# Anywhere {branch} is used, the branch name will be substituted.
# These comments will also be removed.
defaults:
  numbered: False
  maxdepth: 6
root: index
subtrees:
  - entries:
      - file: about/release-notes.md
        title: Release notes
      - file: install/compatibility-matrix.md
        title: Compatibility matrix
  - caption: Installation
    entries:
      - file: install/rocm
        title: Installation
      - url: https://github.com/ROCm/TheRock
        title: Build from source
  - caption: ROCm for AI
    entries:
      - file: install/pytorch-comfyui
        title: Install PyTorch and ComfyUI
      # - url: https://rocm.docs.amd.com/projects/install-on-linux/en/${branch}/
      #   title: ROCm on Linux
      # - url: https://rocm.docs.amd.com/projects/install-on-windows/en/latest/
      #   title: HIP SDK on Windows
      # - url: https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/index.html
      #   title: ROCm on Radeon and Ryzen
      # - file: how-to/deep-learning-rocm.md
      #   title: Deep learning frameworks
      #   subtrees:
      #     - entries:
      #         - file: compatibility/ml-compatibility/pytorch-compatibility.rst
      #           title: PyTorch compatibility
      #         - file: compatibility/ml-compatibility/tensorflow-compatibility.rst
      #           title: TensorFlow compatibility
      #         - file: compatibility/ml-compatibility/jax-compatibility.rst
      #           title: JAX compatibility
      #         - file: compatibility/ml-compatibility/verl-compatibility.rst
      #           title: verl compatibility
      #         - file: compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
      #           title: Stanford Megatron-LM compatibility
      #         - file: compatibility/ml-compatibility/dgl-compatibility.rst
      #           title: DGL compatibility
      #         - file: compatibility/ml-compatibility/megablocks-compatibility.rst
      #           title: Megablocks compatibility
      #         - file: compatibility/ml-compatibility/taichi-compatibility.rst
      #           title: Taichi compatibility
      #         - file: compatibility/ml-compatibility/ray-compatibility.rst
      #           title: Ray compatibility
      #         - file: compatibility/ml-compatibility/llama-cpp-compatibility.rst
      #           title: llama.cpp compatibility
      #         - file: compatibility/ml-compatibility/flashinfer-compatibility.rst
      #           title: FlashInfer compatibility

  # - caption: How to
  #   entries:
  #     - file: how-to/rocm-for-ai/index.rst
  #       title: Use ROCm for AI
  #       subtrees:
  #         - entries:
  #             - file: how-to/rocm-for-ai/install.rst
  #               title: Installation
  #             - file: how-to/rocm-for-ai/system-setup/index.rst
  #               title: System setup
  #               entries:
  #                 - file: how-to/rocm-for-ai/system-setup/prerequisite-system-validation.rst
  #                   title: System validation
  #                 - file: how-to/rocm-for-ai/system-setup/multi-node-setup.rst
  #                   title: Multi-node setup
  #                 - file: how-to/rocm-for-ai/system-setup/system-health-check.rst
  #                   title: System health benchmarks
  #             - file: how-to/rocm-for-ai/training/index.rst
  #               title: Training
  #               subtrees:
  #                 - entries:
  #                     - file: how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
  #                       title: Train a model with Primus and Megatron-LM
  #                     - file: how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
  #                       title: Train a model with Primus and PyTorch
  #                     - file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
  #                       title: Train a model with JAX MaxText
  #                     - file: how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry
  #                       title: Train a model with LLM Foundry
  #                     - file: how-to/rocm-for-ai/training/scale-model-training.rst
  #                       title: Scale model training
  #
  #             - file: how-to/rocm-for-ai/fine-tuning/index.rst
  #               title: Fine-tuning LLMs
  #               subtrees:
  #                 - entries:
  #                     - file: how-to/rocm-for-ai/fine-tuning/overview.rst
  #                       title: Conceptual overview
  #                     - file: how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference.rst
  #                       title: Fine-tuning
  #                       subtrees:
  #                         - entries:
  #                             - file: how-to/rocm-for-ai/fine-tuning/single-gpu-fine-tuning-and-inference.rst
  #                               title: Use a single accelerator
  #                             - file: how-to/rocm-for-ai/fine-tuning/multi-gpu-fine-tuning-and-inference.rst
  #                               title: Use multiple accelerators
  #
  #             - file: how-to/rocm-for-ai/inference/index.rst
  #               title: Inference
  #               subtrees:
  #                 - entries:
  #                     - file: how-to/rocm-for-ai/inference/hugging-face-models.rst
  #                       title: Run models from Hugging Face
  #                     - file: how-to/rocm-for-ai/inference/llm-inference-frameworks.rst
  #                       title: LLM inference frameworks
  #                     - file: how-to/rocm-for-ai/inference/benchmark-docker/vllm.rst
  #                       title: vLLM inference performance testing
  #                     - file: how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference.rst
  #                       title: PyTorch inference performance testing
  #                     - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang.rst
  #                       title: SGLang inference performance testing
  #                     - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst
  #                       title: SGLang distributed inference with Mooncake
  #                     - file: how-to/rocm-for-ai/inference/deploy-your-model.rst
  #                       title: Deploy your model
  #
  #             - file: how-to/rocm-for-ai/inference-optimization/index.rst
  #               title: Inference optimization
  #               subtrees:
  #                 - entries:
  #                     - file: how-to/rocm-for-ai/inference-optimization/model-quantization.rst
  #                     - file: how-to/rocm-for-ai/inference-optimization/model-acceleration-libraries.rst
  #                     - file: how-to/rocm-for-ai/inference-optimization/optimizing-with-composable-kernel.md
  #                       title: Optimize with Composable Kernel
  #                     - file: how-to/rocm-for-ai/inference-optimization/optimizing-triton-kernel.rst
  #                       title: Optimize Triton kernels
  #                     - file: how-to/rocm-for-ai/inference-optimization/profiling-and-debugging.rst
  #                       title: Profile and debug
  #                     - file: how-to/rocm-for-ai/inference-optimization/workload.rst
  #                       title: Workload optimization
  #
  #             - url: https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/
  #               title: AI tutorials
  #
  #     - file: how-to/rocm-for-hpc/index.rst
  #       title: Use ROCm for HPC
  #     - file: how-to/system-optimization/index.rst
  #       title: System optimization
  #     - file: how-to/gpu-performance/mi300x.rst
  #       title: AMD Instinct MI300X performance guides
  #     - file: how-to/system-debugging.md
  #     - file: conceptual/compiler-topics.md
  #       title: Use advanced compiler features
  #       subtrees:
  #         - entries:
  #             - url: https://rocm.docs.amd.com/projects/llvm-project/en/latest/index.html
  #               title: ROCm compiler infrastructure
  #             - url: https://rocm.docs.amd.com/projects/llvm-project/en/latest/conceptual/using-gpu-sanitizer.html
  #               title: Use AddressSanitizer
  #             - url: https://rocm.docs.amd.com/projects/llvm-project/en/latest/conceptual/openmp.html
  #               title: OpenMP support
  #     - file: how-to/setting-cus
  #       title: Set the number of CUs
  #     - file: how-to/Bar-Memory.rst
  #       title: Troubleshoot BAR access limitation
  #     - url: https://github.com/amd/rocm-examples
  #       title: ROCm examples
  #
  # - caption: Conceptual
  #   entries:
  #     - file: conceptual/gpu-arch.md
  #       title: GPU architecture overview
  #       subtrees:
  #         - entries:
  #             - file: conceptual/gpu-arch/mi300.md
  #               title: MI300 microarchitecture
  #               subtrees:
  #                 - entries:
  #                     - url: https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/instruction-set-architectures/amd-instinct-mi300-cdna3-instruction-set-architecture.pdf
  #                       title: AMD Instinct MI300/CDNA3 ISA
  #                     - url: https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/white-papers/amd-cdna-3-white-paper.pdf
  #                       title: White paper
  #                     - file: conceptual/gpu-arch/mi300-mi200-performance-counters.rst
  #                       title: MI300 and MI200 performance counters
  #                     - file: conceptual/gpu-arch/mi350-performance-counters.rst
  #                       title: MI350 series performance counters
  #             - file: conceptual/gpu-arch/mi250.md
  #               title: MI250 microarchitecture
  #               subtrees:
  #                 - entries:
  #                     - url: https://www.amd.com/system/files/TechDocs/instinct-mi200-cdna2-instruction-set-architecture.pdf
  #                       title: AMD Instinct MI200/CDNA2 ISA
  #                     - url: https://www.amd.com/content/dam/amd/en/documents/instinct-business-docs/white-papers/amd-cdna2-white-paper.pdf
  #                       title: White paper
  #             - file: conceptual/gpu-arch/mi100.md
  #               title: MI100 microarchitecture
  #               subtrees:
  #                 - entries:
  #                     - url: https://www.amd.com/system/files/TechDocs/instinct-mi100-cdna1-shader-instruction-set-architecture%C2%A0.pdf
  #                       title: AMD Instinct MI100/CDNA1 ISA
  #                     - url: https://www.amd.com/content/dam/amd/en/documents/instinct-business-docs/white-papers/amd-cdna-white-paper.pdf
  #                       title: White paper
  #     - file: conceptual/file-reorg.md
  #       title: File structure (Linux FHS)
  #     - file: conceptual/gpu-isolation.md
  #       title: GPU isolation techniques
  #     - file: conceptual/cmake-packages.rst
  #       title: Using CMake
  #     - file: conceptual/ai-pytorch-inception.md
  #       title: Inception v3 with PyTorch
  #
  # - caption: Reference
  #   entries:
  #     - file: reference/api-libraries.md
  #       title: ROCm libraries
  #     - file: reference/rocm-tools.md
  #       title: ROCm tools, compilers, and runtimes
  #     - file: reference/gpu-arch-specs.rst
  #     - file: reference/gpu-atomics-operation.rst
  #     - file: reference/precision-support.rst
  #       title: Data types and precision support
  #     - file: reference/graph-safe-support.rst
  #       title: Graph safe support

  # - caption: Contribute
  #   entries:
  #     - file: contribute/contributing.md
  #       title: Contributing to the ROCm documentation
  #       subtrees:
  #         - entries:
  #             - file: contribute/toolchain.md
  #               title: ROCm documentation toolchain
  #             - file: contribute/building.md
  #     - file: contribute/feedback.md
  #       title: Providing feedback about the ROCm documentation
  #     - file: about/license.md
  #       title: ROCm licenses