7.1.0
-    PyTorch: 2.10.0.dev20251112+rocm7.1
-    Python: "3.10"
-    Transformer Engine: 2.4.0.dev0+32e2d1d4
-    Flash Attention: 2.8.3
-    hipBLASLt: 1.2.0-09ab7153e2
-model_groups:
-  - group: Meta Llama
-    tag: llama
-    models:
-      - model: Llama 3.1 8B
-        mad_tag: primus_pyt_train_llama-3.1-8b
-        model_repo: Llama-3.1-8B
-        url: https://huggingface.co/meta-llama/Llama-3.1-8B
-        precision: BF16
-      - model: Llama 3.1 70B
-        mad_tag: primus_pyt_train_llama-3.1-70b
-        model_repo: Llama-3.1-70B
-        url: https://huggingface.co/meta-llama/Llama-3.1-70B
-        precision: BF16
-  - group: DeepSeek
-    tag: deepseek
-    models:
-      - model: DeepSeek V3 16B
-        mad_tag: primus_pyt_train_deepseek-v3-16b
-        model_repo: DeepSeek-V3
-        url: https://huggingface.co/deepseek-ai/DeepSeek-V3
-        precision: BF16

docs/data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.11-benchmark-models.yaml

@@ -1,195 +0,0 @@
-docker:
-  pull_tag: rocm/primus:v25.11
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3
-  components:
-    ROCm: 7.1.0
-    PyTorch: 2.10.0.dev20251112+rocm7.1
-    Python: "3.10"
-    Transformer Engine: 2.4.0.dev0+32e2d1d4
-    Flash Attention: 2.8.3
-    hipBLASLt: 1.2.0-09ab7153e2
-model_groups:
-  - group: Meta Llama
-    tag: llama
-    models:
-    - model: Llama 4 Scout 17B-16E
-      mad_tag: pyt_train_llama-4-scout-17b-16e
-      model_repo: Llama-4-17B_16E
-      url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
-    - model: Llama 3.3 70B
-      mad_tag: pyt_train_llama-3.3-70b
-      model_repo: Llama-3.3-70B
-      url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
-    - model: Llama 3.2 1B
-      mad_tag: pyt_train_llama-3.2-1b
-      model_repo: Llama-3.2-1B
-      url: https://huggingface.co/meta-llama/Llama-3.2-1B
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
-    - model: Llama 3.2 3B
-      mad_tag: pyt_train_llama-3.2-3b
-      model_repo: Llama-3.2-3B
-      url: https://huggingface.co/meta-llama/Llama-3.2-3B
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
-    - model: Llama 3.2 Vision 11B
-      mad_tag: pyt_train_llama-3.2-vision-11b
-      model_repo: Llama-3.2-Vision-11B
-      url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
-      precision: BF16
-      training_modes: [finetune_fw]
-    - model: Llama 3.2 Vision 90B
-      mad_tag: pyt_train_llama-3.2-vision-90b
-      model_repo: Llama-3.2-Vision-90B
-      url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
-      precision: BF16
-      training_modes: [finetune_fw]
-    - model: Llama 3.1 8B
-      mad_tag: pyt_train_llama-3.1-8b
-      model_repo: Llama-3.1-8B
-      url: https://huggingface.co/meta-llama/Llama-3.1-8B
-      precision: BF16
-      training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
-    - model: Llama 3.1 70B
-      mad_tag: pyt_train_llama-3.1-70b
-      model_repo: Llama-3.1-70B
-      url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
-      precision: BF16
-      training_modes: [pretrain, finetune_fw, finetune_lora]
-    - model: Llama 3.1 405B
-      mad_tag: pyt_train_llama-3.1-405b
-      model_repo: Llama-3.1-405B
-      url: https://huggingface.co/meta-llama/Llama-3.1-405B
-      precision: BF16
-      training_modes: [finetune_qlora]
-    - model: Llama 3 8B
-      mad_tag: pyt_train_llama-3-8b
-      model_repo: Llama-3-8B
-      url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
-    - model: Llama 3 70B
-      mad_tag: pyt_train_llama-3-70b
-      model_repo: Llama-3-70B
-      url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
-    - model: Llama 2 7B
-      mad_tag: pyt_train_llama-2-7b
-      model_repo: Llama-2-7B
-      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
-    - model: Llama 2 13B
-      mad_tag: pyt_train_llama-2-13b
-      model_repo: Llama-2-13B
-      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
-    - model: Llama 2 70B
-      mad_tag: pyt_train_llama-2-70b
-      model_repo: Llama-2-70B
-      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
-      precision: BF16
-      training_modes: [finetune_lora, finetune_qlora]
-  - group: OpenAI
-    tag: openai
-    models:
-    - model: GPT OSS 20B
-      mad_tag: pyt_train_gpt_oss_20b
-      model_repo: GPT-OSS-20B
-      url: https://huggingface.co/openai/gpt-oss-20b
-      precision: BF16
-      training_modes: [HF_finetune_lora]
-    - model: GPT OSS 120B
-      mad_tag: pyt_train_gpt_oss_120b
-      model_repo: GPT-OSS-120B
-      url: https://huggingface.co/openai/gpt-oss-120b
-      precision: BF16
-      training_modes: [HF_finetune_lora]
-  - group: DeepSeek
-    tag: deepseek
-    models:
-    - model: DeepSeek V2 16B
-      mad_tag: primus_pyt_train_deepseek-v2
-      model_repo: DeepSeek-V2
-      url: https://huggingface.co/deepseek-ai/DeepSeek-V2
-      precision: BF16
-      training_modes: [pretrain]
-  - group: Qwen
-    tag: qwen
-    models:
-    - model: Qwen 3 8B
-      mad_tag: pyt_train_qwen3-8b
-      model_repo: Qwen3-8B
-      url: https://huggingface.co/Qwen/Qwen3-8B
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
-    - model: Qwen 3 32B
-      mad_tag: pyt_train_qwen3-32b
-      model_repo: Qwen3-32
-      url: https://huggingface.co/Qwen/Qwen3-32B
-      precision: BF16
-      training_modes: [finetune_lora]
-    - model: Qwen 2.5 32B
-      mad_tag: pyt_train_qwen2.5-32b
-      model_repo: Qwen2.5-32B
-      url: https://huggingface.co/Qwen/Qwen2.5-32B
-      precision: BF16
-      training_modes: [finetune_lora]
-    - model: Qwen 2.5 72B
-      mad_tag: pyt_train_qwen2.5-72b
-      model_repo: Qwen2.5-72B
-      url: https://huggingface.co/Qwen/Qwen2.5-72B
-      precision: BF16
-      training_modes: [finetune_lora]
-    - model: Qwen 2 1.5B
-      mad_tag: pyt_train_qwen2-1.5b
-      model_repo: Qwen2-1.5B
-      url: https://huggingface.co/Qwen/Qwen2-1.5B
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
-    - model: Qwen 2 7B
-      mad_tag: pyt_train_qwen2-7b
-      model_repo: Qwen2-7B
-      url: https://huggingface.co/Qwen/Qwen2-7B
-      precision: BF16
-      training_modes: [finetune_fw, finetune_lora]
-  - group: Stable Diffusion
-    tag: sd
-    models:
-    - model: Stable Diffusion XL
-      mad_tag: pyt_huggingface_stable_diffusion_xl_2k_lora_finetuning
-      model_repo: SDXL
-      url: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
-      precision: BF16
-      training_modes: [posttrain]
-  - group: Flux
-    tag: flux
-    models:
-    - model: FLUX.1-dev
-      mad_tag: pyt_train_flux
-      model_repo: Flux
-      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
-      precision: BF16
-      training_modes: [posttrain]
-  - group: NCF
-    tag: ncf
-    models:
-    - model: NCF
-      mad_tag: pyt_ncf_training
-      model_repo:
-      url: https://github.com/ROCm/FluxBenchmark
-      precision: FP32
-  - group: DLRM
-    tag: dlrm
-    models:
-    - model: DLRM v2
-      mad_tag: pyt_train_dlrm
-      model_repo: DLRM
-      url: https://github.com/AMD-AGI/DLRMBenchmark
-      training_modes: [pretrain]

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

@@ -1,13 +1,13 @@
 docker:
-  pull_tag: rocm/primus:v26.1
+  pull_tag: rocm/primus:v25.11
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
   components:
     ROCm: 7.1.0
     PyTorch: 2.10.0.dev20251112+rocm7.1
     Python: "3.10"
-    Transformer Engine: 2.6.0.dev0+f141f34b
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
     Flash Attention: 2.8.3
-    hipBLASLt: 34459f66ea
+    hipBLASLt: 1.2.0-09ab7153e2
     Triton: 3.4.0
     RCCL: 2.27.7
 model_groups:

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

@@ -1,13 +1,13 @@
 docker:
-  pull_tag: rocm/primus:v26.1
+  pull_tag: rocm/primus:v25.11
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
   components:
     ROCm: 7.1.0
     PyTorch: 2.10.0.dev20251112+rocm7.1
     Python: "3.10"
-    Transformer Engine: 2.6.0.dev0+f141f34b
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
     Flash Attention: 2.8.3
-    hipBLASLt: 34459f66ea
+    hipBLASLt: 1.2.0-09ab7153e2
 model_groups:
   - group: Meta Llama
     tag: llama

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

@@ -1,13 +1,15 @@
 docker:
-  pull_tag: rocm/primus:v26.1
+  pull_tag: rocm/primus:v25.10
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
   components:
     ROCm: 7.1.0
+    Primus: 0.3.0
+    Primus Turbo: 0.1.1
     PyTorch: 2.10.0.dev20251112+rocm7.1
     Python: "3.10"
-    Transformer Engine: 2.6.0.dev0+f141f34b
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
     Flash Attention: 2.8.3
-    hipBLASLt: 34459f66ea
+    hipBLASLt: 1.2.0-09ab7153e2
 model_groups:
   - group: Meta Llama
     tag: llama

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

@@ -52,7 +52,7 @@ accelerate training workloads:
               - {{ component_version }}
             {% endfor %}
 
-   .. _amd-megatron-lm-model-support-v26.01:
+   .. _amd-megatron-lm-model-support-v25.11:
 
    Supported models
    ================
@@ -97,7 +97,7 @@ accelerate training workloads:
    Some models, such as Llama, require an external license agreement through
    a third party (for example, Meta).
 
-.. _amd-megatron-lm-performance-measurements-v26.01:
+.. _amd-megatron-lm-performance-measurements-v25.11:
 
 Performance measurements
 ========================
@@ -129,7 +129,7 @@ To test for optimal performance, consult the recommended :ref:`System health ben
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 
-.. _mi300x-amd-megatron-lm-training-v26.01:
+.. _mi300x-amd-megatron-lm-training-v25.11:
 
 Environment setup
 =================
@@ -138,7 +138,7 @@ Use the following instructions to set up the environment, configure the script t
 reproduce the benchmark results on MI300X Series GPUs with the AMD Megatron-LM Docker
 image.
 
-.. _amd-megatron-lm-requirements-v26.01:
+.. _amd-megatron-lm-requirements-v25.11:
 
 Download the Docker image
 -------------------------
@@ -190,7 +190,7 @@ Download the Docker image
 The Docker container hosts a verified commit of
 `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev>`__.
 
-.. _amd-megatron-lm-environment-setup-v26.01:
+.. _amd-megatron-lm-environment-setup-v25.11:
 
 Configuration
 =============
@@ -200,39 +200,39 @@ Configuration
    Update the ``train_llama3.sh`` configuration script in the ``examples/llama``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v25.11>`.
 
 .. container:: model-doc pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
 
    Update the ``train_llama2.sh`` configuration script in the ``examples/llama``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v25.11>`.
 
 .. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
 
    Update the ``train_deepseekv3.sh`` configuration script in the ``examples/deepseek_v3``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v3>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v25.11>`.
 
 .. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
 
    Update the ``train_deepseekv2.sh`` configuration script in the ``examples/deepseek_v2``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v2>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v25.11>`.
 
 .. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
 
    Update the ``train_mixtral_moe.sh`` configuration script in the ``examples/mixtral``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/mixtral>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v25.11>`.
 
 .. note::
 
-   See :ref:`Key options <amd-megatron-lm-benchmark-test-vars-v26.01>` for more information on configuration options.
+   See :ref:`Key options <amd-megatron-lm-benchmark-test-vars-v25.11>` for more information on configuration options.
 
 Multi-node configuration
 ------------------------
@@ -240,7 +240,7 @@ Multi-node configuration
 Refer to :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your environment for multi-node
 training. See :ref:`amd-megatron-lm-multi-node-examples` for example run commands.
 
-.. _amd-megatron-lm-tokenizer-v26.01:
+.. _amd-megatron-lm-tokenizer-v25.11:
 
 Tokenizer
 ---------
@@ -377,7 +377,7 @@ Download the dataset
 
    ``TOKENIZER_MODEL`` can be any accessible Hugging Face tokenizer.
    Remember to either pre-download the tokenizer or setup Hugging Face access
-   otherwise when needed -- see the :ref:`Tokenizer <amd-megatron-lm-tokenizer-v26.01>` section.
+   otherwise when needed -- see the :ref:`Tokenizer <amd-megatron-lm-tokenizer-v25.11>` section.
 
    .. note::
 
@@ -479,13 +479,13 @@ Download the dataset
 
    Ensure that the files are accessible inside the Docker container.
 
-.. _amd-megatron-lm-run-training-v26.01:
+.. _amd-megatron-lm-run-training-v25.11:
 
 Run training
 ============
 
 Use the following example commands to set up the environment, configure
-:ref:`key options <amd-megatron-lm-benchmark-test-vars-v26.01>`, and run training on
+:ref:`key options <amd-megatron-lm-benchmark-test-vars-v25.11>`, and run training on
 MI300X Series GPUs with the AMD Megatron-LM environment.
 
 Before starting training, export the following environment variables.
@@ -920,7 +920,7 @@ Single node training
           RECOMPUTE_ACTIVATIONS=full \
           CKPT_FORMAT=torch_dist
 
-.. _amd-megatron-lm-multi-node-examples-v26.01:
+.. _amd-megatron-lm-multi-node-examples-v25.11:
 
 Multi-node training examples
 ----------------------------
@@ -971,7 +971,7 @@ training on 16 nodes, try the following command:
 
    sbatch examples/deepseek_v3/train_deepseek_v3_slurm.sh
 
-.. _amd-megatron-lm-benchmark-test-vars-v26.01:
+.. _amd-megatron-lm-benchmark-test-vars-v25.11:
 
 Key options
 -----------

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

@@ -16,23 +16,14 @@ previous releases of the ``ROCm/megatron-lm`` Docker image on `Docker Hub <https
      - Components
      - Resources
 
-   * - v26.1 (latest)
+   * - v25.11
      -
        * ROCm 7.1.0
        * PyTorch 2.10.0.dev20251112+rocm7.1
      -
        * :doc:`Primus Megatron documentation <../primus-megatron>`
        * :doc:`Megatron-LM (legacy) documentation <../megatron-lm>`
-       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d>`__
+       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197>`__
-
-   * - v25.11
-     -
-       * ROCm 7.1.0
-       * PyTorch 2.10.0.dev20251112+rocm7.1
-     -
-       * :doc:`Primus Megatron documentation <primus-megatron-v25.11>`
-       * :doc:`Megatron-LM (legacy) documentation <megatron-lm-v25.10>`
-       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3>`__
 
    * - v25.10
      -

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.10.rst

@@ -37,7 +37,7 @@ GPUs containing essential components, including PyTorch, ROCm libraries, and
 Megatron-LM utilities. It contains the following software components to
 accelerate training workloads:
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/megatron-lm-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
 
    .. tab-set::
 
@@ -146,7 +146,7 @@ image.
 Download the Docker image
 -------------------------
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/megatron-lm-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
 
    {% set docker = data.docker %}
    1. Use the following command to pull the Docker image from Docker Hub.
@@ -811,7 +811,7 @@ Single node training
       Note that DeepSeek-V2-Lite is experiencing instability due to GPU memory access fault
       for large iterations.
       For stability, it's recommended to use Primus for this workload.
-      See :doc:`../primus-megatron`.
+      See :doc:`primus-megatron`.
 
 .. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b
 

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.10.rst

@@ -25,10 +25,10 @@ model training. Performance acceleration is powered by `Primus Turbo
    <https://hub.docker.com/r/rocm/megatron-lm/>`__ Docker Hub registry will be
    deprecated soon in favor of `rocm/primus <https://hub.docker.com/r/rocm/primus>`__.
    The ``rocm/primus`` Docker containers will cover PyTorch training ecosystem frameworks,
-   including Megatron-LM and :doc:`torchtitan <../primus-pytorch>`.
+   including Megatron-LM and :doc:`torchtitan <primus-pytorch>`.
 
    Primus with Megatron is designed to replace the :doc:`ROCm Megatron-LM
-   training <../megatron-lm>` workflow. To learn how to migrate workloads from
+   training <megatron-lm>` workflow. To learn how to migrate workloads from
    Megatron-LM to Primus with Megatron, see
    :doc:`megatron-lm-primus-migration-guide`.
 
@@ -36,7 +36,7 @@ AMD provides a ready-to-use Docker images for MI355X, MI350X,
 MI325X, and MI300X GPUs containing essential components for Primus, ROCm, and
 Megatron-LM.
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
    .. tab-set::
 
@@ -63,7 +63,7 @@ The following models are pre-optimized for performance on AMD Instinct GPUs.
 Some instructions, commands, and training examples in this documentation
 might vary by model -- select one to get started.
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
    {% set model_groups = data.model_groups %}
    .. raw:: html
@@ -120,7 +120,7 @@ system's configuration.
 Environment setup
 =================
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
    Use the following instructions to set up the environment, configure the script to train models, and
    reproduce the benchmark results on AMD Instinct GPUs.
@@ -129,7 +129,7 @@ Environment setup
 
 Pull the Docker image
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
    {% set docker = data.docker %}
 
@@ -175,7 +175,7 @@ Configuration
 Primus defines a training configuration in YAML for each model in
 `examples/megatron/configs <https://github.com/AMD-AGI/Primus/tree/e16b27bf6c1b2798f38848fc574fee60d9a9b902/examples/megatron/configs>`__.
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
    {% set model_groups = data.model_groups %}
    {% for model_group in model_groups %}
@@ -805,7 +805,7 @@ To run training on multiple nodes, you can use the
 `run_slurm_pretrain.sh <https://github.com/AMD-AGI/Primus/blob/main/examples/run_slurm_pretrain.sh>`__
 to launch the multi-node workload. Use the following steps to setup your environment:
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
    {% set docker = data.docker %}
    .. code-block:: shell

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.10.rst

@@ -24,17 +24,17 @@ Primus now supports the PyTorch torchtitan backend.
    <https://hub.docker.com/r/rocm/pytorch-training/>`__ Docker Hub registry will be
    deprecated soon in favor of `rocm/primus <https://hub.docker.com/r/rocm/primus>`__.
    The ``rocm/primus`` Docker containers will cover PyTorch training ecosystem frameworks,
-   including torchtitan and :doc:`Megatron-LM <../primus-megatron>`.
+   including torchtitan and :doc:`Megatron-LM <primus-megatron>`.
 
    Primus with the PyTorch torchtitan backend is designed to replace the
-   :doc:`ROCm PyTorch training <../pytorch-training>` workflow. See
+   :doc:`ROCm PyTorch training <pytorch-training>` workflow. See
-   :doc:`../pytorch-training` to see steps to run workloads without Primus.
+   :doc:`pytorch-training` to see steps to run workloads without Primus.
 
 AMD provides a ready-to-use Docker image for MI355X, MI350X, MI325X, and
 MI300X GPUs containing essential components for Primus and PyTorch training
 with Primus Turbo optimizations.
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
 
    .. tab-set::
 
@@ -61,7 +61,7 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
 Some instructions, commands, and training recommendations in this documentation might
 vary by model -- select one to get started.
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
 
    {% set model_groups = data.model_groups %}
    .. raw:: html
@@ -96,7 +96,7 @@ vary by model -- select one to get started.
 .. seealso::
 
    For additional workloads, including Llama 3.3, Llama 3.2, Llama 2, GPT OSS, Qwen, and Flux models,
-   see the documentation :doc:`../pytorch-training` (without Primus)
+   see the documentation :doc:`pytorch-training` (without Primus)
 
 .. _amd-primus-pytorch-performance-measurements-v2510:
 
@@ -122,7 +122,7 @@ doesn’t test configurations and run conditions outside those described.
 Pull the Docker image
 =====================
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
 
    Use the following command to pull the Docker image from Docker Hub.
 
@@ -134,11 +134,11 @@ Run training
 ============
 
 Once the setup is complete, choose between the following two workflows to start benchmarking training.
-For fine-tuning workloads and multi-node training examples, see :doc:`../pytorch-training` (without Primus).
+For fine-tuning workloads and multi-node training examples, see :doc:`pytorch-training` (without Primus).
 For best performance on MI325X, MI350X, and MI355X GPUs, you might need to
 tweak some configurations (such as batch sizes).
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
 
    {% set docker = data.docker %}
    {% set model_groups = data.model_groups %}

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.11.rst

@@ -1,422 +0,0 @@
-: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 Primus and PyTorch
-****************************************
-
-.. caution::
-
-   This documentation does not reflect the latest version of ROCm Primus PyTorch training
-   performance benchmark documentation. See :doc:`../primus-pytorch` for the latest version.
-
-`Primus <https://github.com/AMD-AGI/Primus>`__ is a unified and flexible
-LLM training framework designed to streamline training. It streamlines LLM
-training on AMD Instinct GPUs using a modular, reproducible configuration paradigm.
-Primus now supports the PyTorch torchtitan backend.
-
-.. note::
-
-   For a unified training solution on AMD GPUs with ROCm, the `rocm/pytorch-training
-   <https://hub.docker.com/r/rocm/pytorch-training/>`__ Docker Hub registry will be
-   deprecated soon in favor of `rocm/primus <https://hub.docker.com/r/rocm/primus>`__.
-   The ``rocm/primus`` Docker containers will cover PyTorch training ecosystem frameworks,
-   including torchtitan and :doc:`Megatron-LM <../primus-megatron>`.
-
-   Primus with the PyTorch torchtitan backend is designed to replace the
-   :doc:`ROCm PyTorch training <../pytorch-training>` workflow. See
-   :doc:`../pytorch-training` to see steps to run workloads without Primus.
-
-AMD provides a ready-to-use Docker image for MI355X, MI350X, MI325X, and
-MI300X GPUs containing essential components for Primus and PyTorch training
-with Primus Turbo optimizations.
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.11-benchmark-models.yaml
-
-   .. tab-set::
-
-      .. tab-item:: {{ data.docker.pull_tag }}
-         :sync: {{ data.docker.pull_tag }}
-
-         .. list-table::
-            :header-rows: 1
-
-            * - Software component
-              - Version
-
-            {% for component_name, component_version in data.docker.components.items() %}
-            * - {{ component_name }}
-              - {{ component_version }}
-            {% endfor %}
-
-.. _amd-primus-pytorch-model-support-v25.11:
-
-Supported models
-================
-
-The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X GPUs.
-Some instructions, commands, and training recommendations in this documentation might
-vary by model -- select one to get started.
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.11-benchmark-models.yaml
-
-   {% set model_groups = data.model_groups %}
-   .. raw:: html
-
-      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
-         <div class="row gx-0">
-            <div class="col-2 me-1 px-2 model-param-head">Model</div>
-            <div class="row col-10 pe-0">
-      {% for model_group in model_groups %}
-               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
-      {% endfor %}
-            </div>
-         </div>
-
-         <div class="row gx-0 pt-1">
-            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
-            <div class="row col-10 pe-0">
-      {% for model_group in model_groups %}
-         {% set models = model_group.models %}
-         {% for model in models %}
-            {% if models|length % 3 == 0 %}
-               <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
-            {% else %}
-               <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
-            {% endif %}
-         {% endfor %}
-      {% endfor %}
-            </div>
-         </div>
-      </div>
-
-.. seealso::
-
-   For additional workloads, including Llama 3.3, Llama 3.2, Llama 2, GPT OSS, Qwen, and Flux models,
-   see the documentation :doc:`../pytorch-training` (without Primus)
-
-.. _amd-primus-pytorch-performance-measurements-v25.11:
-
-System validation
-=================
-
-Before running AI workloads, it's important to validate that your AMD hardware is configured
-correctly and performing optimally.
-
-If you have already validated your system settings, including aspects like NUMA auto-balancing, you
-can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
-optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
-before starting training.
-
-To test for optimal performance, consult the recommended :ref:`System health benchmarks
-<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
-system's configuration.
-
-This Docker image is optimized for specific model configurations outlined
-below. Performance can vary for other training workloads, as AMD
-doesn’t test configurations and run conditions outside those described.
-
-Pull the Docker image
-=====================
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.11-benchmark-models.yaml
-
-   Use the following command to pull the Docker image from Docker Hub.
-
-   .. code-block:: shell
-
-      docker pull {{ data.docker.pull_tag }}
-
-Run training
-============
-
-Once the setup is complete, choose between the following two workflows to start benchmarking training.
-For fine-tuning workloads and multi-node training examples, see :doc:`../pytorch-training` (without Primus).
-For best performance on MI325X, MI350X, and MI355X GPUs, you might need to
-tweak some configurations (such as batch sizes).
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.11-benchmark-models.yaml
-
-   {% set docker = data.docker %}
-   {% set model_groups = data.model_groups %}
-
-   .. tab-set::
-
-      .. tab-item:: MAD-integrated benchmarking
-
-   {% for model_group in model_groups %}
-      {% for model in model_group.models %}
-
-         .. container:: model-doc {{ model.mad_tag }}
-
-            The following run command is tailored to {{ model.model }}.
-            See :ref:`amd-primus-pytorch-model-support-v25.11` to switch to another available model.
-
-            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
-               directory and install the required packages on the host machine.
-
-               .. code-block:: shell
-
-                  git clone https://github.com/ROCm/MAD
-                  cd MAD
-                  pip install -r requirements.txt
-
-            2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
-               using one node with the {{ model.precision }} data type on the host machine.
-
-               .. code-block:: shell
-
-                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
-                  madengine run \
-                      --tags {{ model.mad_tag }} \
-                      --keep-model-dir \
-                      --live-output \
-                      --timeout 28800
-
-               MAD launches a Docker container with the name
-               ``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
-               model are collected in ``~/MAD/perf.csv``.
-
-      {% endfor %}
-   {% endfor %}
-
-      .. tab-item:: Primus benchmarking
-
-   {% for model_group in model_groups %}
-      {% for model in model_group.models %}
-
-         .. container:: model-doc {{ model.mad_tag }}
-
-            The following run commands are tailored to {{ model.model }}.
-            See :ref:`amd-primus-pytorch-model-support-v25.11` to switch to another available model.
-
-            .. rubric:: Download the Docker image and required packages
-
-            1. Pull the ``{{ docker.pull_tag }}`` Docker image from Docker Hub.
-
-               .. code-block:: shell
-
-                  docker pull {{ docker.pull_tag }}
-
-            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 $HOME/.ssh:/root/.ssh \
-                      --shm-size 64G \
-                      --name training_env \
-                      {{ docker.pull_tag }}
-
-               Use these commands if you exit the ``training_env`` container and need to return to it.
-
-               .. code-block:: shell
-
-                  docker start training_env
-                  docker exec -it training_env bash
-
-               The Docker container hosts verified commit ``c4c083de`` of the `Primus
-               <https://github.com/AMD-AGI/Primus/tree/c4c083de64ba3e8f19ccc9629411267108931f9e/>`__ repository.
-
-            .. rubric:: Prepare training datasets and dependencies
-
-            The following benchmarking examples require downloading models and datasets
-            from Hugging Face. To ensure successful access to gated repos, set your
-            ``HF_TOKEN``.
-
-            .. code-block:: shell
-
-               export HF_TOKEN=$your_personal_hugging_face_access_token
-
-            .. rubric:: Pretraining
-
-            To get started, navigate to the ``Primus`` directory in your container.
-
-            .. code-block::
-
-               cd /workspace/Primus
-
-            Now, to start the pretraining benchmark, use the ``run_pretrain.sh`` script
-            included with Primus with the appropriate options.
-
-            .. rubric:: Benchmarking examples
-
-            .. container:: model-doc primus_pyt_train_llama-3.1-8b
-
-               Use the following command to run train Llama 3.1 8B with BF16 precision using Primus torchtitan.
-
-               .. tab-set::
-
-                  .. tab-item:: MI355X and MI350X
-                     :sync: MI355X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI355X/llama3.1_8B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-
-                  .. tab-item:: MI325X
-                     :sync: MI325X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh --training.local_batch_size 6
-
-                  .. tab-item:: MI300X
-                     :sync: MI300X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-
-               To train Llama 3.1 8B with FP8 precision, use the following command.
-
-               .. tab-set::
-
-                  .. tab-item:: MI355X and MI350X
-                     :sync: MI355X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI355X/llama3.1_8B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-
-                  .. tab-item:: MI325X
-                     :sync: MI325X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh --training.local_batch_size 7
-
-                  .. tab-item:: MI300X
-                     :sync: MI300X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-
-            .. container:: model-doc primus_pyt_train_llama-3.1-70b
-
-               Use the following command to run train Llama 3.1 70B with BF16 precision using Primus torchtitan.
-
-               .. tab-set::
-
-                  .. tab-item:: MI355X and MI350X
-                     :sync: MI355X and MI300X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-
-                  .. tab-item:: MI325X
-                     :sync: MI325X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh --training.local_batch_size 6
-
-                  .. tab-item:: MI300X
-                     :sync: MI300X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-
-               To train Llama 3.1 70B with FP8 precision, use the following command.
-
-               .. tab-set::
-
-                  .. tab-item:: MI355X and MI350X
-                     :sync: MI355X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI355X/llama3.1_70B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-
-                  .. tab-item:: MI325X
-                     :sync: MI325X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh --training.local_batch_size 5
-
-                  .. tab-item:: MI300X
-                     :sync: MI300X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-
-            .. container:: model-doc primus_pyt_train_deepseek-v3-16b
-
-               Use the following command to run train DeepSeek V3 16B with BF16 precision using Primus torchtitan.
-
-               .. tab-set::
-
-                  .. tab-item:: MI355X and MI350X
-                     :sync: MI355X and MI300X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI355X/deepseek_v3_16b-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-
-                  .. tab-item:: MI325X
-                     :sync: MI325X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
-                        bash examples/run_pretrain.sh --training.local_batch_size 10
-
-                  .. tab-item:: MI300X
-                     :sync: MI300X
-
-                     .. code-block:: shell
-
-                        EXP=examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
-                        bash examples/run_pretrain.sh
-      {% endfor %}
-   {% endfor %}
-
-Further reading
-===============
-
-- For an introduction to Primus, see `Primus: A Lightweight, Unified Training
-  Framework for Large Models on AMD GPUs <https://rocm.blogs.amd.com/software-tools-optimization/primus/README.html>`__.
-
-- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
-
-- To learn more about system settings and management practices to configure your system for
-  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
-
-- For a list of other ready-made Docker images for AI with ROCm, see
-  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
-
-Previous versions
-=================
-
-See :doc:`pytorch-training-history` to find documentation for previous releases
-of the ``ROCm/pytorch-training`` Docker image.

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

@@ -16,30 +16,21 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
      - Components
      - Resources
 
-   * - v26.1 (latest)
-     -
-       * ROCm 7.1.0
-       * PyTorch 2.10.0.dev20251112+rocm7.1
-     -
-       * :doc:`Primus PyTorch training documentation <../primus-megatron>`
-       * :doc:`PyTorch training (legacy) documentation <../megatron-lm>`
-       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d>`__
-
    * - v25.11
      -
        * ROCm 7.1.0
        * PyTorch 2.10.0.dev20251112+rocm7.1
      -
-       * :doc:`Primus PyTorch training documentation <primus-pytorch-v25.11>`
+       * :doc:`Primus PyTorch Training documentation <../primus-pytorch>`
-       * :doc:`PyTorch training (legacy) documentation <pytorch-training-v25.11>`
+       * :doc:`PyTorch training (legacy) documentation <../pytorch-training>`
-       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3>`__
+       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197>`__
 
    * - v25.10
      -
        * ROCm 7.1.0
        * PyTorch 2.10.0.dev20251112+rocm7.1
      -
-       * :doc:`Primus PyTorch training documentation <primus-pytorch-v25.10>`
+       * :doc:`Primus PyTorch Training documentation <primus-pytorch-v25.10>`
        * :doc:`PyTorch training (legacy) documentation <pytorch-training-v25.10>`
        * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197>`__
 
@@ -49,7 +40,7 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
        * Primus 0.3.0
        * PyTorch 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
      -
-       * :doc:`Primus PyTorch training documentation <primus-pytorch-v25.9>`
+       * :doc:`Primus PyTorch Training documentation <primus-pytorch-v25.9>`
        * :doc:`PyTorch training (legacy) documentation <pytorch-training-v25.9>`
        * `Docker Hub (gfx950) <https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6>`__
        * `Docker Hub (gfx942) <https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357>`__
@@ -59,7 +50,7 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
        * ROCm 6.4.3
        * PyTorch 2.8.0a0+gitd06a406
      -
-       * :doc:`Primus PyTorch training documentation <primus-pytorch-v25.8>`
+       * :doc:`Primus PyTorch Training documentation <primus-pytorch-v25.8>`
        * :doc:`PyTorch training (legacy) documentation <pytorch-training-v25.8>`
        * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.8/images/sha256-5082ae01d73fec6972b0d84e5dad78c0926820dcf3c19f301d6c8eb892e573c5>`__
 

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.10.rst

@@ -30,7 +30,7 @@ environment for fine-tuning and pretraining a model on AMD Instinct MI325X
 and MI300X GPUs. It includes the following software components to accelerate
 training workloads:
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
 
    .. tab-set::
 
@@ -58,7 +58,7 @@ MI355X, MI350X, MI325X, and MI300X GPUs. Some instructions, commands, and
 training recommendations in this documentation might vary by model -- select
 one to get started.
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
 
    {% set model_groups = data.model_groups %}
    .. raw:: html
@@ -94,7 +94,7 @@ one to get started.
 
 The following table lists supported training modes per model.
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
 
    {% set model_groups = data.model_groups %}
    .. dropdown:: Supported training modes
@@ -164,7 +164,7 @@ doesn’t test configurations and run conditions outside those described.
 Run training
 ============
 
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.10-benchmark-models.yaml
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
 
    {% set docker = data.docker %}
    {% set model_groups = data.model_groups %}

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.11.rst

@@ -1,669 +0,0 @@
-: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 on ROCm
-**************************************
-
-.. caution::
-
-   This documentation does not reflect the latest version of ROCm PyTorch training
-   performance benchmark documentation. See :doc:`../pytorch-training` for the latest version.
-
-.. note::
-
-   For a unified training solution on AMD GPUs with ROCm, the `rocm/pytorch-training
-   <https://hub.docker.com/r/rocm/pytorch-training/>`__ Docker Hub registry will be
-   deprecated soon in favor of `rocm/primus <https://hub.docker.com/r/rocm/primus>`__.
-   The ``rocm/primus`` Docker containers will cover PyTorch training ecosystem frameworks,
-   including torchtitan and :doc:`Megatron-LM <../primus-megatron>`.
-
-   See :doc:`../primus-pytorch` for details.
-
-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 image provides a prebuilt optimized
-environment for fine-tuning and pretraining a model on AMD Instinct MI325X
-and MI300X GPUs. It includes the following software components to accelerate
-training workloads:
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.11-benchmark-models.yaml
-
-   .. tab-set::
-
-      .. tab-item:: {{ data.docker.pull_tag }}
-         :sync: {{ data.docker.pull_tag }}
-
-         .. list-table::
-            :header-rows: 1
-
-            * - Software component
-              - Version
-
-            {% for component_name, component_version in data.docker.components.items() %}
-            * - {{ component_name }}
-              - {{ component_version }}
-            {% endfor %}
-
-.. _amd-pytorch-training-model-support-v25.11:
-
-Supported models
-================
-
-The following models are pre-optimized for performance on the AMD Instinct
-MI355X, MI350X, MI325X, and MI300X GPUs. Some instructions, commands, and
-training recommendations in this documentation might vary by model -- select
-one to get started.
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.11-benchmark-models.yaml
-
-   {% set model_groups = data.model_groups %}
-   .. raw:: html
-
-      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
-         <div class="row gx-0">
-            <div class="col-2 me-1 px-2 model-param-head">Model</div>
-            <div class="row col-10 pe-0">
-      {% for model_group in model_groups %}
-               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
-      {% endfor %}
-            </div>
-         </div>
-
-         <div class="row gx-0 pt-1">
-            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
-            <div class="row col-10 pe-0">
-      {% for model_group in model_groups %}
-         {% set models = model_group.models %}
-         {% for model in models %}
-            {% if models|length % 3 == 0 %}
-               <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
-            {% else %}
-               <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
-            {% endif %}
-         {% endfor %}
-      {% endfor %}
-            </div>
-         </div>
-      </div>
-
-.. _amd-pytorch-training-supported-training-modes-v25.11:
-
-The following table lists supported training modes per model.
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.11-benchmark-models.yaml
-
-   {% set model_groups = data.model_groups %}
-   .. dropdown:: Supported training modes
-
-      .. list-table::
-         :header-rows: 1
-
-         * - Model
-           - Supported training modes
-
-      {% for model_group in model_groups %}
-         {% set models = model_group.models %}
-         {% for model in models %}
-         {% if model.training_modes %}
-         * - {{ model.model }}
-           - ``{{ model.training_modes | join('``, ``') }}``
-
-         {% endif %}
-         {% endfor %}
-      {% endfor %}
-
-      .. note::
-
-         Some model and fine-tuning combinations are not listed. This is
-         because the `upstream torchtune repository <https://github.com/pytorch/torchtune>`__
-         doesn't provide default YAML configurations for them.
-         For advanced usage, you can create a custom configuration to enable
-         unlisted fine-tuning methods by using an existing file in the
-         ``/workspace/torchtune/recipes/configs`` directory as a template.
-
-.. _amd-pytorch-training-performance-measurements-v25.11:
-
-Performance measurements
-========================
-
-To evaluate performance, the
-`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
-page provides reference throughput and latency measurements for training
-popular AI models.
-
-.. note::
-
-   The performance data presented in
-   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
-   should not be interpreted as the peak performance achievable by AMD
-   Instinct MI325X and MI300X GPUs or ROCm software.
-
-System validation
-=================
-
-Before running AI workloads, it's important to validate that your AMD hardware is configured
-correctly and performing optimally.
-
-If you have already validated your system settings, including aspects like NUMA auto-balancing, you
-can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
-optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
-before starting training.
-
-To test for optimal performance, consult the recommended :ref:`System health benchmarks
-<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
-system's configuration.
-
-This Docker image is optimized for specific model configurations outlined
-below. Performance can vary for other training workloads, as AMD
-doesn’t test configurations and run conditions outside those described.
-
-Run training
-============
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.11-benchmark-models.yaml
-
-   {% set docker = data.docker %}
-   {% set model_groups = data.model_groups %}
-
-   Once the setup is complete, choose between two options to start benchmarking training:
-
-   .. tab-set::
-
-      .. tab-item:: MAD-integrated benchmarking
-
-   {% for model_group in model_groups %}
-      {% for model in model_group.models %}
-
-         .. container:: model-doc {{ model.mad_tag }}
-
-            The following run command is tailored to {{ model.model }}.
-            See :ref:`amd-pytorch-training-model-support-v25.11` to switch to another available model.
-
-            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
-               directory and install the required packages on the host machine.
-
-               .. code-block:: shell
-
-                  git clone https://github.com/ROCm/MAD
-                  cd MAD
-                  pip install -r requirements.txt
-
-            2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
-               using one node with the {{ model.precision }} data type on the host machine.
-
-               .. code-block:: shell
-
-                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
-                  madengine run \
-                      --tags {{ model.mad_tag }} \
-                      --keep-model-dir \
-                      --live-output \
-                      --timeout 28800
-
-               MAD launches a Docker container with the name
-               ``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
-               model are collected in ``~/MAD/perf.csv``.
-
-      {% endfor %}
-   {% endfor %}
-
-      .. tab-item:: Standalone benchmarking
-
-   {% for model_group in model_groups %}
-      {% for model in model_group.models %}
-
-         .. container:: model-doc {{ model.mad_tag }}
-
-            The following commands are tailored to {{ model.model }}.
-            See :ref:`amd-pytorch-training-model-support-v25.11` to switch to another available model.
-
-      {% endfor %}
-   {% endfor %}
-
-         .. rubric:: 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 {{ docker.pull_tag }}
-
-         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 $HOME/.ssh:/root/.ssh \
-                   --shm-size 64G \
-                   --name training_env \
-                   {{ docker.pull_tag }}
-
-            Use these commands if you exit the ``training_env`` container and need to return to it.
-
-            .. code-block:: shell
-
-               docker start training_env
-               docker exec -it training_env bash
-
-         3. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
-            repository and navigate to the benchmark scripts directory
-            ``/workspace/MAD/scripts/pytorch_train``.
-
-            .. code-block:: shell
-
-               git clone https://github.com/ROCm/MAD
-               cd MAD/scripts/pytorch_train
-
-         .. rubric:: Prepare training datasets and dependencies
-
-         1. The following benchmarking examples require downloading models and datasets
-            from Hugging Face. To ensure successful access to gated repos, set your
-            ``HF_TOKEN``.
-
-            .. code-block:: shell
-
-               export HF_TOKEN=$your_personal_hugging_face_access_token
-
-         2. Run the setup script to install libraries and datasets needed for benchmarking.
-
-            .. code-block:: shell
-
-               ./pytorch_benchmark_setup.sh
-
-            .. container:: model-doc pyt_train_llama-3.1-8b
-
-               ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
-
-               .. list-table::
-                  :header-rows: 1
-
-                  * - Library
-                    - Reference
-
-                  * - ``accelerate``
-                    - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
-
-                  * - ``datasets``
-                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
-
-            .. container:: model-doc pyt_train_llama-3.1-70b
-
-               ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
-
-               .. list-table::
-                  :header-rows: 1
-
-                  * - Library
-                    - Reference
-
-                  * - ``datasets``
-                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
-
-                  * - ``torchdata``
-                    - `TorchData <https://meta-pytorch.org/data/beta/index.html#torchdata>`__
-
-                  * - ``tomli``
-                    - `Tomli <https://pypi.org/project/tomli/>`__
-
-                  * - ``tiktoken``
-                    - `tiktoken <https://github.com/openai/tiktoken>`__
-
-                  * - ``blobfile``
-                    - `blobfile <https://pypi.org/project/blobfile/>`__
-
-                  * - ``tabulate``
-                    - `tabulate <https://pypi.org/project/tabulate/>`__
-
-                  * - ``wandb``
-                    - `Weights & Biases <https://github.com/wandb/wandb>`__
-
-                  * - ``sentencepiece``
-                    - `SentencePiece <https://github.com/google/sentencepiece>`__ 0.2.0
-
-                  * - ``tensorboard``
-                    - `TensorBoard <https://www.tensorflow.org/tensorboard>`__ 2.18.0
-
-            .. container:: model-doc pyt_train_flux
-
-               ``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
-
-               .. list-table::
-                  :header-rows: 1
-
-                  * - Library
-                    - Reference
-
-                  * - ``accelerate``
-                    - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
-
-                  * - ``datasets``
-                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`__ 3.2.0
-
-                  * - ``sentencepiece``
-                    - `SentencePiece <https://github.com/google/sentencepiece>`__ 0.2.0
-
-                  * - ``tensorboard``
-                    - `TensorBoard <https://www.tensorflow.org/tensorboard>`__ 2.18.0
-
-                  * - ``csvkit``
-                    - `csvkit <https://csvkit.readthedocs.io/en/latest/>`__ 2.0.1
-
-                  * - ``deepspeed``
-                    - `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`__ 0.16.2
-
-                  * - ``diffusers``
-                    - `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`__ 0.31.0
-
-                  * - ``GitPython``
-                    - `GitPython <https://github.com/gitpython-developers/GitPython>`__ 3.1.44
-
-                  * - ``opencv-python-headless``
-                    - `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`__ 4.10.0.84
-
-                  * - ``peft``
-                    - `PEFT <https://huggingface.co/docs/peft/en/index>`__ 0.14.0
-
-                  * - ``protobuf``
-                    - `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`__ 5.29.2
-
-                  * - ``pytest``
-                    - `PyTest <https://docs.pytest.org/en/stable/>`__ 8.3.4
-
-                  * - ``python-dotenv``
-                    - `python-dotenv <https://pypi.org/project/python-dotenv/>`__ 1.0.1
-
-                  * - ``seaborn``
-                    - `Seaborn <https://seaborn.pydata.org/>`__ 0.13.2
-
-                  * - ``transformers``
-                    - `Transformers <https://huggingface.co/docs/transformers/en/index>`__ 4.47.0
-
-            ``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
-
-            * `frank-chieng/chinese_architecture_siheyuan <https://huggingface.co/datasets/frank-chieng/chinese_architecture_siheyuan>`__
-
-   {% for model_group in model_groups %}
-      {% for model in model_group.models %}
-         {% set training_modes = model.training_modes %}
-         {% set training_mode_descs = {
-            "pretrain": "Benchmark pre-training.",
-            "HF_pretrain": "Llama 3.1 8B pre-training with FP8 precision."
-         } %}
-         {% set available_modes = training_modes | select("in", ["pretrain", "HF_pretrain"]) | list %}
-         {% if available_modes %}
-
-         .. container:: model-doc {{ model.mad_tag }}
-
-            .. rubric:: Pretraining
-
-            To start the pre-training benchmark, use the following command with the
-            appropriate options. See the following list of options and their descriptions.
-
-            {% if model.mad_tag == "pyt_train_dlrm" %}
-
-            1. Go to the DLRM directory.
-
-               .. code-block:: shell
-
-                  cd /workspace/DLRMBenchmark
-
-            2. To run the single node training benchmark for DLRM-v2 with TF32 precision,
-               run the following script.
-
-               .. code-block:: shell
-
-                  ./launch_training_single_node.sh
-
-               To run with MAD within the Docker container, use the following command.
-
-               .. code-block:: shell
-
-                  ./pytorch_benchmark_report.sh -t pretrain -m DLRM
-
-            {% else %}
-
-            .. code-block:: shell
-
-               ./pytorch_benchmark_report.sh -t {% if available_modes | length == 1 %}{{ available_modes[0] }}{% else %}$training_mode{% endif %} \
-                   -m {{ model.model_repo }} \
-                   -p $datatype \
-                   -s $sequence_length
-
-            {% if model.mad_tag == "pyt_train_flux" %}
-            .. container:: model-doc {{ model.mad_tag }}
-
-               .. note::
-
-                  Currently, FLUX models are not supported out-of-the-box on this Docker.
-                  To use FLUX, refer to ``rocm/pytorch-training`` Docker: :doc:`pytorch-training-v25.6`
-
-                  Occasionally, downloading the Flux dataset might fail. In the event of this
-                  error, manually download it from Hugging Face at
-                  `black-forest-labs/FLUX.1-dev <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
-                  and save it to `/workspace/FluxBenchmark`. This ensures that the test script can access
-                  the required dataset.
-            {% endif %}
-
-            .. list-table::
-               :header-rows: 1
-
-               * - Name
-                 - Options
-                 - Description
-
-               {% for mode in available_modes %}
-               * - {% if loop.first %}``$training_mode``{% endif %}
-                 - ``{{ mode }}``
-                 - {{ training_mode_descs[mode] }}
-               {% endfor %}
-
-               * - ``$datatype``
-                 - ``BF16``{% if model.mad_tag == "pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
-                 - Only Llama 3.1 8B supports FP8 precision.
-
-               * - ``$sequence_length``
-                 - Sequence length for the language model.
-                 - Between 2048 and 8192. 8192 by default.
-            {% endif %}
-         {% endif %}
-
-         {% set training_modes = model.training_modes %}
-         {% set training_mode_descs = {
-            "posttrain": "Benchmark post-training.",
-         } %}
-         {% set available_modes = training_modes | select("in", ["posttrain"]) | list %}
-         {% if available_modes %}
-
-         .. container:: model-doc {{ model.mad_tag }}
-
-            .. rubric:: Post-training
-
-            To start the post-training benchmark, use the following command with the
-            appropriate options. See the following list of options and their descriptions.
-
-            .. code-block:: shell
-
-               ./pytorch_benchmark_report.sh -t {% if available_modes | length == 1 %}{{ available_modes[0] }}{% else %}$training_mode{% endif %} \
-                   -m {{ model.model_repo }} \
-                   -p $datatype \
-                   -s $sequence_length
-
-            .. list-table::
-               :header-rows: 1
-
-               * - Name
-                 - Options
-                 - Description
-
-               {% for mode in available_modes %}
-               * - {% if loop.first %}``$training_mode``{% endif %}
-                 - ``{{ mode }}``
-                 - {{ training_mode_descs[mode] }}
-               {% endfor %}
-
-               * - ``$datatype``
-                 - ``BF16``{% if model.mad_tag == "pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
-                 - Only Llama 3.1 8B supports FP8 precision.
-
-               * - ``$sequence_length``
-                 - Sequence length for the language model.
-                 - Between 2048 and 8192. 8192 by default.
-         {% endif %}
-
-         {% set training_mode_descs = {
-            "finetune_fw": "Full weight fine-tuning (BF16 and FP8 supported).",
-            "finetune_lora": "LoRA fine-tuning (BF16 supported).",
-            "finetune_qlora": "QLoRA fine-tuning (BF16 supported).",
-            "HF_finetune_lora": "LoRA fine-tuning with Hugging Face PEFT.",
-         } %}
-         {% set available_modes = training_modes | select("in", ["finetune_fw", "finetune_lora", "finetune_qlora", "HF_finetune_lora"]) | list %}
-         {% if available_modes %}
-         .. container:: model-doc {{ model.mad_tag }}
-
-            .. rubric:: Fine-tuning
-
-            To start the fine-tuning benchmark, use the following command with the
-            appropriate options. See the following list of options and their descriptions.
-            See :ref:`supported training modes <amd-pytorch-training-supported-training-modes-v25.11>`.
-
-            .. code-block:: shell
-
-               ./pytorch_benchmark_report.sh -t $training_mode \
-                   -m {{ model.model_repo }} \
-                   -p $datatype \
-                   -s $sequence_length
-
-            .. list-table::
-               :header-rows: 1
-
-               * - Name
-                 - Options
-                 - Description
-
-               {% for mode in available_modes %}
-               * - {% if loop.first %}``$training_mode``{% endif %}
-                 - ``{{ mode }}``
-                 - {{ training_mode_descs[mode] }}
-               {% endfor %}
-
-               * - ``$datatype``
-                 - ``BF16``{% if "finetune_fw" in available_modes %} or ``FP8``{% endif %}
-                 - All models support BF16.{% if "finetune_fw" in available_modes %} FP8 is only available for full weight fine-tuning.{% endif %}
-
-               * - ``$sequence_length``
-                 - Between 2048 and 16384.
-                 - Sequence length for the language model.
-
-            {% if model.mad_tag in ["pyt_train_llama3.2-vision-11b", "pyt_train_llama-3.2-vision-90b"] %}
-            .. note::
-
-               For LoRA and QLoRA support with vision models (Llama 3.2 11B and 90B),
-               use the following torchtune commit for compatibility:
-
-               .. code-block:: shell
-
-                  git checkout 48192e23188b1fc524dd6d127725ceb2348e7f0e
-
-            {% elif model.mad_tag in ["pyt_train_llama-2-7b", "pyt_train_llama-2-13b", "pyt_train_llama-2-70b"] %}
-            .. note::
-
-               You might encounter the following error with Llama 2: ``ValueError: seq_len (16384) of
-               input tensor should be smaller than max_seq_len (4096)``.
-               This error indicates that an input sequence is longer than the model's maximum context window.
-
-               Ensure your tokenized input does not exceed the model's ``max_seq_len`` (4096
-               tokens in this case). You can resolve this by truncating the input or splitting
-               it into smaller chunks before passing it to the model.
-
-               Note on reproducibility: The results in this guide are based on
-               commit ``b4c98ac`` from the upstream
-               `<https://github.com/pytorch/torchtune>`__ repository. For the
-               latest updates, you can use the main branch.
-
-            {% endif %}
-         {% endif %}
-      {% endfor %}
-   {% endfor %}
-
-            .. rubric:: Benchmarking examples
-
-            For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
-
-.. _amd-pytorch-training-multinode-examples-v25.11:
-
-Multi-node training
--------------------
-
-Refer to :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your environment for multi-node
-training. See :ref:`rocm-for-ai-multi-node-setup-pyt-train-example` for example Slurm run commands.
-
-Pre-training
-~~~~~~~~~~~~
-
-Multi-node training with torchtitan is supported. The provided SLURM script is pre-configured for Llama 3 70B.
-
-To launch the training job on a SLURM cluster for Llama 3 70B, run the following commands from the MAD repository.
-
-.. code-block:: shell
-
-   # In the MAD repository
-   cd scripts/pytorch_train
-   sbatch run_slurm_train.sh
-
-Fine-tuning
-~~~~~~~~~~~
-
-Multi-node training with torchtune is supported. The provided SLURM script is pre-configured for Llama 3.3 70B.
-
-To launch the training job on a SLURM cluster for Llama 3.3 70B, run the following commands from the MAD repository.
-
-.. code-block:: shell
-
-   huggingface-cli login # Get access to HF Llama model space
-   huggingface-cli download meta-llama/Llama-3.3-70B-Instruct --local-dir ./models/Llama-3.3-70B-Instruct # Download the Llama 3.3 model locally
-   # In the MAD repository
-   cd scripts/pytorch_train
-   sbatch Torchtune_Multinode.sh
-
-.. note::
-
-   Information regarding benchmark setup:
-
-   * By default, Llama 3.3 70B is fine-tuned using ``alpaca_dataset``.
-   * You can adjust the torchtune `YAML configuration file
-     <https://github.com/pytorch/torchtune/blob/main/recipes/configs/llama3_3/70B_full_multinode.yaml>`__
-     if you're using a different model.
-   * The number of nodes and other parameters can be tuned in the SLURM script ``Torchtune_Multinode.sh``.
-   * Set the ``mounting_paths`` inside the SLURM script.
-
-Once the run is finished, you can find the log files in the ``result_torchtune/`` directory.
-
-Further reading
-===============
-
-- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
-
-- To learn more about system settings and management practices to configure your system for
-  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
-
-- For a list of other ready-made Docker images for AI with ROCm, see
-  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
-
-Previous versions
-=================
-
-See :doc:`pytorch-training-history` to find documentation for previous releases
-of the ``ROCm/pytorch-training`` Docker image.

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

@@ -47,7 +47,7 @@ Megatron-LM.
               - {{ component_version }}
             {% endfor %}
 
-.. _amd-primus-megatron-lm-model-support-v26.01:
+.. _amd-primus-megatron-lm-model-support-v25.11:
 
 Supported models
 ================
@@ -108,7 +108,7 @@ To test for optimal performance, consult the recommended :ref:`System health ben
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 
-.. _mi300x-amd-primus-megatron-lm-training-v26.01:
+.. _mi300x-amd-primus-megatron-lm-training-v25.11:
 
 Environment setup
 =================
@@ -118,7 +118,7 @@ Environment setup
    Use the following instructions to set up the environment, configure the script to train models, and
    reproduce the benchmark results on AMD Instinct GPUs.
 
-.. _amd-primus-megatron-lm-requirements-v26.01:
+.. _amd-primus-megatron-lm-requirements-v25.11:
 
 Pull the Docker image
 
@@ -157,16 +157,16 @@ Pull the Docker image
          docker start primus_training_env
          docker exec -it primus_training_env bash
 
-The Docker container hosts verified commit ``9c529cd4`` of the `Primus
+The Docker container hosts verified commit ``c4c083de`` of the `Primus
-<https://github.com/AMD-AGI/Primus/tree/9c529cd4a934a68a880ede036c3e97b792e38167>`__ repository.
+<https://github.com/AMD-AGI/Primus/tree/c4c083de64ba3e8f19ccc9629411267108931f9e/>`__ repository.
 
-.. _amd-primus-megatron-lm-environment-setup-v26.01:
+.. _amd-primus-megatron-lm-environment-setup-v25.11:
 
 Configuration
 =============
 
 Primus defines a training configuration in YAML for each model in
-`examples/megatron/configs <https://github.com/AMD-AGI/Primus/tree/9c529cd4a934a68a880ede036c3e97b792e38167/examples/megatron/configs>`__.
+`examples/megatron/configs <https://github.com/AMD-AGI/Primus/tree/c4c083de64ba3e8f19ccc9629411267108931f9e/examples/megatron/configs>`__.
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
@@ -207,7 +207,7 @@ You can use either mock data or real data for training.
 
   Ensure that the files are accessible inside the Docker container.
 
-.. _amd-primus-megatron-lm-tokenizer-v26.01:
+.. _amd-primus-megatron-lm-tokenizer-v25.11:
 
 Tokenizer
 ---------
@@ -220,7 +220,15 @@ right permissions to access the tokenizer for each model.
    # Export your HF_TOKEN in the workspace
    export HF_TOKEN=<your_hftoken>
 
-.. _amd-primus-megatron-lm-run-training-v26.01:
+.. note::
+
+   In Primus, each model uses a tokenizer from Hugging Face. For example, Llama
+   3.1 8B model uses ``tokenizer_model: meta-llama/Llama-3.1-8B`` and
+   ``tokenizer_type: Llama3Tokenizer`` defined in the `llama3.1-8B model
+   <https://github.com/AMD-AGI/Primus/blob/e16b27bf6c1b2798f38848fc574fee60d9a9b902/examples/megatron/configs/llama3.1_8B-pretrain.yaml>`__
+   definition.
+
+.. _amd-primus-megatron-lm-run-training-v25.11:
 
 Run training
 ============
@@ -244,7 +252,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.3 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run pre-training for Llama 3.3 70B BF16, run:
 
@@ -255,10 +263,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/llama3.3_70B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama3.3_70B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/llama3.3_70B-BF16-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -270,16 +276,14 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/llama3.3_70B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama3.3_70B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/llama3.3_70B-BF16-pretrain.yaml
 
 .. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.1 8B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run pre-training for Llama 3.1 8B FP8, run:
 
@@ -290,10 +294,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/llama3.1_8B-FP8-pretrain.yaml \
-              --log_file /tmp/primus_llama3.1_8B_fp8.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/llama3.1_8B-FP8-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -305,10 +307,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
-              --log_file /tmp/primus_llama3.1_8B_fp8.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml
 
    For Llama 3.1 8B BF16, use the following command:
 
@@ -319,10 +319,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/llama3.1_BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama3.1_8B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/llama3.1_8B-BF16-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -334,16 +332,14 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama3.1_8B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml
 
 .. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-70b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.1 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run pre-training for Llama 3.1 70B BF16, run:
 
@@ -354,10 +350,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama3.1_70B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -369,10 +363,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama3.1_70B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml
 
    To run the training on a single node for Llama 3.1 70B FP8, use the following command.
 
@@ -389,10 +381,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/llama3.1_70B-FP8-pretrain.yaml \
-              --log_file /tmp/primus_llama3.1_70B_fp8.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/llama3.1_70B-FP8-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -404,10 +394,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
-              --log_file /tmp/primus_llama3.1_70B_fp8_proxy.log \
+            bash ./examples/run_pretrain.sh \
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
                 --train_iters 50 \
                 --num_layers 40 \
                 --fp8 hybrid \
@@ -417,7 +405,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run pre-training for Llama 2 7B FP8, run:
 
@@ -428,10 +416,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/llama2_7B-FP8-pretrain.yaml \
-              --log_file /tmp/primus_llama2_7B_fp8.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/llama2_7B-FP8-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -443,10 +429,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/llama2_7B-FP8-pretrain.yaml \
-              --log_file /tmp/primus_llama2_7B_fp8.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/llama2_7B-FP8-pretrain.yaml
 
    To run pre-training for Llama 2 7B BF16, run:
 
@@ -457,10 +441,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/llama2_7B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama2_7B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/llama2_7B-BF16-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -472,16 +454,14 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/llama2_7B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama2_7B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/llama2_7B-BF16-pretrain.yaml
 
 .. container:: model-doc primus_pyt_megatron_lm_train_llama-2-70b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run pre-training for Llama 2 70B BF16, run:
 
@@ -492,10 +472,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/llama2_70B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama2_70B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/llama2_70B-BF16-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -507,16 +485,14 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/llama2_70B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_llama2_70B.log \
+            bash ./examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/llama2_70B-BF16-pretrain.yaml
 
 .. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v3-proxy
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to DeepSeek-V3.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run training on a single node for DeepSeek-V3 (MoE with expert parallel) BF16 with 3-layer proxy,
    use the following command:
@@ -528,10 +504,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/deepseek_v3-BF16-pretrain.yaml \
-              --log_file /tmp/primus_deepseek_v3_proxy.log \
+            bash examples/run_pretrain.sh \
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/deepseek_v3-BF16-pretrain.yaml \
                 --num_layers 3 \
                 --moe_layer_freq 1 \
                 --train_iters 50 \
@@ -548,21 +522,17 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/deepseek_v3-BF16-pretrain.yaml \
-              --log_file /tmp/primus_deepseek_v3_proxy.log \
+            bash examples/run_pretrain.sh \
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/deepseek_v3-BF16-pretrain.yaml \
                 --num_layers 3 \
                 --moe_layer_freq 1 \
-              --micro_batch_size 3 \
-              --global_batch_size 192 \
                 --train_iters 50
 
 .. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to DeepSeek-V2-Lite.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run training on a single node for DeepSeek-V2-Lite (MoE with expert parallel) BF16,
    use the following command:
@@ -574,10 +544,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/deepseek_v2_lite-BF16-pretrain.yaml \
-              --log_file /tmp/primus_deepseek_v2_lite.log \
+            bash examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs//MI355X/deepseek_v2_lite-BF16-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -589,16 +557,14 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/deepseek_v2_lite-BF16-pretrain.yaml \
-              --log_file /tmp/primus_deepseek_v2_lite.log \
+            bash examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/deepseek_v2_lite-BF16-pretrain.yaml
 
 .. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x7b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Mixtral 8x7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
    use the following command:
@@ -610,10 +576,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/mixtral_8x7B_v0.1-BF16-pretrain.yaml \
-              --log_file /tmp/primus_mixtral_8x7B.log \
+            bash examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/mixtral_8x7B_v0.1-BF16-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -625,16 +589,15 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/mixtral_8x7B_v0.1-BF16-pretrain.yaml \
-              --log_file /tmp/primus_mixtral_8x7B.log \
+            bash examples/run_pretrain.sh \
-              -- train pretrain \
+                --train_iters 50
-              --config examples/megatron/configs/MI300X/mixtral_8x7B_v0.1-BF16-pretrain.yaml
 
 .. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Mixtral 8x22B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run training on a single node for Mixtral 8x22B BF16 (MoE with expert parallel) 4-layer proxy,
    use the following command:
@@ -646,10 +609,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/mixtral_8x22B_v0.1-BF16-pretrain.yaml \
-              --log_file /tmp/primus_mixtral_8x22B_proxy.log \
+            bash examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/mixtral_8x22B_v0.1-BF16-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -661,21 +622,19 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/mixtral_8x22B_v0.1-BF16-pretrain.yaml \
-              --log_file /tmp/primus_mixtral_8x22B_proxy.log \
+            bash examples/run_pretrain.sh \
-              -- train pretrain \
+                --train_iters 50 \
-              --config examples/megatron/configs/MI300X/mixtral_8x22B_v0.1-BF16-pretrain.yaml \
                 --num_layers 4 \
                 --pipeline_model_parallel_size 1 \
                 --micro_batch_size 1 \
-              --global_batch_size 16 \
+                --global_batch_size 16
-              --train_iters 50
 
 .. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-7b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Qwen 2.5 7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run training on a single node for Qwen 2.5 7B BF16, use the following
    command:
@@ -687,10 +646,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/qwen2.5_7B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_qwen2.5_7B.log \
+            bash examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/qwen2.5_7B-BF16-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -702,10 +659,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/qwen2.5_7B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_qwen2.5_7B.log \
+            bash examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/qwen2.5_7B-BF16-pretrain.yaml
 
    For FP8, use the following command.
 
@@ -716,10 +671,8 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/qwen2.5_7B-FP8-pretrain.yaml \
-              --log_file /tmp/primus_qwen2.5_7B_fp8.log \
+            bash examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI355X/qwen2.5_7B-FP8-pretrain.yaml
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -731,16 +684,14 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/qwen2.5_7B-FP8-pretrain.yaml \
-              --log_file /tmp/primus_qwen2.5_7B_fp8.log \
+            bash examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/qwen2.5_7B-FP8-pretrain.yaml
 
 .. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-72b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Qwen 2.5 72B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To run the training on a single node for Qwen 2.5 72B BF16, use the following command.
 
@@ -751,10 +702,11 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI355X/qwen2.5_72B-pretrain.yaml \
-              --log_file /tmp/primus_qwen2.5_72B.log \
+            bash examples/run_pretrain.sh \
-              -- train pretrain \
+                --train_iters 50 \
-              --config examples/megatron/configs/MI355X/qwen2.5_72B-BF16-pretrain.yaml
+                --micro_batch_size 16 \
+                --global_batch_size 256
 
       .. tab-item:: MI300X
          :sync: MI325X and MI300X
@@ -766,12 +718,10 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
             export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
             export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
-            bash runner/primus-cli direct \
+            EXP=examples/megatron/configs/MI300X/qwen2.5_72B-BF16-pretrain.yaml \
-              --log_file /tmp/primus_qwen2.5_72B.log \
+            bash examples/run_pretrain.sh
-              -- train pretrain \
-              --config examples/megatron/configs/MI300X/qwen2.5_72B-BF16-pretrain.yaml
 
-.. _amd-primus-megatron-multi-node-examples-v26.01:
+.. _amd-primus-megatron-multi-node-examples-v25.11:
 
 Multi-node training examples
 ----------------------------
@@ -780,7 +730,7 @@ Refer to :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure y
 training.
 
 To run training on multiple nodes, you can use the
-`run_slurm_pretrain.sh <https://github.com/AMD-AGI/Primus/blob/9c529cd4a934a68a880ede036c3e97b792e38167/examples/run_slurm_pretrain.sh>`__
+`run_slurm_pretrain.sh <https://github.com/AMD-AGI/Primus/blob/main/examples/run_slurm_pretrain.sh>`__
 to launch the multi-node workload. Use the following steps to setup your environment:
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
@@ -813,13 +763,13 @@ to launch the multi-node workload. Use the following steps to setup your environ
    * If ``NCCL_IB_HCA`` and ``NCCL_SOCKET_IFNAME`` are not set, Primus will try to auto-detect. However, since NICs can vary accross different cluster, it is encouraged to explicitly export your NCCL parameters for the cluster.
    * To find your network interface, you can use ``ip a``.
    * To find RDMA interfaces, you can use ``ibv_devices`` to get the list of all the RDMA/IB  devices.
-   * Remember to set ``DOCKER_IMAGE`` and ``HF_TOKEN`` (see :ref:`amd-primus-megatron-lm-tokenizer-v26.01`) as appropriate.
+   * Remember to set ``DOCKER_IMAGE`` and ``HF_TOKEN`` (see :ref:`amd-primus-megatron-lm-tokenizer-v25.11`) as appropriate.
 
 .. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.1 8B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To train Llama 3.1 8B FP8 on 8 nodes, run:
 
@@ -836,7 +786,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To train Llama 2 7B FP8 on 8 nodes, run:
 
@@ -853,7 +803,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.1 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To train Llama 3.1 70B FP8 on 8 nodes, run:
 
@@ -883,7 +833,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To train Llama 2 70B FP8 on 8 nodes, run:
 
@@ -913,7 +863,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.3 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To train Llama 3.3 70B FP8 on 8 nodes, run:
 
@@ -943,7 +893,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To train Mixtral 8x7B BF16 on 8 nodes, run:
 
@@ -961,7 +911,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v25.11` to switch to another available model.
 
    To train Qwen2.5 72B FP8 on 8 nodes, run:
 
@@ -976,7 +926,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
           --global_batch_size 512 \
           --recompute_num_layers 80 \
 
-.. _amd-primus-megatron-lm-benchmark-test-vars-v26.01:
+.. _amd-primus-megatron-lm-benchmark-test-vars-v25.11:
 
 Key options
 -----------

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

@@ -45,7 +45,7 @@ with Primus Turbo optimizations.
               - {{ component_version }}
             {% endfor %}
 
-.. _amd-primus-pytorch-model-support-v26.01:
+.. _amd-primus-pytorch-model-support-v25.11:
 
 Supported models
 ================
@@ -91,7 +91,7 @@ vary by model -- select one to get started.
    For additional workloads, including Llama 3.3, Llama 3.2, Llama 2, GPT OSS, Qwen, and Flux models,
    see the documentation :doc:`pytorch-training` (without Primus)
 
-.. _amd-primus-pytorch-performance-measurements-v26.01:
+.. _amd-primus-pytorch-performance-measurements-v25.11:
 
 System validation
 =================
@@ -146,7 +146,7 @@ tweak some configurations (such as batch sizes).
          .. container:: model-doc {{ model.mad_tag }}
 
             The following run command is tailored to {{ model.model }}.
-            See :ref:`amd-primus-pytorch-model-support-v26.01` to switch to another available model.
+            See :ref:`amd-primus-pytorch-model-support-v25.11` to switch to another available model.
 
             1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
                directory and install the required packages on the host machine.
@@ -184,7 +184,7 @@ tweak some configurations (such as batch sizes).
          .. container:: model-doc {{ model.mad_tag }}
 
             The following run commands are tailored to {{ model.model }}.
-            See :ref:`amd-primus-pytorch-model-support-v26.01` to switch to another available model.
+            See :ref:`amd-primus-pytorch-model-support-v25.11` to switch to another available model.
 
             .. rubric:: Download the Docker image and required packages
 
@@ -220,8 +220,8 @@ tweak some configurations (such as batch sizes).
                   docker start training_env
                   docker exec -it training_env bash
 
-               The Docker container hosts verified commit ``9c529cd4`` of the `Primus
+               The Docker container hosts verified commit ``c4c083de`` of the `Primus
-               <https://github.com/AMD-AGI/Primus/tree/9c529cd4a934a68a880ede036c3e97b792e38167/>`__ repository.
+               <https://github.com/AMD-AGI/Primus/tree/c4c083de64ba3e8f19ccc9629411267108931f9e/>`__ repository.
 
             .. rubric:: Prepare training datasets and dependencies
 
@@ -257,31 +257,24 @@ tweak some configurations (such as batch sizes).
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI355X/llama3.1_8B-BF16-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_8B.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI355X/llama3.1_8B-BF16-pretrain.yaml
 
                   .. tab-item:: MI325X
                      :sync: MI325X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_8B.log \
+                        bash examples/run_pretrain.sh --training.local_batch_size 6 
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
-                          --training.local_batch_size 6
 
                   .. tab-item:: MI300X
                      :sync: MI300X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_8B.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml
 
                To train Llama 3.1 8B with FP8 precision, use the following command.
 
@@ -292,31 +285,24 @@ tweak some configurations (such as batch sizes).
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI355X/llama3.1_8B-FP8-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_8B_fp8.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI355X/llama3.1_8B-FP8-pretrain.yaml
 
                   .. tab-item:: MI325X
                      :sync: MI325X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_8B_fp8.log \
+                        bash examples/run_pretrain.sh --training.local_batch_size 7 
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
-                          --training.local_batch_size 7
 
                   .. tab-item:: MI300X
                      :sync: MI300X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_8B_fp8.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml
 
             .. container:: model-doc primus_pyt_train_llama-3.1-70b
 
@@ -329,31 +315,24 @@ tweak some configurations (such as batch sizes).
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_70B.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml
 
                   .. tab-item:: MI325X
                      :sync: MI325X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_70B.log \
+                        bash examples/run_pretrain.sh --training.local_batch_size 6 
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
-                          --training.local_batch_size 6
 
                   .. tab-item:: MI300X
                      :sync: MI300X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_70B.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml
 
                To train Llama 3.1 70B with FP8 precision, use the following command.
 
@@ -364,31 +343,24 @@ tweak some configurations (such as batch sizes).
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI355X/llama3.1_70B-FP8-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_70B_fp8.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI355X/llama3.1_70B-FP8-pretrain.yaml
 
                   .. tab-item:: MI325X
                      :sync: MI325X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_70B_fp8.log \
+                        bash examples/run_pretrain.sh --training.local_batch_size 5 
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
-                          --training.local_batch_size 5
 
                   .. tab-item:: MI300X
                      :sync: MI300X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
-                          --log_file /tmp/primus_llama3.1_70B_fp8.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml
 
             .. container:: model-doc primus_pyt_train_deepseek-v3-16b
 
@@ -401,31 +373,24 @@ tweak some configurations (such as batch sizes).
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI355X/deepseek_v3_16b-pretrain.yaml \
-                          --log_file /tmp/primus_deepseek_v3_16b.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI355X/deepseek_v3_16b-pretrain.yaml
 
                   .. tab-item:: MI325X
                      :sync: MI325X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
-                          --log_file /tmp/primus_deepseek_v3_16b.log \
+                        bash examples/run_pretrain.sh --training.local_batch_size 10 
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
-                          --training.local_batch_size 10
 
                   .. tab-item:: MI300X
                      :sync: MI300X
 
                      .. code-block:: shell
 
-                        bash runner/primus-cli direct \
+                        EXP=examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
-                          --log_file /tmp/primus_deepseek_v3_16b.log \
+                        bash examples/run_pretrain.sh
-                          -- train pretrain \
-                          --config examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml
       {% endfor %}
    {% endfor %}
 

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

@@ -43,7 +43,7 @@ training workloads:
               - {{ component_version }}
             {% endfor %}
 
-.. _amd-pytorch-training-model-support-v26.01:
+.. _amd-pytorch-training-model-support-v25.11:
 
 Supported models
 ================
@@ -85,7 +85,7 @@ one to get started.
          </div>
       </div>
 
-.. _amd-pytorch-training-supported-training-modes-v26.01:
+.. _amd-pytorch-training-supported-training-modes-v25.11:
 
 The following table lists supported training modes per model.
 
@@ -120,7 +120,7 @@ The following table lists supported training modes per model.
          unlisted fine-tuning methods by using an existing file in the
          ``/workspace/torchtune/recipes/configs`` directory as a template.
 
-.. _amd-pytorch-training-performance-measurements-v26.01:
+.. _amd-pytorch-training-performance-measurements-v25.11:
 
 Performance measurements
 ========================
@@ -176,7 +176,7 @@ Run training
          .. container:: model-doc {{ model.mad_tag }}
 
             The following run command is tailored to {{ model.model }}.
-            See :ref:`amd-pytorch-training-model-support-v26.01` to switch to another available model.
+            See :ref:`amd-pytorch-training-model-support-v25.11` to switch to another available model.
 
             1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
                directory and install the required packages on the host machine.
@@ -214,7 +214,7 @@ Run training
          .. container:: model-doc {{ model.mad_tag }}
 
             The following commands are tailored to {{ model.model }}.
-            See :ref:`amd-pytorch-training-model-support-v26.01` to switch to another available model.
+            See :ref:`amd-pytorch-training-model-support-v25.11` to switch to another available model.
 
       {% endfor %}
    {% endfor %}
@@ -409,10 +409,6 @@ Run training
 
             {% if model.mad_tag == "pyt_train_dlrm" %}
 
-            .. note::
-
-               DLRM is supported on MI300X, MI325X, MI350X, and MI355X GPUs.
-
             1. Go to the DLRM directory.
 
                .. code-block:: shell
@@ -536,7 +532,7 @@ Run training
 
             To start the fine-tuning benchmark, use the following command with the
             appropriate options. See the following list of options and their descriptions.
-            See :ref:`supported training modes <amd-pytorch-training-supported-training-modes-v26.01>`.
+            See :ref:`supported training modes <amd-pytorch-training-supported-training-modes-v25.11>`.
 
             .. code-block:: shell
 
@@ -601,7 +597,7 @@ Run training
 
             For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
 
-.. _amd-pytorch-training-multinode-examples-v26.01:
+.. _amd-pytorch-training-multinode-examples-v25.11:
 
 Multi-node training
 -------------------

docs/reference/api-libraries.md

@@ -18,6 +18,7 @@
 (artificial-intelligence-apis)=
 
 * {doc}`Composable Kernel <composable_kernel:index>`
+* {doc}`hipDNN <hipdnn:index>`
 * {doc}`MIGraphX <amdmigraphx:index>`
 * {doc}`MIOpen <miopen:index>`
 * {doc}`MIVisionX <mivisionx:index>`

docs/what-is-rocm.rst

@@ -36,6 +36,7 @@ Machine Learning & Computer Vision
   :header: "Component", "Description"
 
   ":doc:`Composable Kernel <composable_kernel:index>`", "Provides a programming model for writing performance critical kernels for machine learning workloads across multiple architectures"
+  ":doc:`hipDNN <hipdnn:index>`", "A graph-based deep learning library that enables multi-operation fusion for improved performance on AMD GPUs. "
   ":doc:`MIGraphX <amdmigraphx:index>`", "Graph inference engine that accelerates machine learning model inference"
   ":doc:`MIOpen <miopen:index>`", "An open source deep-learning library"
   ":doc:`MIVisionX <mivisionx:index>`", "Set of comprehensive computer vision and machine learning libraries, utilities, and applications"