diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..4e174cdb --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,54 @@ +name: Deploy Docs to GitHub Pages + +on: + push: + branches: [develop] + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: pages + cancel-in-progress: false + +jobs: + build: + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5.4.0 + with: + python-version: "3.10" + + - name: Install dependencies + run: pip install -r docs/sphinx/requirements.txt + + - name: Build Sphinx docs + env: + DOCS_BASE_URL: https://amd-tme.github.io/instinct-docs/ + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: sphinx-build -b html docs/ docs/_build/html + + - name: Add .nojekyll + run: touch docs/_build/html/.nojekyll + + - uses: actions/configure-pages@v5 + + - uses: actions/upload-pages-artifact@v4 + with: + path: docs/_build/html + + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-22.04 + needs: build + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/.gitignore b/.gitignore index aaecec1c..5db81a7c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,13 @@ .venv .vscode build +.playwright-mcp/ # documentation artifacts _build/ _images/ _static/ +!docs/_static/ _templates/ _toc.yml docBin/ diff --git a/.wordlist.txt b/.wordlist.txt index 7d016eec..921a2b21 100644 --- a/.wordlist.txt +++ b/.wordlist.txt @@ -73,5 +73,6 @@ DeepSeek DriveNets minimap FFI +Slurm Slurm's WireGuard diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..169a3eaf --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,40 @@ +# Contributing to AMD GPU Systems and Infrastructure documentation + +Thanks for your interest in improving this site. This repository is a documentation-only project built with [Sphinx](https://www.sphinx-doc.org/) and [MyST](https://myst-parser.readthedocs.io/) via [rocm-docs-core](https://github.com/ROCm/rocm-docs-core), and published through Read the Docs. + +## How to propose a change + +1. Fork the repository and create a branch for your change. +2. Edit or add Markdown (MyST) content under `docs/`. +3. Build and review the site locally (see below) before opening a pull request. +4. Open a pull request against the `develop` branch, describing what changed and why. + +Small fixes (typos, broken links, clarifications) are welcome without prior discussion. For larger structural changes (new sections, navigation changes), please open an issue first to discuss the approach. + +## Building the documentation locally + +See the [README](README.md#documentation-build-guide) for full setup instructions. In short: + +```bash +python3 -m venv .venv/docs +source .venv/docs/bin/activate +pip install -r docs/sphinx/requirements.txt +python3 -m sphinx -b html -d _build/doctrees -D language=en ./docs/ docs/_build/html +``` + +Then serve `docs/_build/html/` locally to review your changes before submitting. + +## Linting and spelling + +Pull requests are checked by CI for: + +- **Markdown linting** — standard Markdown formatting rules. +- **Spelling** — checked against a dictionary plus this repo's [`.wordlist.txt`](.wordlist.txt). If you introduce a legitimate technical term, product name, or acronym that isn't recognized, add it to `.wordlist.txt` in the same PR. + +Both checks run automatically on every pull request; please fix any reported issues before requesting review. + +## Reporting issues + +If you find a problem with the documentation but aren't able to submit a fix yourself, please [open an issue](../../issues) describing the page, the problem, and (if applicable) the expected content. + +For security-related concerns, see [SECURITY.md](SECURITY.md) instead of filing a public issue. diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 00000000..00bac7b5 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,369 @@ +Copyright (c) 2026 Advanced Micro Devices, Inc. + +=========================================================================== + +All files exclusive of files in docs/images and docs/system-admin/images +are governed by the following terms: + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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Except for the limited purpose of indicating that +material is shared under a Creative Commons public license or as +otherwise permitted by the Creative Commons policies published at +creativecommons.org/policies, Creative Commons does not authorize the +use of the trademark "Creative Commons" or any other trademark or logo +of Creative Commons without its prior written consent including, +without limitation, in connection with any unauthorized modifications +to any of its public licenses or any other arrangements, +understandings, or agreements concerning use of licensed material. For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. \ No newline at end of file diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 00000000..2b0c5f87 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,12 @@ +# Security Policy + +This repository contains documentation content only (Markdown/MyST sources built with Sphinx). It does not ship runtime or executable code, so the attack surface is limited to the documentation site itself and its build tooling. + +## Reporting a vulnerability + +If you discover a security issue related to this repository (for example, in the published site, its build pipeline, or a dependency), please report it privately rather than opening a public issue: + +- Preferred: use [GitHub's private vulnerability reporting](https://docs.github.com/en/code-security/security-advisories/guidance-on-reporting-and-writing/privately-reporting-a-security-vulnerability) on this repository. +- Alternatively, report to the AMD Product Security Incident Response Team (PSIRT) at [psirt@amd.com](mailto:psirt@amd.com), or see [amd.com/en/corporate/product-security](https://www.amd.com/en/corporate/product-security) for AMD's full disclosure policy. + +Please do not report security vulnerabilities through public GitHub issues, discussions, or pull requests. diff --git a/docs/_static/index.css b/docs/_static/index.css index 94250126..01209d10 100644 --- a/docs/_static/index.css +++ b/docs/_static/index.css @@ -8,8 +8,206 @@ max-width: 1440px !important; width: 100% !important; margin: 0 auto !important; - contain: layout; + contain: layout; } + +/* ─── Landing page only: give the card grid + "on this page" rail extra + room beyond the site's standard 1440px page width. The primary sidebar is + hidden by default here (see the .id-sidebar-toggle-input rules below), so + this 1432px cap is sized to match the main content's width in that default + state — the same effective width the card grid already had when the + sidebar used to always take its 20%-of-container cut out of a wider 1790px + box. If a visitor opts back into the sidebar, the grid shrinks to make + room for it rather than the page growing, which is an acceptable trade for + an opt-in control. Scoped via :has() on our own .id-landing-layout wrapper + so every other page is unaffected. ─── */ +.bd-main .bd-content .bd-article-container:has(.id-landing-layout) { + max-width: 1432px !important; +} +/* Upstream instinct-design.css also caps the nested
itself (and + .bd-page-width, further up the tree) at 1180px for any page with a hero — + both need the same landing-page escape hatch as .bd-article-container + above, or the grid stays starved regardless of the container override. */ +.bd-main .bd-content .bd-article-container:has(.id-landing-layout) > .bd-article { + max-width: 1432px !important; +} +.bd-page-width:has(.id-landing-layout) { + max-width: 1432px !important; +} + +/* The primary ("System Administrators" / "Common Reference" / ...) sidebar + duplicates the landing grid below it, so default it to collapsed here and + let visitors opt back in with the checkbox-driven toggle above. This can't + reuse the theme's own "Toggle primary sidebar" control (rocm_docs_theme's + toggle-primary-sidebar.html + custom.css) — that targets a checkbox + (input#__primary) from an older theme generation that no longer exists in + the pydata-sphinx-theme version this site runs, so the label is dead + markup on every page, not just this one. This is a plain checkbox hack + instead: the input lives inside .id-landing-main, and :has() reaches back + up to .bd-container (the nearest ancestor shared with .bd-sidebar-primary, + which lives in a separate branch of the DOM) to flip its display. */ +.id-sidebar-toggle-input { + position: absolute; + width: 1px; + height: 1px; + overflow: hidden; + opacity: 0; +} +.id-sidebar-toggle-label { + display: inline-flex; + align-items: center; + gap: 0.35rem; + font-size: 0.78rem; + font-weight: 600; + color: var(--id-muted-text); + cursor: pointer; + margin-bottom: 1rem; + user-select: none; +} +html[data-theme="light"] .id-sidebar-toggle-label { + color: var(--id-muted-text-light); +} +.id-sidebar-toggle-label:hover { + color: var(--id-teal); +} +html[data-theme="light"] .id-sidebar-toggle-label:hover { + color: var(--id-secondary); +} +.id-sidebar-toggle-label svg { + width: 18px; + height: 18px; + transition: transform 0.2s ease-in-out; +} +.id-sidebar-toggle-input:checked ~ .id-sidebar-toggle-label svg { + transform: rotate(90deg); +} +/* Desktop only (matches the theme's own $breakpoint-sidebar-primary: lg / + 960px, see pydata-sphinx-theme's _layout.scss) — below that width the + sidebar is already an off-canvas drawer opened via the theme's own mobile + hamburger toggle, which our checkbox has nothing to do with. Forcing + display:none unconditionally would fight that drawer and make it + impossible to open the sidebar at all on mobile. */ +@media (min-width: 960px) { + .bd-container:has(.id-landing-layout) .bd-sidebar-primary { + display: none; + } + .bd-container:has(.id-sidebar-toggle-input:checked) .bd-sidebar-primary { + display: flex; + } + /* rocm_docs_theme's own "Toggle primary sidebar" label (for="__primary") + duplicates our toggle here — it turns out not to be fully dead: a + pydata-sphinx-theme script grabs the first .primary-toggle element on + the page by class (regardless of the stale `for` target) and wires it + to the mobile off-canvas drawer's real checkbox. But that drawer only + has a visual effect below this same 960px breakpoint, so on desktop + clicking it does nothing — pure duplicate clutter next to our working + toggle. Hide it here only; leave it alone below 960px where it's still + the sole way to open the sidebar drawer on the landing page. */ + .bd-container:has(.id-landing-layout) label.primary-toggle { + display: none; + } +} +@media (max-width: 959.98px) { + .id-sidebar-toggle-label { + display: none; + } +} + +.id-landing-layout { + display: grid; + grid-template-columns: minmax(0, 1fr) 240px; + gap: 2.5rem; + align-items: start; +} +.id-landing-main { + min-width: 0; +} + +/* Plain vertical whitespace where the id-glow-line divider used to sit — + the divider itself is gone, but a bare margin-bottom on .id-hero-actions + read as too tight once it was removed. No visual mark, just breathing + room, roughly a

tag's worth. */ +.id-hero-spacer { + height: 10px; +} + +/* Deliberate 4-per-row card grid on the landing page at desktop widths. + The upstream default (auto-fill, minmax(248px, 1fr)) is what subpages + use, but here it would silently drop to 3 or 5 columns depending on + which deck a grid sits in (the tinted "Start Here" deck has its own + padding subpages don't), so pin it explicitly for a consistent look. */ +@media (min-width: 1000px) { + .id-landing-main .id-grid { + grid-template-columns: repeat(4, 1fr); + } +} + +/* "On this page" rail — reuses the same .bd-toc-nav/.page-toc classes and + markup shape as the interior-page Contents sidebar so it inherits the + site-wide Bootstrap scrollspy (data-bs-spy on ) and matches its + look, without depending on the theme's own (heading-driven) secondary + sidebar, which the landing page's raw-HTML sections don't populate. */ +.id-page-rail { + position: sticky; + top: calc(var(--pst-header-height, 4rem) + 1.5rem); + align-self: start; + max-height: calc(100vh - var(--pst-header-height, 4rem) - 3rem); + overflow-y: auto; + padding-top: 0.25rem; +} +.id-page-rail .page-toc.onthispage { + display: flex; + align-items: center; + gap: 0.5rem; + font-size: 0.7rem; + font-weight: 700; + letter-spacing: 0.1em; + text-transform: uppercase; + color: var(--id-muted-text); + margin-bottom: 0.75rem; +} +html[data-theme="light"] .id-page-rail .page-toc.onthispage { + color: var(--id-muted-text-light); +} +/* .bd-toc-nav never normally holds external links (the real Contents rail + is anchor-only), so the theme's external-link icon rule is scoped to + nav.bd-links and doesn't reach here — add it back for our one external + entry (Industries & Verticals) so it matches the sidebar's treatment. */ +.id-page-rail .bd-toc-nav a.reference.external::after { + content: var(--pst-icon-external-link); + font: var(--fa-font-solid); + font-size: 0.75em; + margin-left: 0.3em; +} + +.id-page-rail .bd-toc-nav a.active { + color: var(--id-teal); + font-weight: 600; +} +html[data-theme="light"] .id-page-rail .bd-toc-nav a.active { + color: var(--id-secondary); +} + +@media (max-width: 1200px) { + .id-landing-layout { + grid-template-columns: 1fr; + } + .id-page-rail { + display: none; + } +} + +/* Section names are plain links to their subpage, not a collapse toggle — + drop instinct-design.css's disclosure-triangle chevron so the link + doesn't still read as a dropdown, and suppress the browser's default + anchor underline (never needed when this was a

). */ +.id-subhead::before { + display: none; +} +.id-subhead { + text-decoration: none; +} + .small-sd-card-large.sd-card {} #buttonWrapper:hover { border-color: hsla(231, 99%, 66%, 1); @@ -20,10 +218,11 @@ h2 { margin: 0; font-size: 1.5em; } -.card-header { +.bd-main h2.card-header { font-size: 18px; font-weight: 700; line-height: 1.4; + margin-top: 0; margin-bottom: 10px; color: var(--text-color-light); display: -webkit-box; diff --git a/docs/conf.py b/docs/conf.py index 661fd843..cc249045 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,7 +6,10 @@ external_projects = ["gpu-operator", "device-metrics-exporter"] external_projects_path = "projects.yaml" -html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "instinct.docs.amd.com") +html_baseurl = os.environ.get( + "READTHEDOCS_CANONICAL_URL", + os.environ.get("DOCS_BASE_URL", "instinct.docs.amd.com"), +) html_context = {} if os.environ.get("READTHEDOCS", "") == "True": html_context["READTHEDOCS"] = True @@ -22,8 +25,8 @@ html_copy_source = True html_theme = "rocm_docs_theme" html_theme_options = { - "flavor": "instinct", - "link_main_doc": False, + "flavor": "instinct-design", + "link_main_doc": True, "repository_url": "https://github.com/rocm/instinct-docs", "use_repository_button": True, "use_issues_button": True, @@ -41,17 +44,24 @@ html_static_path = ['_static', 'images'] +# Landing-page card layout. Loaded after the instinct-design flavor's +# own sheet so its card rules win on the index page. html_css_files = ["index.css"] # Table of contents external_toc_path = "./sphinx/_toc.yml" -exclude_patterns = ['.venv'] +# system-admin/_cards holds reusable `.id-card` HTML partials pulled in via +# MyST {include} directives (see docs/system-admin/*.md). They aren't +# standalone pages, so keep them out of document discovery / the toctree. +exclude_patterns = ['.venv', 'system-admin/_cards/*'] # Redirects for pages removed in favor of the canonical ROCm-hosted docs. # sphinx-reredirects generates a static meta-refresh/JS redirect file at each # old docname's build path, so old bookmarks/links land on the live page # instead of 404ing. +_GSID_VERTICALS_BASE = "https://instinct.docs.amd.com/projects/gsid-verticals-docs/en/latest" + redirects = { "gpu-arch/gpu-arch": "https://rocm.docs.amd.com/en/latest/reference/gpu-arch/index.html", "gpu-arch/mi100": "https://rocm.docs.amd.com/en/latest/reference/gpu-arch/mi100.html", @@ -59,6 +69,34 @@ "gpu-arch/mi300": "https://rocm.docs.amd.com/en/latest/reference/gpu-arch/mi300.html", "gpu-arch/mi300-mi200-performance-counters": "https://rocm.docs.amd.com/en/latest/reference/gpu-arch/mi300-mi200-performance-counters.html", "gpu-arch/mi350": "https://rocm.docs.amd.com/en/latest/reference/gpu-arch/mi350.html", + # Industries/Verticals content moved to its own project (gsid-verticals-docs). + # Most old docnames map 1:1 to a page there; DevitoPRO and GSplat were dropped + # or never migrated, so those two fall back to the nearest index page. + "vision/index": f"{_GSID_VERTICALS_BASE}/vision/index.html", + "vision/ai": f"{_GSID_VERTICALS_BASE}/vision/ai.html", + "vision/decode": f"{_GSID_VERTICALS_BASE}/vision/decode.html", + "vision/preprocess": f"{_GSID_VERTICALS_BASE}/vision/preprocess.html", + "data-science/index": f"{_GSID_VERTICALS_BASE}/data-science/index.html", + "data-science/hipDF": f"{_GSID_VERTICALS_BASE}/data-science/hipDF.html", + "data-science/hipGRAPH": f"{_GSID_VERTICALS_BASE}/data-science/hipGRAPH.html", + "data-science/hipVS": f"{_GSID_VERTICALS_BASE}/data-science/hipVS.html", + "data-science/hipMM": f"{_GSID_VERTICALS_BASE}/data-science/hipMM.html", + "data-science/hipRAFT": f"{_GSID_VERTICALS_BASE}/data-science/hipRAFT.html", + "life-science/index": f"{_GSID_VERTICALS_BASE}/life-science/index.html", + "life-science/hipCIM": f"{_GSID_VERTICALS_BASE}/life-science/hipCIM.html", + "life-science/MONAI": f"{_GSID_VERTICALS_BASE}/life-science/MONAI.html", + "finance/index": f"{_GSID_VERTICALS_BASE}/finance/index.html", + "finance/xgboost": f"{_GSID_VERTICALS_BASE}/finance/xgboost.html", + "finance/lightgbm": f"{_GSID_VERTICALS_BASE}/finance/lightgbm.html", + "finance/thundergbm": f"{_GSID_VERTICALS_BASE}/finance/thundergbm.html", + "isv-apps/index": f"{_GSID_VERTICALS_BASE}/isv-apps/index.html", + "isv-apps/ansys-fluent": f"{_GSID_VERTICALS_BASE}/isv-apps/ansys-fluent.html", + "isv-apps/ansys-mechanical": f"{_GSID_VERTICALS_BASE}/isv-apps/ansys-mechanical.html", + "isv-apps/cadence-fidelity": f"{_GSID_VERTICALS_BASE}/isv-apps/cadence-fidelity.html", + "isv-apps/devito": f"{_GSID_VERTICALS_BASE}/isv-apps/index.html", + "isv-apps/siemens": f"{_GSID_VERTICALS_BASE}/isv-apps/siemens.html", + "isv-apps/stone-ridge": f"{_GSID_VERTICALS_BASE}/isv-apps/stone-ridge.html", + "simulation/gsplat": f"{_GSID_VERTICALS_BASE}/index.html", } # Generate llms.txt and llms-full.txt after each build (the llms.txt standard, @@ -96,5 +134,4 @@ def collapse_primary_sidebar(app, pagename, templatename, context, doctree): def setup(app): - app.add_css_file("css/index.css") app.connect("html-page-context", collapse_primary_sidebar) diff --git a/docs/data-science/ROCmDS-Blogs.md b/docs/data-science/ROCmDS-Blogs.md deleted file mode 100644 index 8142cb5b..00000000 --- a/docs/data-science/ROCmDS-Blogs.md +++ /dev/null @@ -1,88 +0,0 @@ -# ROCm-DS Blogs - -This page contains a list of blog posts related to the AMD ROCm™ Data Science Toolkit (ROCm-DS) -and its individual components. See [ROCm Blogs](https://rocm.blogs.amd.com/) for -blogs related to all AMD and ROCm products. - -:::::{dropdown} ROCm-DS Blogs -:open: - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: "https://rocm.blogs.amd.com/_images/images-2025-04-21-ROCm-DS.webp" -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Introducing ROCm-DS: Revolutionizing Data Processing with AMD Instinct GPUs

- -

AMD is thrilled to announce the release of ROCm-DS, a toolkit aimed at accelerating data science workloads on AMD Instinct GPUs. -

-::: - -:::: - -::::: - -:::::{dropdown} hipDF Blogs -:open: - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: "https://rocm.blogs.amd.com/_images/artificial-intelligence-cupy_hipdf_portfolio_opt-images-cupy_hipdf.webp" -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

CuPy and hipDF on AMD: The basics and beyond.

- -

Learn how to deploy CuPy and hipDF on AMD GPUs. See their high-performance computing advantages, and use CuPy and hipDF in a detailed example of an investment portfolio allocation optimization using the Markowitz model. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: "https://rocm.blogs.amd.com/_images/artificial-intelligence-hipDF_pandas_accelerated-images-hipDF_Dataframe.webp" -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

DataFrame Acceleration: hipDF and hipDF.pandas on AMD GPUs.

- -

This blog post demonstrates how hipDF significantly enhances and accelerates data manipulation, aggregation, and transformation tasks on AMD hardware using ROCm. -

-::: - -:::: - -::::: - -:::::{dropdown} hipVS Blogs -:open: - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: "https://rocm.blogs.amd.com/_images/software-tools-optimization-hipvs-images-hipvs_thumbnail.webp" -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Accelerating Vector Search: hipVS and hipRAFT on AMD

- -

Learn how hipVS accelerates vector search on AMD Instinct GPUs, with notebook demos for semantic search, RAG, and recommendation systems. -

-::: - -:::: - -::::: diff --git a/docs/data-science/hipDF.md b/docs/data-science/hipDF.md deleted file mode 100644 index f70a4cdc..00000000 --- a/docs/data-science/hipDF.md +++ /dev/null @@ -1,58 +0,0 @@ -# hipDF - -hipDF enables GPU accelerated DataFrames and DataFrame operations. This library, built on top of ROCm™, enables -large-scale data processing on AMD Instinct™ GPUs, allowing you to perform many data manipulation operations -at breakneck speeds. Built on the familiar [Apache Arrow](https://arrow.apache.org/) memory format and using -APIs similar to the well known Python [Pandas library](https://pandas.pydata.org/), hipDF allows you to not -only build accelerated data processing workloads, but also accelerate your existing Pandas applications with minimal -effort. - -By adopting the well-known cuDF API on AMD hardware, hipDF ensures compatibility and ease of use across various -computing environments. This API compatibility enables existing cuDF workloads to be effortlessly transitioned -to run on supported AMD devices, allowing you to use the ROCm platform for all of your data processing tasks. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS_Docs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

Installation instructions, how-to guides, and API reference material can all be found on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/hipDF.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the hipDF source code on Github. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS_Blogs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Blogs

- -

View blog posts related to ROCm-DS and hipDF. -

-::: - -:::: diff --git a/docs/data-science/hipGRAPH.md b/docs/data-science/hipGRAPH.md deleted file mode 100644 index 8eb12c47..00000000 --- a/docs/data-science/hipGRAPH.md +++ /dev/null @@ -1,53 +0,0 @@ -# hipGRAPH - -hipGRAPH enables GPU accelerated complex networks and graphs, and contains a set of well-known graph -algorithms. This library, built on top of ROCm™, enables you to build, analyze, and otherwise manipulate -complex graphs on AMD Instinct™ GPUs. - -hipGRAPH is currently in an Early Access state. Running production workloads is not recommended. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS_Docs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

Installation instructions, how-to guides, and API reference material can all be found on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/hipGRAPH.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the hipGRAPH source code on Github. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS_Blogs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Blogs

- -

View blog posts related to ROCm-DS and hipGRAPH. -

-::: - -:::: diff --git a/docs/data-science/hipMM.md b/docs/data-science/hipMM.md deleted file mode 100644 index 516e7c33..00000000 --- a/docs/data-science/hipMM.md +++ /dev/null @@ -1,37 +0,0 @@ -# hipMM - -HIP Memory Manager (hipMM) provides advanced GPU memory management utilities for a variety of libraries in -the AMD ROCm™ Data Science Toolkit (ROCm-DS). hipMM focuses on improving memory usage efficiency for workloads that -leverage hipDF, hipGRAPH, hipVS, and hipRAFT. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS_Docs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

Installation instructions, how-to guides, and API reference material are on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/hipMM.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the hipMM source code on Github. -

-::: - -:::: diff --git a/docs/data-science/hipRAFT.md b/docs/data-science/hipRAFT.md deleted file mode 100644 index a4f9b6da..00000000 --- a/docs/data-science/hipRAFT.md +++ /dev/null @@ -1,36 +0,0 @@ -# hipRAFT - -hipRAFT contains a variety of fundamental primitives and algorithms for machine learning and data mining -workloads. As part of AMD ROCm™ Data Science Toolkit (ROCm-DS), it provides functionality for other ROCm-DS libraries, including hipDF, hipGRAPH, and hipVS. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS_Docs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

Installation instructions, how-to guides, and API reference material are on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/hipRAFT.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the hipRAFT source code on Github. -

-::: - -:::: diff --git a/docs/data-science/hipVS.md b/docs/data-science/hipVS.md deleted file mode 100644 index 198385b8..00000000 --- a/docs/data-science/hipVS.md +++ /dev/null @@ -1,52 +0,0 @@ -# hipVS - -hipVS enables GPU-accelerated vector search operations on AMD Instinct™ GPUs. As a part of the AMD ROCm™ -Data Science Toolkit (ROCm-DS), hipVS builds upon the core ROCm libraries to accelerate operations such as -approximate and exact nearest neighbor algorithms, as well as a variety of clustering algorithms. hipVS seamlessly -integrates with other ROCm-DS libraries such as hipDF, enabling DataFrames to be used for vector search operations. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS_Docs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

Installation instructions, how-to guides, and API reference material are on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/hipVS.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the hipVS source code on Github. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS_Blogs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Blogs

- -

View blog posts related to ROCm-DS and hipVS. -

-::: - -:::: diff --git a/docs/data-science/index.md b/docs/data-science/index.md deleted file mode 100644 index bdc1bb0a..00000000 --- a/docs/data-science/index.md +++ /dev/null @@ -1,118 +0,0 @@ -# ROCm-DS: The AMD ROCm Data Science Toolkit - -Unlock the future of data science with the AMD ROCm™ Data Science Toolkit (ROCm-DS), an innovative open-source toolkit built on -the powerful ROCm platform. Tap into the unparalleled speed and efficiency of AMD Instinct™ GPUs, as ROCm-DS provides you -with all of the necessary tools to tackle larger datasets and execute complex data science workloads with lightning speed. -Transform your data science capabilities to accelerate both new and existing projects, and boost performance and productivity -across your business. - -With ROCm-DS you have access to a rapidly expanding set of tools, empowering you to build, manage, and run entire data science -workflows directly on AMD Instinct GPUs. Say goodbye to data-loading bottlenecks in AI workloads, accelerate your -traditional data processing and analysis tasks, and manipulate immense datasets quickly and efficiently in GPU memory. -Experience the exhilaration of streamlined AI and data science workloads powered by cutting-edge GPU technology. - -Behind ROCm-DS lies a passionate, driven team dedicated to continuously expanding and improving this toolkit, ensuring you -have everything you need to push the boundaries of data science. Get ready to revolutionize your data processing applications -and unleash new possibilities with ROCm-DS. - -:::::{grid} 2 2 2 2 - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/hipDF.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipDF

- -

GPU Accelerated DataFrames to enhance your data manipulation and analysis tasks with AMD Instinct GPUs. -.

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/hipGRAPH.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipGRAPH

- -

Create, manipulate, and analyze complex networks and graphs on AMD Instinct GPUs. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/hipVS.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipVS

- -

Accelerate a variety of vector search operations on AMD Instinct GPUs. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/hipMM.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipMM

- -

Advanced memory management utilities for the various ROCm-DS libraries. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/hipRAFT.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipRAFT

- -

Fundamental algorithms and primitives used by the various ROCm-DS libraries. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the source code for all ROCm-DS components on Github. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-DS_Blogs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCm-DS Blogs

- -

Browse blogs detailing how to accelerate your data science workloads on AMD Instinct GPUs. -

-:::: - -::::: diff --git a/docs/finance/finance-blogs.md b/docs/finance/finance-blogs.md deleted file mode 100644 index 7ea1d9e6..00000000 --- a/docs/finance/finance-blogs.md +++ /dev/null @@ -1,36 +0,0 @@ -# ROCm-Finance Blogs - -This page contains a list of blog posts related to the AMD ROCm™ Finance Toolkit (ROCm-Finance) -and its individual components. See [ROCm Blogs](https://rocm.blogs.amd.com/) for -blogs related to all AMD and ROCm products. - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/rocm-finance.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Using Gradient Boosting Libraries on MI300X for Financial Risk Prediction

- -

Financial institutions deal with massive datasets and complex models that demand high computational power. -With AMD ROCm™ and GPU-accelerated libraries like LightGBM and ThunderGBM, we can significantly reduce training time while improving model performance.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-6.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Accelerating XGBoost with Dask using multiple AMD GPUs

- -

XGBoost is an optimized library for distributed gradient boosting. It has become the leading machine learning library for solving regression and classification problems.

-::: - -:::: diff --git a/docs/finance/index.md b/docs/finance/index.md deleted file mode 100644 index 4b118c1c..00000000 --- a/docs/finance/index.md +++ /dev/null @@ -1,103 +0,0 @@ -# ROCm-Finance: ROCm toolkit for finance - -ROCm-Finance pulls the trajectory of tomorrow into today: an open toolkit on the [ROCm](https://rocm.docs.amd.com/) stack that -delivers GPU-native gradient-boosting stacks that the industry already trusts. XGBoost, -LightGBM, and ThunderGBM, tuned for [AMD Instinct](https://www.amd.com/en/products/accelerators/instinct.html) -accelerators, so training, scoring, and simulation work land closer to real time than the CPU-era -baselines could achieve. - -ROCm-Finance collapses the distance between signal and decision. The same workloads that once -queued overnight now run in minutes. Risk, fraud detection, forecasting, and simulation pipelines step -into the high-bandwidth GPU computing ROCm was built to serve. ROCm-Finance provides production-oriented kernels, memory paths, and scaling behavior so your boosting jobs feel like -they arrived from the next generation, even on this week's cluster. - -For more information on ROCm-Finance, including comparisons, prerequisites, installation, and deep API -reference, see the [ROCm-Finance documentation](https://rocm.docs.amd.com/projects/rocm-finance/en/latest/index.html). - -:::::{grid} 2 2 2 2 - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-1.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

XGBoost

- -

General-purpose GPU gradient boosting. Start here for high-performance workloads for data-intensive applications. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-2.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

LightGBM

- -

Leaf-wise training. Strong fit when sparsity abounds and dataset size drives the bottleneck. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-3.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ThunderGBM

- -

GPU-oriented boosting for highly parallel, GPU-intensive training and simulation-style runs on high-dimensional datasets. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-4.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

GitHub

- -

Source for all ROCm-Finance libraries on GitHub. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-5.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Examples

- -

Runnable examples on GitHub to explore the code. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-6.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCm-Finance Blogs

- -

Browse blogs detailing how to accelerate your finance workloads using gradient boosting on AMD Instinct GPUs. -

-:::: - -::::: diff --git a/docs/finance/lightgbm.md b/docs/finance/lightgbm.md deleted file mode 100644 index 651444a0..00000000 --- a/docs/finance/lightgbm.md +++ /dev/null @@ -1,41 +0,0 @@ -# LightGBM (ROCm-Finance) - -LightGBM is how ROCm-Finance answers scale: leaf-wise training that shines when dataset size—wide -feature stores, long histories, dense microstructure matrices—would otherwise push -decisions into the next shift. On Instinct, that wall between overnight queues and minutes -thins out; the same boosting idiom, routed through ROCm's high-bandwidth, multi-GPU environment. - -Reach for LightGBM when volume is the bottleneck, and you still want gradient boosting semantics -with GPU-native backing. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-6.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

Installation instructions, how-to guides, and API reference material are available on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-2.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the LightGBM source code on Github. -

-::: - -:::: diff --git a/docs/finance/thundergbm.md b/docs/finance/thundergbm.md deleted file mode 100644 index 0090fd49..00000000 --- a/docs/finance/thundergbm.md +++ /dev/null @@ -1,39 +0,0 @@ -# ThunderGBM (ROCm-Finance) - -Use ThunderGBM when parallelism and raw throughput dominate the story: -massively parallel trees, simulation-scale batches, and scenario grids that want the accelerator -to do the heavy lifting today—not in some speculative later hardware generation. ThunderGBM collapses the -signal-to-decision distance, optimized for highly parallel, -GPU-intensive training runs on [AMD Instinct](https://www.amd.com/en/products/accelerators/instinct.html) silicon. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-6.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

ThunderGBM on ROCm—component documentation is available on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-3.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the ThunderGBM source code on Github. -

-::: - -:::: diff --git a/docs/finance/xgboost.md b/docs/finance/xgboost.md deleted file mode 100644 index e8661c49..00000000 --- a/docs/finance/xgboost.md +++ /dev/null @@ -1,37 +0,0 @@ -# XGBoost (ROCm-Finance) - -XGBoost is the general-purpose engine in ROCm-Finance: tabular risk, fraud detection, pricing-side features, and trading-adjacent workloads. -Use it when you want a familiar level-wise boosting path with broad finance coverage and a -straightforward on-ramp from yesterday's pipelines to tomorrow's throughput. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-6.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

XGBoost on ROCm—installation, tuning, and API reference are available on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/finance-1.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the XGBoost source code on Github. -

-::: - -:::: diff --git a/docs/images/mi3xx-reference.png b/docs/images/mi3xx-reference.png deleted file mode 100644 index c3076309..00000000 Binary files a/docs/images/mi3xx-reference.png and /dev/null differ diff --git a/docs/index.md b/docs/index.md index c62ee76b..8585b1e9 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,805 +1,264 @@ --- -html_theme.sidebar_secondary.remove: true +html_theme.sidebar_secondary.remove: true --- -# AMD Instinct Data Center GPU Documentation -The AMD Instinct Documentation site provides comprehensive guides and technical documentation for system administrators and technical users deploying AMD Instinct Data Center GPUs in enterprise environments. This site focuses on large-scale deployment, cluster management, monitoring, and operational best practices for both HPC and AI workloads. For API documentation and core software stack details, visit the [ROCm documentation](https://rocm.docs.amd.com). +
+
-::::::::::{dropdown} Industries/Verticals -:open: + + -::::::::{tab-set} +
+ AMD Instinct™ · Data Center GPU +
-:::::::{tab-item} Computer Vision +# Systems & Infrastructure Documentation -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/models.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Models and applications

- -

Design, develop, train, finetune, and infer your computer vision models on AMD Instinct GPUs. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/decoding.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Image and video decoding

- -

Decode a wide variety of image and video formats leveraging the speed of AMD Instinct GPUs. +

+Everything you need to deploy, validate, and operate AMD Instinct™ Data Center GPUs at scale — drivers, orchestration, cluster management, and acceptance testing for HPC and AI. For API and software-stack reference, see the ROCm documentation.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/processing.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Image processing

- -

Eliminate the image preprocessing bottleneck in your computer vision workloads with AMD Instinct GPUs. -

-::: - -:::: -::::::: +
+ + Install the GPU driver + + + + Customer Acceptance Guide + +
-:::::::{tab-item} Data Science - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/ROCm-DS.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCm-DS

- -

Accelerate your data science workloads with the AMD ROCm™ Data Science Toolkit (ROCm-DS). -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/hipDF.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipDF

- -

GPU Accelerated DataFrames to enhance your data manipulation and analysis tasks with AMD Instinct™ GPUs. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/hipGRAPH.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipGRAPH

- -

Create, manipulate, and analyze complex networks and graphs on AMD Instinct™ GPUs. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/hipVS.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipVS

- -

Accelerate a variety of vector search operations on AMD Instinct™ GPUs. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/hipMM.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipMM

- -

Advanced memory management utilities for the various ROCm-DS libraries. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/hipRAFT.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipRAFT

- -

Fundamental algorithms and primitives used by the various ROCm-DS libraries. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/ROCm-DS_Blogs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Blogs

- -

Browse blogs detailing how to accelerate your data science workloads on AMD Instinct™ GPUs. -

-::: +
-:::: + -::::::: +
-:::::::{tab-item} Finance +
+
Start here
+

System Administrators

+

Deploy and run AMD Instinct GPUs on bare metal, in containers, and across clusters. These guides are the most frequently updated content on this site.

+
-::::{grid} 2 2 3 4 +
+Bare metal +
-:::{grid-item-card} -:padding: 1 -:img-top: ./images/finance-6.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCm-Finance

- -

Boost your financial workloads with the ROCm Toolkit for Finance (ROCm-Finance). -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/finance-5.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

XGBoost

- -

General-purpose GPU gradient boosting. Start here for high-performance workloads for data-intensive applications. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/finance-10.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

LightGBM

- -

Leaf-wise training is the way to go. LightGBM is a strong fit when sparsity abounds and dataset size drives the bottleneck. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/finance-8.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ThunderGBM

- -

GPU-oriented boosting for highly parallel, GPU-intensive training and simulation-style runs on high-dimensional datasets. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/finance-4.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

GitHub

- -

Source for all ROCm-Finance libraries on GitHub. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/finance-9.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Examples

+ + + Instinct GPU Driver + Install and configure the GPU, including logging and error codes. + -

Runnable examples on GitHub to explore the code. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/finance-7.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCm-Finance Blogs

- -

Browse blogs detailing how to accelerate your finance workloads using gradient boosting on AMD Instinct GPUs. -

-::: - -:::: - -::::::: - -:::::::{tab-item} Life Science -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/ROCm-LS.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCm-LS

- -

Accelerate your life science workloads with the ROCm Toolkit for Life Science (ROCm-LS). -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/hipCIM.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipCIM

- -

Run image processing and computer vision workloads on N-dimensional images for medical imaging workloads on AMD Instinct™ GPUs. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/MONAI.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

MONAI

- -

Train and deploy AI and computer vision models for medical imaging use cases on AMD Instinct™ GPUs. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/ROCm-LS_Blogs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Blogs

+ + + GPU Partitioning + Split compute units and memory to partition a single GPU. + -

Browse blogs detailing how to accelerate your life science workloads on AMD Instinct™ GPUs. -

-::: - -:::: - -::::::: -:::::::{tab-item} Simulation & Modelling Apps -::::::{grid} 2 2 3 4 - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/AnsysFluent-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Ansys Fluent

- -

Ansys Fluent is a computational fluid dynamics (CFD) software that leverages AMD GPU acceleration through ROCm for high-performance simulations. The AMD partnership enables exascale-ready CFD solutions for complex engineering challenges. -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/AnsysMech-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Ansys Mechanical

+ + + AMD SMI + Unified user-space tool to manage and monitor GPUs and drivers. + -

Ansys Mechanical 2023R2+ supports MI200 -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/FCharLES-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Cadence Fidelity LES Solver

- -

Fidelity LES Solver supports MI200. -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/DevitoPRO-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Devito Codes DevitoPRO

- -

DevitoPRO 4.8.2+ supports MI200 and MI300X. -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/Starccm-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Siemens Simcenter STAR-CCM+

- -

Simcenter STAR-CCM+ 2402+ supports MI200. -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/ECHELON-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Stone Ridge Technology ECHELON

- -

ECHELON 2023.3+ supports MI200. -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/gsplat.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

GSplat

- -

Render 3D scenes and models with Gaussian splatting on MI300X. -

-::::: - -:::::: - -::::::: -:::::::::: - -::::::::::{dropdown} System Administrators -:open: - -::::::::{tab-set} - -:::::{tab-item} Bare Metal - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/System-Administrators-Bare-Metal-AMD-GPU-Driver.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct GPU Driver

+ + + ROCm Validation Suite + System validation and hardware diagnostics. + -

Install and configure the GPU. Learn about logging including error codes.

-::: - -:::: - -::::: -:::::{tab-item} Containers and Orchestration Tools - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/gpu-operator.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

GPU Operator

- -

The AMD GPU Operator simplifies the deployment and management of AMD Instinct GPU accelerators within Kubernetes clusters.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/network-operator.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Network Operator

- -

Network Operator simplifies the use of AMD AINICs in Kubernetes environments.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/device-plugin.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Device Plugin

- -

Kubernetes (k8s) device plugin to enable registration of AMD GPU to a container cluster

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/device-metrics-exporter.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Device Metrics Exporter

- -

The AMD Device Metrics Exporter enables Prometheus-format metrics collection for AMD GPUs in HPC and AI environments.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/container-toolkit.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD Container Toolkit

+ + + Customer Acceptance Guide + Configure, validate, benchmark, and baseline Instinct GPUs. + -

The AMD Container Toolkit enables seamless integration of AMD Instinct GPUs with Docker, offering streamlined GPU access, enhanced container runtime support, and optimized performance for ML and HPC workloads.

-::: -:::: - -::::: + + + Cluster Validation Suite + Test scripts that validate AMD AI clusters end to end. + + -:::::{tab-item} Cluster Management +
+
-::::{grid} 2 2 3 4 +
+Containers & orchestration +
-:::{grid-item-card} -:padding: 1 -:img-top: ./images/system-acceptance.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct Customer Acceptance Guide

- -

Comprehensive guide for configuring, validating, benchmarking, and baselining AMD Instinct™ GPUs.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/telemetry.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Cluster Networking Guide

- -

Optimize the network for Instinct GPU applications

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/mi3xx-reference.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

MI3XX Reference Design

+ + + GPU Operator + Deploy and manage Instinct GPUs in Kubernetes clusters. + -

Reference design materials and topology examples for the AMD Instinct MI3XX platform.

-::: -:::: + + + Network Operator + Simplify AMD AINICs in Kubernetes environments. + + -::::: + + + Device Plugin + Register AMD GPUs with a Kubernetes container cluster. + + -:::::{tab-item} Cloud + + + Device Metrics Exporter + Prometheus-format GPU metrics for HPC and AI environments. + + -::::{grid} 2 2 3 4 + + + AMD Container Toolkit + Integrate Instinct GPUs with Docker and container runtimes. + + -:::{grid-item-card} -:padding: 1 -:img-top: ./images/instinct-azure.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct on Azure

+ + + Spur + AI-native job scheduler, drop-in compatible with Slurm, with GPU-first scheduling and Raft-based state. + -

Get started with AMD Instinct on Azure

-::: -:::: +
+
-::::: +
+Cluster, cloud & virtualization +
-:::::{tab-item} Virtualization + + + Enterprise AI + Tools to manage enterprise AI infrastructure at scale. + + -::::{grid} 2 2 3 4 + + + Omnistat + Profile GPU resource utilization across the cluster. + + -:::{grid-item-card} -:padding: 1 -:img-top: ./images/virtualization_image.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct Virtualization Driver

+ + + Cluster Networking + Optimize the network for Instinct GPU applications. + -

Explore the Virtualization Driver for Instinct GPUs.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/instinct_virtualization_SMI.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD SMI Documentation

+ + + + Instinct on Azure + Get started with AMD Instinct GPUs on Microsoft Azure. + -

AMD SMI LIB enables you to manage and monitor AMD Virtualization Enabled GPUs.

-::: -:::: + + + Virtualization Driver + Explore the virtualization driver for Instinct GPUs. + + -::::: + + + AMD SMI for Virtualization + Manage and monitor virtualization-enabled AMD GPUs. + + -:::::::: +
+
-:::::::::: +
-::::::::::{dropdown} Resources -:open: + -::::::::{tab-set} +
-:::::{tab-item} Tools +
+

Common Reference

+

Architecture, programming models, and technical collateral that span every deployment.

+
-::::{grid} 2 2 3 4 +
-:::{grid-item-card} -:padding: 1 -:img-top: ./images/enterpriseAI.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Enterprise AI

+ + + Instinct Micro-architecture + Hardware details for MI350, MI300, MI200, and MI100 accelerators. + -

Tools to help enterprise customers address the growing need for AI infrastructure management.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/omnistat.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Omnistat

- -

Profile across the cluster.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/gpu-operator.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Cluster Validation Suite

- -

CVS is a collection of test scripts that validate AMD AI clusters.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/System-Administrators-Bare-Metal-AMD-AMD-SMI.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD SMI

- -

The AMD System Management Interface (AMD SMI) library offers a unified tool for managing and monitoring GPUs, particularly in high-performance computing environments. It provides a user-space interface that allows applications to control GPU operations, monitor performance, and retrieve information about the system’s drivers and GPUs.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/System-Administrators-Bare-Metal-AMD-Tools.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCmValidationSuite

+ + + + AMD SMI API Reference + Full AMD SMI documentation covering all use cases. + -

System validation and diagnosis

-::: -:::: + + + HIP C++ + Learn the HIP programming model. + + -::::: + + + OpenMP + Explore the OpenMP programming model. + + -:::::{tab-item} Common Reference + + + Technical Information Portal + NDA technical documentation and design collateral. Login required. + + -::::{grid} 2 2 3 4 +
-:::{grid-item-card} -:padding: 1 -:img-top: ./images/instinct-microarchitecture.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct Micro-architecture

- -

Review hardware aspects of the AMD Instinct™ MI350, MI300, MI200 and MI100 series of GPU accelerators.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/System-Administrators-Bare-Metal-AMD-GPU-Partitioning.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

GPU Partitioning

- -

Learn how to split the compute units and memory to partition a GPU.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/hipcpp.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

HIP C++

- -

Learn about HIP programming model.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/virtualization_image.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

OpenMP

- -

Explore the OpenMP programming model.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/secure-docs.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD Technical Information Portal

- -

Additional NDA technical documentation, software, and design collateral for AMD Instinct products; -Login Required

-::: +
-:::: +
+ -::::: + -:::::::: +
+ -:::::::::: + diff --git a/docs/isv-apps/ansys-fluent.md b/docs/isv-apps/ansys-fluent.md deleted file mode 100644 index b49bd9da..00000000 --- a/docs/isv-apps/ansys-fluent.md +++ /dev/null @@ -1,32 +0,0 @@ -# Ansys Fluent - -Ansys Fluent is the industry-leading CFD software tool widely used across aerospace, automotive, energy, high tech, and biomedical industries to simulate complex fluids phenomena and optimize product design. - -[Get Fluent here.](https://www.ansys.com/products/fluids/ansys-fluent) - -## Key Features - -- Advanced Physics Models and High Accuracy -- Supports multiple industries -- GA support for MI210, MI250, MI300X, MI325X - -## Supported Versions - -- 2025 R1 & R2 -- 2024 R1 & R2 - -## Installation and Licensing Information - -- [AMD Infinity Hub Recipe](https://github.com/amd/InfinityHub-CI/tree/main/ansys-fluent) - -## Related News - -- [Groundbreaking Achievement with CFD Simulation on AMD GPUs](https://www.ansys.com/blog/ansys-baker-hughes-groundbreaking-cfd-simulation) -- [AMD Ansys Partnership](https://www.ansys.com/partner-ecosystem/high-performance-computing-partners/amd) -- [Boosting Computational Fluid Dynamics Performance with AMD Instinct™ MI300X](https://rocm.blogs.amd.com/ecosystems-and-partners/ansys-fluent-performance/README.html) -- [Ansys Fluent® Adds AMD Instinct™ MI200 and MI300 Acceleration to Power CFD Simulations](https://www.hpcwire.com/2024/09/23/ansys-fluent-adds-amd-instinct-mi200-and-mi300-acceleration-to-power-cfd-simulations/) - -## Technical Documentation - -- [Installing AMD GPU Drivers](https://www.amd.com/en/support/download/drivers.html) -- [Ansys Fluent GPU Acceleration Guide](https://www.ansys.com/resources/documentation) diff --git a/docs/isv-apps/ansys-mechanical.md b/docs/isv-apps/ansys-mechanical.md deleted file mode 100644 index 3dd82e81..00000000 --- a/docs/isv-apps/ansys-mechanical.md +++ /dev/null @@ -1,33 +0,0 @@ -# Ansys Mechanical - -## Overview - -[Ansys Mechanical](https://www.ansys.com/products/structures/ansys-mechanical) is a leading finite element analysis (FEA) platform used for structural engineering. - -[Get Mechanical here.](https://www.ansys.com/products/structures/ansys-mechanical) - -## Key Features - -- One-stop-shop for multi physics structural analysis -- Industry-leading advanced simulation capabilities -- Interoperability Advantage through seamless integration with industry-standard tools -- GA support on MI210 & MI250. Work in progress support for MI300A, MI300X & MI325X - -## Supported Versions - -- 2025 R1 & R2 -- 2024 R1 & R2 -- 2023 R2 - -## Installation and Licensing Information - -- [AMD Infinity Hub Recipe](https://github.com/amd/InfinityHub-CI/tree/main/ansys-mechanical) - -## Related News - -- [Ansys and AMD Collaborate to Speed Simulation of Large Structural Mechanical Models Up to 6x Faster](https://www.ansys.com/news-center/press-releases/8-24-22-ansys-and-amd-collaborate-to-speed-simulation-of-large-structural-mechanical-models-up-to-6x-faster) -- [Powering Mechanical Simulations: AMD Vs. Intel](https://semiengineering.com/powering-mechanical-simulations-amd-vs-intel) - -## Technical Documentation - -- [Installing AMD GPU Drivers](https://www.amd.com/en/support/download/drivers.html) diff --git a/docs/isv-apps/cadence-fidelity.md b/docs/isv-apps/cadence-fidelity.md deleted file mode 100644 index 28f9b5b6..00000000 --- a/docs/isv-apps/cadence-fidelity.md +++ /dev/null @@ -1,19 +0,0 @@ -# Cadence Fidelity LES Solver - -Cadence Fidelity LES Solver, formerly Cascade CharLES, is the industry’s first high-fidelity computational fluid dynamics (CFD) analysis engine that expands the applicability of large eddy simulations (LES) into the mainstream aerospace, automotive, and turbomachinery domains. - -[Get Fidelity LES Solver here.](https://www.cadence.com/en_US/home/resources/technical-briefs/fidelity-les-solver-tb.html) - -## Key Features - -- Designed to scale, Fidelity LES Solver addresses the most demanding fluid dynamics challenges -- Accurate predictions of complex problems for CFD in aeroacoustics, aerodynamics, combustion, heat transfer, as well as multiphase applications -- GA support on MI210 & MI250 - -## Related News - -- [Fidelity LES Solver](https://www.cadence.com/en_US/home/resources/technical-briefs/fidelity-les-solver-tb.html) - -## Technical Documentation - -- [Installing AMD GPU Drivers](https://www.amd.com/en/support/download/drivers.html) diff --git a/docs/isv-apps/devito.md b/docs/isv-apps/devito.md deleted file mode 100644 index c125f0a7..00000000 --- a/docs/isv-apps/devito.md +++ /dev/null @@ -1,26 +0,0 @@ -# DevitoPRO - -DevitoPRO is a domain-specific language (DSL) and code generation framework designed for highly optimized finite-difference kernels for solutions for seismic imaging and exploration, geophysical research, engineering and environmental sciences. - -[Get Devito here](https://github.com/devitocodes/devito) - -## Key Features - -- HPC Optimization: based on advanced compiler technology -- Symbolic Computation: It allows the definition of operators from high-level symbolic equations, facilitating complex mathematical modeling -- GA support for MI210, MI250, MI300A, MI300X, MI325X - -## Supported Versions - -- Devito & DevitoPRO 4.8.2 though 4.8.14 - -## Related News - -- [AMD Drives Leadership Performance and Energy Efficiency in Supercomputing](https://www.amd.com/en/newsroom/press-releases/2022-11-15-amd-drives-leadership-performance-and-energy-effic.html) -- [Devito revolutionizes high-performance computing for the oil and gas industry with AMD](https://community.amd.com/t5/instinct-accelerators/devito-revolutionizes-high-performance-computing-for-the-oil-and/ba-p/625392) -- [DevitoPRO getting HIP with AMD Instinct™ | Devito Codes](https://www.devitocodes.com/instinct) - -## Technical Documentation - -- [Installing AMD GPU Drivers](https://www.amd.com/en/support/download/drivers.html) -- [Build Recipe](https://github.com/amd/InfinityHub-CI/tree/main/devitopro) diff --git a/docs/isv-apps/images/AnsysFluent-tile.png b/docs/isv-apps/images/AnsysFluent-tile.png deleted file mode 100644 index 11f37894..00000000 Binary files a/docs/isv-apps/images/AnsysFluent-tile.png and /dev/null differ diff --git a/docs/isv-apps/images/AnsysMech-tile.png b/docs/isv-apps/images/AnsysMech-tile.png deleted file mode 100644 index e3fc7f27..00000000 Binary files a/docs/isv-apps/images/AnsysMech-tile.png and /dev/null differ diff --git a/docs/isv-apps/images/DevitoPRO-tile.png b/docs/isv-apps/images/DevitoPRO-tile.png deleted file mode 100644 index fc66e359..00000000 Binary files a/docs/isv-apps/images/DevitoPRO-tile.png and /dev/null differ diff --git a/docs/isv-apps/images/ECHELON-tile.png b/docs/isv-apps/images/ECHELON-tile.png deleted file mode 100644 index 88daa021..00000000 Binary files a/docs/isv-apps/images/ECHELON-tile.png and /dev/null differ diff --git a/docs/isv-apps/images/FCharLES-tile b/docs/isv-apps/images/FCharLES-tile deleted file mode 100644 index 1eabf911..00000000 Binary files a/docs/isv-apps/images/FCharLES-tile and /dev/null differ diff --git a/docs/isv-apps/images/FCharLES-tile.png b/docs/isv-apps/images/FCharLES-tile.png deleted file mode 100644 index 1eabf911..00000000 Binary files a/docs/isv-apps/images/FCharLES-tile.png and /dev/null differ diff --git a/docs/isv-apps/images/Starccm-tile.png b/docs/isv-apps/images/Starccm-tile.png deleted file mode 100644 index 940a3905..00000000 Binary files a/docs/isv-apps/images/Starccm-tile.png and /dev/null differ diff --git a/docs/isv-apps/images/generic.jpg b/docs/isv-apps/images/generic.jpg deleted file mode 100644 index 5598ba57..00000000 Binary files a/docs/isv-apps/images/generic.jpg and /dev/null differ diff --git a/docs/isv-apps/index.md b/docs/isv-apps/index.md deleted file mode 100644 index 8895eee8..00000000 --- a/docs/isv-apps/index.md +++ /dev/null @@ -1,89 +0,0 @@ -# Simulation & Modeling Apps - -::::::{grid} 2 2 2 2 - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/AnsysFluent-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Ansys Fluent

- -

Ansys Fluent is a computational fluid dynamics (CFD) software that leverages AMD GPU acceleration through ROCm for high-performance simulations. The AMD partnership enables exascale-ready CFD solutions for complex engineering challenges. -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/AnsysMech-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Ansys Mechanical

- -

Ansys Mechanical 2023R2+ supports MI200 -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/FCharLES-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Cadence Fidelity LES Solver

- -

Fidelity LES Solver supports MI200. -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/DevitoPRO-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Devito Codes DevitoPRO

- -

DevitoPRO 4.8.2+ supports MI200 and MI300X. -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/Starccm-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Siemens Simcenter STAR-CCM+

- -

Simcenter STAR-CCM+ 2402+ supports MI200. -

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ./images/ECHELON-tile.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Stone Ridge Technology ECHELON

- -

ECHELON 2023.3+ supports MI200. -

-::::: - -:::::: diff --git a/docs/isv-apps/siemens.md b/docs/isv-apps/siemens.md deleted file mode 100644 index efa9832e..00000000 --- a/docs/isv-apps/siemens.md +++ /dev/null @@ -1,25 +0,0 @@ -# Siemens Simcenter STAR-CCM+ - -Simcenter STAR-CCM+ is a best-in-class computational fluid dynamics (CFD) software to enable engineers and analysts to drive accelerated innovation on aerodynamics, turbulence, reacting flows, fluid-structure interaction, and multiphase flows. - -## Key Features - -- Accelerated Product Development to help reduce time-to-market -- Optimized design development thru multi-virtual design alternatives for Cost Savings -- Multi-physics simulation capabilities including fluid dynamics, heat transfer, electromagnetics and structural characteristics for complex product interaction analysis -- GA support for MI210 & MI250 - -## Supported Versions - -- Simcenter STAR-CCM+ 2402 and above - -## Related News - -- [Siemens and AMD Partnership](https://rocm.blogs.amd.com/ecosystems-and-partners/Siemens/README.html) -- [Supercharge your CFD simulations with GPUs](https://blogs.sw.siemens.com/simcenter/cfd-simulations-with-gpus/) -- [FD on GPU. A seamless disruption with Simcenter STAR-CCM+](https://blogs.sw.siemens.com/simcenter/cfd-on-gpu-a-seamless-disruption/) - -## Technical Documentation - -- [Installing AMD GPU Drivers](https://www.amd.com/en/support/download/drivers.html) -- [Build Recipe](https://github.com/amd/InfinityHub-CI/tree/main/siemens-star-ccm) diff --git a/docs/isv-apps/stone-ridge.md b/docs/isv-apps/stone-ridge.md deleted file mode 100644 index bc79fbeb..00000000 --- a/docs/isv-apps/stone-ridge.md +++ /dev/null @@ -1,27 +0,0 @@ -# Stone Ridge Technology ECHELON - -ECHELON is a leading reservoir simulation software serving the petroleum industry, including applications in deep-ocean drilling and unconventional reservoirs - known for its speed and scalability. - -[Get ECHELON here.](https://stoneridgetechnology.com/echelon-reservoir-simulation-software/) - -## Key Features - -- Speed: Utilizes GPU technology to perform simulations much faster than traditional CPU-based solutions -- Scalability: Handles large, complex models efficiently -- Accuracy: Provides precise and reliable simulation results -- GA support for MI210 & MI250 - -## Supported Versions - -- ECHELON 2023.3+ - -## Related News - -- [Stone Ridge Expands Reservoir Simulation Options with AMD Instinct™ Accelerators](https://www.hpcwire.com/2024/06/17/stone-ridge-expands-reservoir-simulation-options-with-amd-instinct-accelerators/) -- [Eni launches new supercomputer HPC6 that ranks No.5. in the TOP500 list](https://www.eni.com/en-IT/media/press-release/2024/11/eni-launches-supercomputer-hpc6-top500-list.html) - -## Technical Documentation - -- [Installing AMD GPU Drivers](https://www.amd.com/en/support/download/drivers.html) -- [Build Recipe](https://github.com/amd/InfinityHub-CI/tree/main/srt-echelon) diff --git a/docs/life-science/MONAI.md b/docs/life-science/MONAI.md deleted file mode 100644 index e85ea464..00000000 --- a/docs/life-science/MONAI.md +++ /dev/null @@ -1,45 +0,0 @@ -# MONAI - -The Medical Open Network for Artificial Intelligence ([MONAI](https://monai.io/)) is a domain-optimized, -open-source framework based on PyTorch designed to facilitate deep learning for medical images. The -[MONAI Model Zoo](https://monai.io/model-zoo.html#/) contains a variety of biomedical imaging models that -may be deployed or fine-tuned to suit your purposes. - -MONAI provides out-of-the-box integration with hipCIM, enabling researcher and healthcare professionals to -streamline scientific imaging pipelines, boost computation performance, and speed up innovation across a -wide array of healthcare use cases. - -MONAI support for AMD is currently in an Early Access state and is intended as a preview to the upcoming -production ready release. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-LS_Docs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

Installation instructions, supported features, and information on the MONAI model zoo can all be found on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/MONAI.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the source code for MONAI on AMD on Github. -

-::: - -:::: diff --git a/docs/life-science/ROCmLS-Blogs.md b/docs/life-science/ROCmLS-Blogs.md deleted file mode 100644 index ffecf22b..00000000 --- a/docs/life-science/ROCmLS-Blogs.md +++ /dev/null @@ -1,79 +0,0 @@ -# ROCm-LS Blogs - -This page contains a list of blog posts related to ROCm-LS and its individual components. Please -see the [ROCm Blogs page](https://rocm.blogs.amd.com/) for blogs related to all AMD and ROCm™ -products. - -:::::{dropdown} ROCm-LS Blogs -:open: - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: "https://rocm.blogs.amd.com/_images/images-2025-06-30-rocm-ls.webp" -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Introducing ROCm-LS: Accelerating Life Science Workloads with AMD Instinct™ GPUs

- -

AMD is thrilled to announce the release of ROCm-LS, a toolkit aimed at accelerating life science workloads on AMD Instinct™ GPUs. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: "https://www.amd.com/content/dam/amd/en/images/blogs/designs/5160000-woman-in-a-workstation-blog.jpg" -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Supercharging Genomics Alignment with AMD Instinct™ GPUs

- -

Bringing minimap2 DNA/RNA alignment closer to production-scale sequencing analysis with ROCm and HIP. -

-::: - -:::: - -::::: - -:::::{dropdown} Medical Imaging Blogs -:open: - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: "https://rocm.blogs.amd.com/_images/artificial-intelligence-monai-rocm-images-monai-hipcim-thumbnail.webp" -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Announcing MONAI 1.0.0 for AMD ROCm: Breakthrough AI Acceleration for Medical Imaging Models on AMD Instinct™ GPUs

- -

Learn how to use Medical Open Network for Artificial Intelligence (MONAI) 1.0 on ROCm, with examples and demonstrations. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: "https://rocm.blogs.amd.com/_images/images-2025-06-30-hipcim.webp" -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Announcing hipCIM: A Cutting-Edge Solution for Accelerated Multidimensional Image Processing

- -

Fully utilize the power of AMD's Instinct GPUs to process and interpret detailed multidimensional images with lightning speed. -

-::: - -:::: - -::::: diff --git a/docs/life-science/hipCIM.md b/docs/life-science/hipCIM.md deleted file mode 100644 index ffa6ca4e..00000000 --- a/docs/life-science/hipCIM.md +++ /dev/null @@ -1,40 +0,0 @@ -# hipCIM - -hipCIM enables GPU accelerated image processing and computer vision operations for medical and life science images. -This library, built on top of ROCm™, enables the processing and analysis of multidimensional images used in life -sciences research and medical contexts. - -hipCIM is currently in an Early Access state and is intended as a preview to the production ready release -planned for later this year. - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-LS_Docs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Documentation

- -

Installation instructions, how-to guides, and API reference material can all be found on the ROCm Documentation site. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/hipCIM.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the hipCIM source code on Github. -

-::: - -:::: diff --git a/docs/life-science/index.md b/docs/life-science/index.md deleted file mode 100644 index 1d3ad2d5..00000000 --- a/docs/life-science/index.md +++ /dev/null @@ -1,87 +0,0 @@ -# ROCm-LS: ROCm Toolkit for Life Sciences - -Unlock the transformative power of the ROCm Life Science Toolkit (ROCm-LS), an innovative and robust open-source toolkit built on top of the powerful ROCm™ -platform. Designed with the medical and life sciences fields in mind, ROCm-LS maximizes the computational capabilities of AMD Instinct™ GPUs, enabling you -to execute both new and existing workloads with exceptional speed and efficiency. - -Be a part of the revolution in life sciences research powered by GPUs with the rapidly expanding ROCm-LS suite of tools, empowering researchers and medical -professionals to tackle critical tasks and solve real-world challenges. Fully utilize ROCm-LS to push the boundaries of what is possible in medical and life -science research and to accelerate critical life-saving tasks. - -Dive into a future where medical breakthroughs are accelerated with precision, and solutions to humanity's most pressing health challenges are within arm's -reach. With AMD's powerful Instinct GPUs backing your workloads, ROCm-LS empowers innovation that has the potential to change lives. Join the forefront of -scientific innovation with ROCm-LS and realize the full potential of your research endeavors today! - -:::::{grid} 2 2 2 2 - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/hipCIM.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

hipCIM

- -

GPU Accelerated image processing and computer vision for multidimensional images on AMD Instinct™ GPUs. -.

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/MONAI.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

MONAI

- -

AI and deep learning for medical imaging use cases on AMD Instinct™ GPUs. -.

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-LS.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Github

- -

View the source code for all ROCm-LS components on Github. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-LS_Docs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCm-LS Documentation

- -

View the latest ROCm-LS documentation, including installation instructions and API reference guides, on ROCm Docs. -

-:::: - -::::{grid-item-card} -:padding: 1 -:img-top: ../images/ROCm-LS_Blogs.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCm-LS Blogs

- -

Browse through blogs related to ROCm-LS and its individual components. -

-:::: - -::::: diff --git a/docs/resources/common-reference.md b/docs/resources/common-reference.md deleted file mode 100644 index 5dd609e1..00000000 --- a/docs/resources/common-reference.md +++ /dev/null @@ -1,73 +0,0 @@ -# Common Reference - -::::::{grid} 2 2 3 4 - -:::::{grid-item-card} -:padding: 1 -:img-top: ../system-admin/images/System-Administrators-Bare-Metal-AMD-GPU-Partitioning.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

GPU Partitioning

- -

Learn how to split the compute units and memory to partition a GPU.

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ../images/instinct-microarchitecture.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct Micro-architecture

- -

Review hardware aspects of the AMD Instinct™ MI350, MI300, MI200 and MI100 series of GPU accelerators.

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ../images/hipcpp.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

HIP C++

- -

Learn about HIP programming model.

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ../images/virtualization_image.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

OpenMP

- -

Explore the OpenMP programming model.

-::::: - -:::::{grid-item-card} -:padding: 1 -:img-top: ../images/secure-docs.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD Technical Information Portal

- -

Additional NDA technical documentation, software, and design collateral for AMD Instinct products; -Login Required

-::::: - -:::::: - -:::::::::: diff --git a/docs/resources/index.md b/docs/resources/index.md deleted file mode 100644 index 6d0021cf..00000000 --- a/docs/resources/index.md +++ /dev/null @@ -1,72 +0,0 @@ -# Tools - -::::{grid} 2 2 3 4 - -:::{grid-item-card} -:padding: 1 -:img-top: ../system-admin/images/System-Administrators-Bare-Metal-AMD-AMD-SMI.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD SMI

- -

The AMD System Management Interface (AMD SMI) library offers a unified tool for managing and monitoring GPUs, particularly in high-performance computing environments. It provides a user-space interface that allows applications to control GPU operations, monitor performance, and retrieve information about the system’s drivers and GPUs.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../system-admin/images/enterpriseAI.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Enterprise AI

- -

Tools for enterprise customers for AI infrastructure management.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../system-admin/images/omnistat.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Omnistat

- -

Profile across the cluster.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../system-admin/images/System-Administrators-Bare-Metal-AMD-Tools.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

ROCmValidationSuite

- -

System validation and diagnosis

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../system-admin/images/gpu-operator.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Cluster Validation Suite

- -

CVS is a collection of test scripts that validate AMD AI clusters.

-::: - -:::: - -:::::::::: diff --git a/docs/simulation/gsplat.md b/docs/simulation/gsplat.md deleted file mode 100644 index 863f72a6..00000000 --- a/docs/simulation/gsplat.md +++ /dev/null @@ -1,14 +0,0 @@ -# GSplat - -[GSplat](https://docs.gsplat.studio/main/) is an open-source, GPU-optimized Python library for -differentiable rasterization of 3D Gaussians. GSplat allows you to train and render 3DGS models -on AMD Instinct™ MI300X devices, which enables you to create models and scenes from captured 2D -images and render these in real time. - -## Documentation - -- For full documentation, installation instructions, and API reference guide, refer to the -[ROCm docs site](https://rocm.docs.amd.com/projects/gsplat/en/latest/). -- View the ROCm enabled GSplat code on [Github](https://github.com/ROCm/gsplat). -- Learn more about Gaussian splatting and see examples of its use in the -[GSplat blog](https://rocm.blogs.amd.com/software-tools-optimization/gsplat/README.html). diff --git a/docs/sphinx/_toc.yml.in b/docs/sphinx/_toc.yml.in index 1d342fc1..5718d5cd 100644 --- a/docs/sphinx/_toc.yml.in +++ b/docs/sphinx/_toc.yml.in @@ -1,70 +1,6 @@ root: index.md subtrees: -- caption: Industries/Verticals - entries: - - file: vision/index.md - title: Computer Vision - subtrees: - - entries: - - file: vision/ai.md - title: Models and applications - - file: vision/decode.md - title: Decoding - - file: vision/preprocess.md - title: Image processing - - file: data-science/index.md - title: Data Science (ROCm-DS) - subtrees: - - entries: - - file: data-science/hipDF.md - title: hipDF - - file: data-science/hipGRAPH.md - title: hipGRAPH - - file: data-science/hipVS.md - title: hipVS - - file: data-science/hipMM.md - title: hipMM - - file: data-science/hipRAFT.md - title: hipRAFT - - file: life-science/index.md - title: Life Science (ROCm-LS) - subtrees: - - entries: - - file: life-science/hipCIM.md - title: hipCIM - - entries: - - file: life-science/MONAI.md - title: MONAI - - file: finance/index.md - title: Finance (ROCm-Finance) - subtrees: - - entries: - - file: finance/xgboost.md - title: XGBoost - - file: finance/lightgbm.md - title: LightGBM - - file: finance/thundergbm.md - title: ThunderGBM - - file: isv-apps/index.md - title: Simulation & Modeling Apps - subtrees: - - entries: - - file: isv-apps/ansys-fluent.md - title: Fluent - - file: isv-apps/ansys-mechanical.md - title: Mechanical - - file: isv-apps/cadence-fidelity.md - title: Cadence Fidelity - - file: isv-apps/devito.md - title: DevitoPRO - - file: isv-apps/siemens.md - title: Siemens - - file: isv-apps/stone-ridge.md - title: Stone Ridge - - file: simulation/gsplat.md - title: GSplat - - caption: System Administrators entries: - file: system-admin/bare-metal.md @@ -73,6 +9,16 @@ subtrees: - entries: - url: https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/ title: AMD GPU Driver + - url: https://rocm.docs.amd.com/projects/amdsmi/en/latest/ + title: AMD-SMI + - url: https://rocm.docs.amd.com/projects/cvs/en/latest/index.html + title: Cluster Validation Suite + - url: https://rocm.docs.amd.com/projects/amdsmi/en/latest/conceptual/partition.html + title: GPU Partitioning + - url: https://instinct.docs.amd.com/projects/system-acceptance/en/latest/ + title: Instinct Customer Acceptance Guide + - url: https://rocm.docs.amd.com/projects/ROCmValidationSuite/en/latest/ + title: ROCm Validatation Suite - file: system-admin/co-tools.md title: Containers and Orchestration Tools subtrees: @@ -99,6 +45,10 @@ subtrees: title: Networking - url: https://instinct.docs.amd.com/projects/MI3XX-reference/latest/overview.html title: MI3XX Reference Design + - url: https://docs.amd.com/v/u/en-US/AMD_Instinct_AMD-DriveNets_System_Reference_Architecture_RF-72513 + title: AMD-DriveNets System Reference Architecture + - url: https://rocm.blogs.amd.com/artificial-intelligence/amd-comparative-analysis/README.html + title: Comparative Analysis of Scale-Out RoCE Network Traffic - file: system-admin/cloud.md title: Cloud subtrees: @@ -114,35 +64,11 @@ subtrees: - url: https://instinct.docs.amd.com/projects/amd-smi-virt/en/latest/ title: AMD SMI Documentation -- caption: Resources +# Industries/Verticals content moved to the dedicated gsid-verticals-docs +# project. Local content under vision/, data-science/, life-science/, +# finance/, isv-apps/, simulation/ has been removed from this repo now that +# the migration is complete. +- caption: Industries/Verticals entries: - - file: resources/index.md - title: Tools - subtrees: - - entries: - - url: https://account.amd.com/en/forms/registration/enterpriseai-ea.html - title: Enterprise AI - - url: https://amdresearch.github.io/omnistat/ - title: Omnistat - - url: https://rocm.docs.amd.com/projects/cvs/en/latest/index.html - title: Cluster Validation Suite - - url: https://rocm.docs.amd.com/projects/amdsmi/en/latest/ - title: AMD SMI - - url: https://rocm.docs.amd.com/projects/ROCmValidationSuite/en/latest/ - title: ROCmValidationSuite - - file: resources/common-reference.md - title: Common Reference - subtrees: - - entries: - - url: https://rocm.blogs.amd.com/software-tools-optimization/compute-memory-modes/README.html - title: GPU Partitioning - - url: https://rocm.docs.amd.com/en/latest/reference/gpu-arch/index.html - title: Instinct Micro-architecture - - url: https://rocm.docs.amd.com/projects/amdsmi/en/latest/index.html - title: AMD SMI API Doc - - url: https://rocm.docs.amd.com/projects/HIP/en/latest/index.html - title: HIP C++ - - url: https://rocm.docs.amd.com/projects/llvm-project/en/latest/conceptual/openmp.html - title: OpenMP - - url: https://docs.amd.com/v/u/en-US/ug1729-amd-instinct-accelerators - title: AMD Technical Information Portal + - url: https://instinct.docs.amd.com/projects/gsid-verticals-docs/en/latest/ + title: Industries & Verticals diff --git a/docs/system-admin/_cards/amd-container-toolkit.md b/docs/system-admin/_cards/amd-container-toolkit.md new file mode 100644 index 00000000..35663bfe --- /dev/null +++ b/docs/system-admin/_cards/amd-container-toolkit.md @@ -0,0 +1,6 @@ + + + AMD Container Toolkit + Integrate Instinct GPUs with Docker and container runtimes. + + diff --git a/docs/system-admin/_cards/amd-smi-virtualization.md b/docs/system-admin/_cards/amd-smi-virtualization.md new file mode 100644 index 00000000..7c6a9a4b --- /dev/null +++ b/docs/system-admin/_cards/amd-smi-virtualization.md @@ -0,0 +1,6 @@ + + + AMD SMI for Virtualization + Manage and monitor virtualization-enabled AMD GPUs. + + diff --git a/docs/system-admin/_cards/amd-smi.md b/docs/system-admin/_cards/amd-smi.md new file mode 100644 index 00000000..968b12b7 --- /dev/null +++ b/docs/system-admin/_cards/amd-smi.md @@ -0,0 +1,6 @@ + + + AMD SMI + Unified user-space tool to manage and monitor GPUs and drivers. + + diff --git a/docs/system-admin/_cards/cluster-networking.md b/docs/system-admin/_cards/cluster-networking.md new file mode 100644 index 00000000..bb0f19cc --- /dev/null +++ b/docs/system-admin/_cards/cluster-networking.md @@ -0,0 +1,6 @@ + + + Cluster Networking + Optimize the network for Instinct GPU applications. + + diff --git a/docs/system-admin/_cards/cluster-validation-suite.md b/docs/system-admin/_cards/cluster-validation-suite.md new file mode 100644 index 00000000..7a8e8b33 --- /dev/null +++ b/docs/system-admin/_cards/cluster-validation-suite.md @@ -0,0 +1,6 @@ + + + Cluster Validation Suite + Test scripts that validate AMD AI clusters end to end. + + diff --git a/docs/system-admin/_cards/customer-acceptance-guide.md b/docs/system-admin/_cards/customer-acceptance-guide.md new file mode 100644 index 00000000..5e3a0a5a --- /dev/null +++ b/docs/system-admin/_cards/customer-acceptance-guide.md @@ -0,0 +1,6 @@ + + + Customer Acceptance Guide + Configure, validate, benchmark, and baseline Instinct GPUs. + + diff --git a/docs/system-admin/_cards/device-metrics-exporter.md b/docs/system-admin/_cards/device-metrics-exporter.md new file mode 100644 index 00000000..6fd31e03 --- /dev/null +++ b/docs/system-admin/_cards/device-metrics-exporter.md @@ -0,0 +1,6 @@ + + + Device Metrics Exporter + Prometheus-format GPU metrics for HPC and AI environments. + + diff --git a/docs/system-admin/_cards/device-plugin.md b/docs/system-admin/_cards/device-plugin.md new file mode 100644 index 00000000..2631d453 --- /dev/null +++ b/docs/system-admin/_cards/device-plugin.md @@ -0,0 +1,6 @@ + + + Device Plugin + Register AMD GPUs with a Kubernetes container cluster. + + diff --git a/docs/system-admin/_cards/drivenets-reference.md b/docs/system-admin/_cards/drivenets-reference.md new file mode 100644 index 00000000..d8e6cf98 --- /dev/null +++ b/docs/system-admin/_cards/drivenets-reference.md @@ -0,0 +1,6 @@ + + + AMD-DriveNets System Reference Architecture + End-to-end reference architecture for large-scale AI GPU clusters, pairing AMD Instinct MI350-series (MI355X) compute with the DriveNets AI Fabric. + + diff --git a/docs/system-admin/_cards/enterprise-ai.md b/docs/system-admin/_cards/enterprise-ai.md new file mode 100644 index 00000000..fae50bdf --- /dev/null +++ b/docs/system-admin/_cards/enterprise-ai.md @@ -0,0 +1,6 @@ + + + Enterprise AI + Tools to manage enterprise AI infrastructure at scale. + + diff --git a/docs/system-admin/_cards/gpu-operator.md b/docs/system-admin/_cards/gpu-operator.md new file mode 100644 index 00000000..e3614689 --- /dev/null +++ b/docs/system-admin/_cards/gpu-operator.md @@ -0,0 +1,6 @@ + + + GPU Operator + Deploy and manage Instinct GPUs in Kubernetes clusters. + + diff --git a/docs/system-admin/_cards/gpu-partitioning.md b/docs/system-admin/_cards/gpu-partitioning.md new file mode 100644 index 00000000..ec6ab543 --- /dev/null +++ b/docs/system-admin/_cards/gpu-partitioning.md @@ -0,0 +1,6 @@ + + + GPU Partitioning + Split compute units and memory to partition a single GPU. + + diff --git a/docs/system-admin/_cards/instinct-gpu-driver.md b/docs/system-admin/_cards/instinct-gpu-driver.md new file mode 100644 index 00000000..4bbe4918 --- /dev/null +++ b/docs/system-admin/_cards/instinct-gpu-driver.md @@ -0,0 +1,6 @@ + + + Instinct GPU Driver + Install and configure the GPU, including logging and error codes. + + diff --git a/docs/system-admin/_cards/instinct-on-azure.md b/docs/system-admin/_cards/instinct-on-azure.md new file mode 100644 index 00000000..6382feae --- /dev/null +++ b/docs/system-admin/_cards/instinct-on-azure.md @@ -0,0 +1,6 @@ + + + Instinct on Azure + Get started with AMD Instinct GPUs on Microsoft Azure. + + diff --git a/docs/system-admin/_cards/mi3xx-reference.md b/docs/system-admin/_cards/mi3xx-reference.md new file mode 100644 index 00000000..fd8b16a6 --- /dev/null +++ b/docs/system-admin/_cards/mi3xx-reference.md @@ -0,0 +1,6 @@ + + + MI3XX Reference Design + Reference design materials and topology examples for the AMD Instinct MI3XX platform. + + diff --git a/docs/system-admin/_cards/network-operator.md b/docs/system-admin/_cards/network-operator.md new file mode 100644 index 00000000..6a9eff30 --- /dev/null +++ b/docs/system-admin/_cards/network-operator.md @@ -0,0 +1,6 @@ + + + Network Operator + Simplify AMD AINICs in Kubernetes environments. + + diff --git a/docs/system-admin/_cards/omnistat.md b/docs/system-admin/_cards/omnistat.md new file mode 100644 index 00000000..906b7618 --- /dev/null +++ b/docs/system-admin/_cards/omnistat.md @@ -0,0 +1,6 @@ + + + Omnistat + Profile GPU resource utilization across the cluster. + + diff --git a/docs/system-admin/_cards/roce-comparative-analysis.md b/docs/system-admin/_cards/roce-comparative-analysis.md new file mode 100644 index 00000000..37d995c5 --- /dev/null +++ b/docs/system-admin/_cards/roce-comparative-analysis.md @@ -0,0 +1,6 @@ + + + Comparative Analysis of Scale-Out RoCE Network Traffic + Compares RoCE network traffic patterns and loads across GPT-4, Llama 3, DeepSeek-V2, and Grok 4.0 LLM training. + + diff --git a/docs/system-admin/_cards/rocm-validation-suite.md b/docs/system-admin/_cards/rocm-validation-suite.md new file mode 100644 index 00000000..577b8dd3 --- /dev/null +++ b/docs/system-admin/_cards/rocm-validation-suite.md @@ -0,0 +1,6 @@ + + + ROCm Validation Suite + System validation and hardware diagnostics. + + diff --git a/docs/system-admin/_cards/spur.md b/docs/system-admin/_cards/spur.md new file mode 100644 index 00000000..e01a84dd --- /dev/null +++ b/docs/system-admin/_cards/spur.md @@ -0,0 +1,6 @@ + + + Spur + AI-native job scheduler, drop-in compatible with Slurm, with GPU-first scheduling and Raft-based state. + + diff --git a/docs/system-admin/_cards/virtualization-driver.md b/docs/system-admin/_cards/virtualization-driver.md new file mode 100644 index 00000000..2b86c13a --- /dev/null +++ b/docs/system-admin/_cards/virtualization-driver.md @@ -0,0 +1,6 @@ + + + Virtualization Driver + Explore the virtualization driver for Instinct GPUs. + + diff --git a/docs/system-admin/bare-metal.md b/docs/system-admin/bare-metal.md index f521fe35..0e33e4bb 100644 --- a/docs/system-admin/bare-metal.md +++ b/docs/system-admin/bare-metal.md @@ -1,18 +1,23 @@ -# Bare metal - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/System-Administrators-Bare-Metal-AMD-GPU-Driver.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct GPU Driver

- -

Install and configure the GPU. Learn about logging including error codes.

-::: - -:::: +# Bare metal + +
+ +```{include} _cards/instinct-gpu-driver.md +``` + +```{include} _cards/gpu-partitioning.md +``` + +```{include} _cards/amd-smi.md +``` + +```{include} _cards/rocm-validation-suite.md +``` + +```{include} _cards/customer-acceptance-guide.md +``` + +```{include} _cards/cluster-validation-suite.md +``` + +
diff --git a/docs/system-admin/cloud.md b/docs/system-admin/cloud.md index 1db73caa..4decd8d3 100644 --- a/docs/system-admin/cloud.md +++ b/docs/system-admin/cloud.md @@ -1,18 +1,8 @@ -# Cloud - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/instinct-azure.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct on Azure

- -

Get started with AMD Instinct on Azure

-::: - -:::: +# Cloud + +
+ +```{include} _cards/instinct-on-azure.md +``` + +
diff --git a/docs/system-admin/cluster.md b/docs/system-admin/cluster.md index f07ed125..1d5c0042 100644 --- a/docs/system-admin/cluster.md +++ b/docs/system-admin/cluster.md @@ -1,78 +1,32 @@ -# Cluster Documentation Hub +# Cluster -## Design and Guides +
-::::{grid} 2 4 4 4 +```{include} _cards/enterprise-ai.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/system-acceptance.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct Customer Acceptance Guide

- -

Comprehensive guide for configuring, validating, benchmarking, and baselining AMD Instinct™ GPUs.

-::: +```{include} _cards/omnistat.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/telemetry.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Cluster Networking Guide

- -

Optimize the network for Instinct GPU applications

-::: +```{include} _cards/cluster-networking.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/mi3xx-reference.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

MI3XX Reference Design

- -

Reference design materials and topology examples for the AMD Instinct MI3XX platform.

-::: +```{include} _cards/customer-acceptance-guide.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/amd-drivenets-system-reference.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD-DriveNets System Reference Architecture

- -

Validated, end-to-end reference architecture for building and operating large-scale AI GPU clusters using AMD Instinct MI350-series (MI355X) compute paired with the DriveNets AI Fabric networking solution.

-::: +```{include} _cards/mi3xx-reference.md +``` -:::: +```{include} _cards/drivenets-reference.md +``` -## Articles and Overviews +
-::::{grid} 2 4 4 4 +## Articles and overviews -:::{grid-item-card} -:padding: 1 -:img-top: ./images/RoCE-comparative-analysis.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Comparative Analysis of Scale-Out RoCE Network Traffic Patterns and Loads in Training Large Language Models

- -

Compares RoCE network traffic patterns and loads across GPT-4, Llama 3, DeepSeek-V2, and Grok 4.0 LLM training.

-::: +
-:::: +```{include} _cards/roce-comparative-analysis.md +``` + +
diff --git a/docs/system-admin/co-tools.md b/docs/system-admin/co-tools.md index 8337e0f7..f54c1542 100644 --- a/docs/system-admin/co-tools.md +++ b/docs/system-admin/co-tools.md @@ -1,83 +1,23 @@ # Containers and Orchestration Tools -::::{grid} 2 2 2 2 +
-:::{grid-item-card} -:padding: 1 -:img-top: ./images/gpu-operator.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

GPU Operator

- -

The AMD GPU Operator simplifies the deployment and management of AMD Instinct GPU accelerators within Kubernetes clusters.

-::: +```{include} _cards/gpu-operator.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/network-operator.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Network Operator

- -

Network Operator simplifies the use of AMD AINICs in Kubernetes environments.

-::: +```{include} _cards/network-operator.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/device-plugin.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Device Plugin

- -

Kubernetes (k8s) device plugin to enable registration of AMD GPU to a container cluster

-::: +```{include} _cards/device-plugin.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/device-metrics-exporter.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Device Metrics Exporter

- -

The AMD Device Metrics Exporter enables Prometheus-format metrics collection for AMD GPUs in HPC and AI environments.

-::: +```{include} _cards/device-metrics-exporter.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/container-toolkit.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD Container Toolkit

- -

The AMD Container Toolkit enables seamless integration of AMD Instinct GPUs with Docker, offering streamlined GPU access, enhanced container runtime support, and optimized performance for ML and HPC workloads.

-::: +```{include} _cards/amd-container-toolkit.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/telemetry.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Spur

- -

An AI-native job scheduler written in Rust. Drop-in compatible with Slurm's CLI, REST API, and C FFI — with WireGuard mesh networking, GPU-first scheduling, and Raft-based state that survives restarts.

-::: +```{include} _cards/spur.md +``` -:::: +
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a/docs/system-admin/images/telemetry.jpg and /dev/null differ diff --git a/docs/system-admin/index.md b/docs/system-admin/index.md index ee2fb474..acb32b2c 100644 --- a/docs/system-admin/index.md +++ b/docs/system-admin/index.md @@ -1,109 +1,75 @@ -# System Administrators - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/System-Administrators-Bare-Metal-AMD-GPU-Driver.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct GPU Driver

- -

Install and configure the GPU. Learn about logging including error codes.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/system-acceptance.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

MI300X System Acceptance Tests

- -

Test the correct functioning and optimal performance of server systems equipped with AMD Instinct MI300X GPU accelerators.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/gpu-operator.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

GPU Operator

- -

The AMD GPU Operator simplifies the deployment and management of AMD Instinct GPU accelerators within Kubernetes clusters.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/device-plugin.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Device Plugin

- -

Kubernetes (k8s) device plugin to enable registration of AMD GPU to a container cluster

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/device-metrics-exporter.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Device Metrics Exporter

- -

The AMD Device Metrics Exporter enables Prometheus-format metrics collection for AMD GPUs in HPC and AI environments.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/container-toolkit.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD Container Toolkit

- -

The AMD Container Toolkit enables seamless integration of AMD Instinct GPUs with Docker, offering streamlined GPU access, enhanced container runtime support, and optimized performance for ML and HPC workloads.

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/telemetry.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Cluster Networking

- -

Optimize the network for Instinct GPU applications

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ./images/instinct-azure.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct on Azure

- -

Get started with AMD Instinct on Azure

-::: - -:::: +# System Administrators + +Deploy and run AMD Instinct GPUs on bare metal, in containers, and across clusters. + +## Bare metal + +
+ +```{include} _cards/instinct-gpu-driver.md +``` + +```{include} _cards/gpu-partitioning.md +``` + +```{include} _cards/amd-smi.md +``` + +```{include} _cards/rocm-validation-suite.md +``` + +```{include} _cards/customer-acceptance-guide.md +``` + +```{include} _cards/cluster-validation-suite.md +``` + +
+ +## Containers & orchestration + +
+ +```{include} _cards/gpu-operator.md +``` + +```{include} _cards/network-operator.md +``` + +```{include} _cards/device-plugin.md +``` + +```{include} _cards/device-metrics-exporter.md +``` + +```{include} _cards/amd-container-toolkit.md +``` + +```{include} _cards/spur.md +``` + +
+ +## Cluster, cloud & virtualization + +
+ +```{include} _cards/enterprise-ai.md +``` + +```{include} _cards/omnistat.md +``` + +```{include} _cards/cluster-networking.md +``` + +```{include} _cards/instinct-on-azure.md +``` + +```{include} _cards/virtualization-driver.md +``` + +```{include} _cards/amd-smi-virtualization.md +``` + +
diff --git a/docs/system-admin/virtualization.md b/docs/system-admin/virtualization.md index c716c0a1..7110ae78 100644 --- a/docs/system-admin/virtualization.md +++ b/docs/system-admin/virtualization.md @@ -1,31 +1,11 @@ # Virtualization -::::{grid} 2 2 3 4 +
-:::{grid-item-card} -:padding: 1 -:img-top: ./images/instinct_virtualization.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Instinct Virtualization Driver

- -

Explore the Virtualization Driver for Instinct GPUs.

-::: +```{include} _cards/virtualization-driver.md +``` -:::{grid-item-card} -:padding: 1 -:img-top: ./images/instinct_virtualization_SMI.jpg -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

AMD SMI Documentation

- -

AMD SMI LIB enables you to manage and monitor AMD Virtualization Enabled GPUs.

-::: +```{include} _cards/amd-smi-virtualization.md +``` -:::: +
diff --git a/docs/vision/ai.md b/docs/vision/ai.md deleted file mode 100644 index 9926fc72..00000000 --- a/docs/vision/ai.md +++ /dev/null @@ -1,11 +0,0 @@ -# AI models and applications - -[MIVisionX](https://github.com/ROCm/MIVisionX) is a comprehensive toolkit consisting of a set of computer vision and machine intelligence libraries, utilities, and applications which enable you to build a wide variety of applications and models. MIVisionX delivers a highly optimized implementation of the [Khronos OpenVX Extensions](https://www.khronos.org/openvx/) along with Convolutional Neural Network model compilers and optimizers supporting [ONNX](https://onnx.ai/) and [NNEF](https://www.khronos.org/nnef) exchange formats. This toolkit enables the rapid prototyping and deployment of optimized computer vision and machine learning inference workloads on AMD Instinct GPUs. - -The MIVisionX toolkit provides you with all the necessary tools you need throughout the whole neural network life-cycle. Use these tools to design, develop, quantize, prune, retrain, and infer your neural network on the powerful AMD Instinct family of GPUs. - -## Documentation - -For full documentation, refer to the ROCm docs site: - -- [MIVisionX](https://rocm.docs.amd.com/projects/MIVisionX/en/latest/index.html) diff --git a/docs/vision/decode.md b/docs/vision/decode.md deleted file mode 100644 index db93eb76..00000000 --- a/docs/vision/decode.md +++ /dev/null @@ -1,19 +0,0 @@ -# Video and Image decoding - -Leverage the power of AMD Instinct GPUs to quickly decode a wide variety of image and video formats. Eliminate bottlenecks with these libraries and accelerate your training, finetuning, and inference workloads by ensuring quick and easy access to your data. - -## Video decoding - -Decode videos for use in your computer vision workloads with [rocDecode](https://github.com/ROCm/rocDecode) or its Python bindings, [rocPyDecode](https://github.com/ROCm/rocPyDecode). These libraries support the H.264, H.265, AV1, and VP9 video codecs and enable video decoding on AMD Instinct GPUs. - -## Image decoding - -The [rocJPEG](https://github.com/ROCm/rocJPEG) library enables fast and efficient JPEG decoding on AMD GPUs. This library includes the ability to run on batches to quickly decode large numbers of images simultaneously, fully utilizing the advantages offered by AMD Instinct GPUs. - -## Documentation - -For full documentation, refer to the ROCm docs site: - -- [rocDecode](https://rocm.docs.amd.com/projects/rocDecode/en/latest/) -- [rocPyDecode](https://rocm.docs.amd.com/projects/rocPyDecode/en/latest/index.html) -- [rocJPEG](https://rocm.docs.amd.com/projects/rocJPEG/en/latest/) diff --git a/docs/vision/index.md b/docs/vision/index.md deleted file mode 100644 index 420dd875..00000000 --- a/docs/vision/index.md +++ /dev/null @@ -1,47 +0,0 @@ -# Computer Vision Overview - -::::{grid} 2 2 2 2 - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/models.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Models and applications

- -

Design, develop, train, finetune, and infer your computer vision models on AMD Instinct GPUs. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/decoding.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Image and video decoding

- -

Decode a wide variety of image and video formats leveraging the speed of AMD Instinct GPUs. -

-::: - -:::{grid-item-card} -:padding: 1 -:img-top: ../images/processing.png -:class-img-top: small-sd-card-img-top -:class-body: small-sd-card -:class: small-sd-card -+++ - -

Image processing

- -

Eliminate the image preprocessing bottleneck in your computer vision workloads with AMD Instinct GPUs. -

-::: - -:::: diff --git a/docs/vision/preprocess.md b/docs/vision/preprocess.md deleted file mode 100644 index 09a41852..00000000 --- a/docs/vision/preprocess.md +++ /dev/null @@ -1,18 +0,0 @@ -# Image Preprocessing - -Eliminate the bottleneck caused by slow CPU image processing to further accelerate your computer vision workloads on AMD Instinct GPUs with [rocAL](https://github.com/ROCm/rocAL) and [RPP](https://github.com/ROCm/rpp). These libraries allow you to perform a wide variety of well known computer vision operations with GPU acceleration to augment and transform your images, preparing them for your deep learning workloads. - -## ROCm Augmentation Library - -The [ROCm Augmentation Library (rocAL)](https://github.com/ROCm/rocAL) enables efficient loading and data preprocessing for deep learning applications. This allows for the creation of efficient computer vision pipelines that fully utilize the power of AMD Instinct GPUs. rocAL is designed to efficiently decode and process image and video from a variety of storage formats, saving you time that could be better spent training or running your models. - -## ROCm Performance Primitives - -The [ROCm Performance Primitives (RPP)](https://github.com/ROCm/rpp) library contains a collection of high-performance computer vision operations with HIP, OpenCL, or CPU backends. This library enables you to efficiently apply augmentations, statistical functions, geometric and morphological transformations, popular filters, color model conversions, or other popular computer vision operations to preprocess your images for all of your computer vision workloads. - -## Documentation - -For full documentation, refer to the ROCm docs site: - -- [rocAL](https://rocm.docs.amd.com/projects/rocAL/en/latest/) -- [RPP](https://rocm.docs.amd.com/projects/rpp/en/latest/)