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* Add Using ROCm for AI:wq Add PyTorch Docker installation images Split doc into subtopics Add metadata Clean up index Clean up hugging face guide Clean up installation guide Fix rST formatting Clean up install and train-a-model Clean up MAD Delete unused file Add ref anchors and clean up MAD doc Add formatting fixes Update toc and section index Format some code blocks Remove install guide and update toc Chop installation guide Clean up deployment and hugging face sections Change headings to end in -ing Fix spelling in Training a model Delete MAD and split out install content Fix formatting Change words to satisfy spellcheck linter * Add review suggestions and add helpful links Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> Add helpful links and add review suggestions Remove fine-tuning link and links to D5 and MAGMA Update docs/how-to/rocm-for-ai/deploy-your-model.rst Co-authored-by: Young Hui - AMD <145490163+yhuiYH@users.noreply.github.com> Update DeepSpeed link Add subheading to ML framework installation and closing blurb to hugging face models guide * Reorder topics
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AMD ROCm™ documentation
Welcome to the ROCm docs home page! If you're new to ROCm, you can review the following resources to learn more about our products and what we support:
You can install ROCm on our Radeon™, Radeon™ PRO, and Instinct™ GPUs. If you're using Radeon
GPUs, we recommend reading the
{doc}Radeon-specific ROCm documentation<radeon:index>.
For hands-on applications, refer to our ROCm blogs site.
Our 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-installation.jpg :img-alt: Install documentation :padding: 2
- Linux
- {doc}
Quick start guide<rocm-install-on-linux:tutorial/quick-start> - {doc}
Linux install guide<rocm-install-on-linux:how-to/native-install/index> - {doc}
Package manager integration<rocm-install-on-linux:how-to/native-install/package-manager-integration>
- {doc}
- Windows
- {doc}
Windows install guide<rocm-install-on-windows:how-to/install> - {doc}
Application deployment guidelines<rocm-install-on-windows:conceptual/deployment-guidelines>
- {doc}
- Deep learning frameworks
- {doc}
Install Docker containers<rocm-install-on-linux:how-to/docker> - {doc}
PyTorch for ROCm<rocm-install-on-linux:how-to/3rd-party/pytorch-install> - {doc}
TensorFlow for ROCm<rocm-install-on-linux:how-to/3rd-party/tensorflow-install> - {doc}
JAX for ROCm<rocm-install-on-linux:how-to/3rd-party/jax-install> - {doc}
MAGMA for ROCm<rocm-install-on-linux:how-to/3rd-party/magma-install> - {doc}
ROCm & Spack<rocm-install-on-linux:how-to/spack>:::
- {doc}
:::{grid-item-card} :class-card: sd-text-black :img-top: ./data/banner-compatibility.jpg :img-alt: Compatibility information :padding: 2
- Compatibility matrix
- {doc}
System requirements (Linux)<rocm-install-on-linux:reference/system-requirements> - {doc}
System requirements (Windows)<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> - OpenMP
- Precision support
- {doc}
ROCm on Radeon GPUs<radeon:index>:::
:::{grid-item-card} :class-card: sd-text-black :img-top: ./data/banner-reference.jpg :img-alt: Reference documentation :padding: 2
:::{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
- System tuning for various architectures
- GPU-enabled MPI
- Using compiler features
- System level debugging
- 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
- Setting the number of CUs
- File structure (Linux FHS)
- GPU isolation techniques
- Using CMake
- ROCm & PCIe atomics
- Inception v3 with PyTorch
- Inference optimization with MIGraphX :::
::::