4.2 KiB
AMD ROCm documentation
ROCm is an open-source software platform optimized to extract HPC and AI workload performance from AMD Instinct accelerators and AMD Radeon GPUs while maintaining compatibility with industry software frameworks. For more information, see What is ROCm?
If you're using Radeon GPUs, consider reviewing {doc}Radeon-specific ROCm documentation<radeon:index>.
Installation instructions are available from:
- {doc}
ROCm installation for Linux<rocm-install-on-linux:index> - {doc}
HIP SDK installation for Windows<rocm-install-on-windows:index> - Deep learning frameworks installation
- Build ROCm from source
ROCm documentation is organized into the following categories:
::::{grid} 1 2 2 2 :class-container: rocm-doc-grid
:::{grid-item-card} :class-card: sd-text-black :img-top: ./data/banner-compatibility.jpg :img-alt: Compatibility information :padding: 2
- Compatibility matrix
- {doc}
Linux system requirements<rocm-install-on-linux:reference/system-requirements> - {doc}
Windows system requirements<rocm-install-on-windows:reference/system-requirements> - {doc}
Third-party support<rocm-install-on-linux:reference/3rd-party-support-matrix> - {doc}
User/kernel space<rocm-install-on-linux:reference/user-kernel-space-compat-matrix> - {doc}
Docker<rocm-install-on-linux:reference/docker-image-support-matrix> - {doc}
OpenMP<llvm-project:conceptual/openmp> - Precision support
- {doc}
ROCm on Radeon GPUs<radeon:index>:::
:::{grid-item-card} :class-card: sd-text-black :img-top: ./data/banner-howto.jpg :img-alt: How-to documentation :padding: 2
- Using ROCm for AI
- Using ROCm for HPC
- Fine-tuning LLMs and inference optimization
- System optimization
- AMD Instinct MI300X tuning guides
- System debugging
- GPU-enabled MPI
- Using advanced compiler features
- Setting the number of CUs
- GitHub examples :::
:::{grid-item-card} :class-card: sd-text-black :img-top: ./data/banner-conceptual.jpg :img-alt: Conceptual documentation :padding: 2
- GPU architecture
- GPU memory
- File structure (Linux FHS)
- GPU isolation techniques
- Using CMake
- ROCm & PCIe atomics
- Inception v3 with PyTorch
- Inference optimization with MIGraphX :::
:::{grid-item-card} :class-card: sd-text-black :img-top: ./data/banner-reference.jpg :img-alt: Reference documentation :padding: 2
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