mirror of
https://github.com/ROCm/ROCm.git
synced 2026-02-12 07:25:22 -05:00
a32210fa7e
Add release notes Add install instructions Add PyTorch + ComfyUI instructions Add custom selector directives Add JS and CSS for selector Add custom icon directive and utils Clean up conf.py
233 lines
12 KiB
YAML
233 lines
12 KiB
YAML
# Anywhere {branch} is used, the branch name will be substituted.
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# These comments will also be removed.
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defaults:
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numbered: False
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maxdepth: 6
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root: index
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subtrees:
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- entries:
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- file: about/release-notes.md
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title: Release notes
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- file: install/compatibility-matrix.md
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title: Compatibility matrix
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- caption: Installation
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entries:
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- file: install/rocm
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title: Installation
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- url: https://github.com/ROCm/TheRock
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title: Build from source
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- caption: ROCm for AI
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entries:
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- file: install/pytorch-comfyui
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title: Install PyTorch and ComfyUI
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# - url: https://rocm.docs.amd.com/projects/install-on-linux/en/${branch}/
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# title: ROCm on Linux
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# - url: https://rocm.docs.amd.com/projects/install-on-windows/en/latest/
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# title: HIP SDK on Windows
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# - url: https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/index.html
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# title: ROCm on Radeon and Ryzen
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# - file: how-to/deep-learning-rocm.md
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# title: Deep learning frameworks
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# subtrees:
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# - entries:
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# - file: compatibility/ml-compatibility/pytorch-compatibility.rst
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# title: PyTorch compatibility
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# - file: compatibility/ml-compatibility/tensorflow-compatibility.rst
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# title: TensorFlow compatibility
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# - file: compatibility/ml-compatibility/jax-compatibility.rst
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# title: JAX compatibility
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# - file: compatibility/ml-compatibility/verl-compatibility.rst
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# title: verl compatibility
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# - file: compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
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# title: Stanford Megatron-LM compatibility
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# - file: compatibility/ml-compatibility/dgl-compatibility.rst
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# title: DGL compatibility
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# - file: compatibility/ml-compatibility/megablocks-compatibility.rst
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# title: Megablocks compatibility
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# - file: compatibility/ml-compatibility/taichi-compatibility.rst
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# title: Taichi compatibility
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# - file: compatibility/ml-compatibility/ray-compatibility.rst
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# title: Ray compatibility
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# - file: compatibility/ml-compatibility/llama-cpp-compatibility.rst
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# title: llama.cpp compatibility
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# - file: compatibility/ml-compatibility/flashinfer-compatibility.rst
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# title: FlashInfer compatibility
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# - caption: How to
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# entries:
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# - file: how-to/rocm-for-ai/index.rst
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# title: Use ROCm for AI
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# subtrees:
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# - entries:
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# - file: how-to/rocm-for-ai/install.rst
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# title: Installation
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# - file: how-to/rocm-for-ai/system-setup/index.rst
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# title: System setup
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# entries:
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# - file: how-to/rocm-for-ai/system-setup/prerequisite-system-validation.rst
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# title: System validation
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# - file: how-to/rocm-for-ai/system-setup/multi-node-setup.rst
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# title: Multi-node setup
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# - file: how-to/rocm-for-ai/system-setup/system-health-check.rst
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# title: System health benchmarks
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# - file: how-to/rocm-for-ai/training/index.rst
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# title: Training
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# subtrees:
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# - entries:
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# - file: how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
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# title: Train a model with Primus and Megatron-LM
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# - file: how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
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# title: Train a model with Primus and PyTorch
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# - file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
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# title: Train a model with JAX MaxText
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# - file: how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry
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# title: Train a model with LLM Foundry
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# - file: how-to/rocm-for-ai/training/scale-model-training.rst
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# title: Scale model training
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#
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# - file: how-to/rocm-for-ai/fine-tuning/index.rst
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# title: Fine-tuning LLMs
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# subtrees:
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# - entries:
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# - file: how-to/rocm-for-ai/fine-tuning/overview.rst
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# title: Conceptual overview
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# - file: how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference.rst
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# title: Fine-tuning
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# subtrees:
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# - entries:
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# - file: how-to/rocm-for-ai/fine-tuning/single-gpu-fine-tuning-and-inference.rst
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# title: Use a single accelerator
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# - file: how-to/rocm-for-ai/fine-tuning/multi-gpu-fine-tuning-and-inference.rst
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# title: Use multiple accelerators
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#
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# - file: how-to/rocm-for-ai/inference/index.rst
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# title: Inference
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# subtrees:
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# - entries:
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# - file: how-to/rocm-for-ai/inference/hugging-face-models.rst
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# title: Run models from Hugging Face
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# - file: how-to/rocm-for-ai/inference/llm-inference-frameworks.rst
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# title: LLM inference frameworks
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# - file: how-to/rocm-for-ai/inference/benchmark-docker/vllm.rst
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# title: vLLM inference performance testing
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# - file: how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference.rst
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# title: PyTorch inference performance testing
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# - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang.rst
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# title: SGLang inference performance testing
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# - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst
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# title: SGLang distributed inference with Mooncake
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# - file: how-to/rocm-for-ai/inference/deploy-your-model.rst
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# title: Deploy your model
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#
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# - file: how-to/rocm-for-ai/inference-optimization/index.rst
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# title: Inference optimization
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# subtrees:
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# - entries:
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# - file: how-to/rocm-for-ai/inference-optimization/model-quantization.rst
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# - file: how-to/rocm-for-ai/inference-optimization/model-acceleration-libraries.rst
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# - file: how-to/rocm-for-ai/inference-optimization/optimizing-with-composable-kernel.md
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# title: Optimize with Composable Kernel
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# - file: how-to/rocm-for-ai/inference-optimization/optimizing-triton-kernel.rst
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# title: Optimize Triton kernels
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# - file: how-to/rocm-for-ai/inference-optimization/profiling-and-debugging.rst
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# title: Profile and debug
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# - file: how-to/rocm-for-ai/inference-optimization/workload.rst
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# title: Workload optimization
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#
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# - url: https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/
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# title: AI tutorials
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#
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# - file: how-to/rocm-for-hpc/index.rst
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# title: Use ROCm for HPC
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# - file: how-to/system-optimization/index.rst
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# title: System optimization
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# - file: how-to/gpu-performance/mi300x.rst
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# title: AMD Instinct MI300X performance guides
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# - file: how-to/system-debugging.md
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# - file: conceptual/compiler-topics.md
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# title: Use advanced compiler features
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# subtrees:
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# - entries:
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# - url: https://rocm.docs.amd.com/projects/llvm-project/en/latest/index.html
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# title: ROCm compiler infrastructure
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# - url: https://rocm.docs.amd.com/projects/llvm-project/en/latest/conceptual/using-gpu-sanitizer.html
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# title: Use AddressSanitizer
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# - url: https://rocm.docs.amd.com/projects/llvm-project/en/latest/conceptual/openmp.html
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# title: OpenMP support
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# - file: how-to/setting-cus
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# title: Set the number of CUs
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# - file: how-to/Bar-Memory.rst
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# title: Troubleshoot BAR access limitation
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# - url: https://github.com/amd/rocm-examples
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# title: ROCm examples
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#
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# - caption: Conceptual
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# entries:
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# - file: conceptual/gpu-arch.md
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# title: GPU architecture overview
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# subtrees:
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# - entries:
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# - file: conceptual/gpu-arch/mi300.md
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# title: MI300 microarchitecture
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# subtrees:
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# - entries:
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# - url: https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/instruction-set-architectures/amd-instinct-mi300-cdna3-instruction-set-architecture.pdf
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# title: AMD Instinct MI300/CDNA3 ISA
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# - url: https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/white-papers/amd-cdna-3-white-paper.pdf
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# title: White paper
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# - file: conceptual/gpu-arch/mi300-mi200-performance-counters.rst
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# title: MI300 and MI200 performance counters
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# - file: conceptual/gpu-arch/mi350-performance-counters.rst
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# title: MI350 series performance counters
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# - file: conceptual/gpu-arch/mi250.md
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# title: MI250 microarchitecture
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# subtrees:
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# - entries:
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# - url: https://www.amd.com/system/files/TechDocs/instinct-mi200-cdna2-instruction-set-architecture.pdf
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# title: AMD Instinct MI200/CDNA2 ISA
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# - url: https://www.amd.com/content/dam/amd/en/documents/instinct-business-docs/white-papers/amd-cdna2-white-paper.pdf
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# title: White paper
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# - file: conceptual/gpu-arch/mi100.md
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# title: MI100 microarchitecture
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# subtrees:
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# - entries:
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# - url: https://www.amd.com/system/files/TechDocs/instinct-mi100-cdna1-shader-instruction-set-architecture%C2%A0.pdf
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# title: AMD Instinct MI100/CDNA1 ISA
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# - url: https://www.amd.com/content/dam/amd/en/documents/instinct-business-docs/white-papers/amd-cdna-white-paper.pdf
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# title: White paper
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# - file: conceptual/file-reorg.md
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# title: File structure (Linux FHS)
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# - file: conceptual/gpu-isolation.md
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# title: GPU isolation techniques
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# - file: conceptual/cmake-packages.rst
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# title: Using CMake
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# - file: conceptual/ai-pytorch-inception.md
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# title: Inception v3 with PyTorch
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#
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# - caption: Reference
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# entries:
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# - file: reference/api-libraries.md
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# title: ROCm libraries
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# - file: reference/rocm-tools.md
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# title: ROCm tools, compilers, and runtimes
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# - file: reference/gpu-arch-specs.rst
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# - file: reference/gpu-atomics-operation.rst
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# - file: reference/precision-support.rst
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# title: Data types and precision support
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# - file: reference/graph-safe-support.rst
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# title: Graph safe support
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# - caption: Contribute
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# entries:
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# - file: contribute/contributing.md
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# title: Contributing to the ROCm documentation
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# subtrees:
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# - entries:
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# - file: contribute/toolchain.md
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# title: ROCm documentation toolchain
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# - file: contribute/building.md
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# - file: contribute/feedback.md
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# title: Providing feedback about the ROCm documentation
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# - file: about/license.md
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# title: ROCm licenses
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