[Additional Platforms] Regenerate quick-start-additional-platforms.js and additional-platforms.json (#2127)

This PR is auto-generated. It regenerates the third-party vendor
accelerator picker assets after a change to _additional_platforms/*.json
or _get_started/additional_platforms/*.md.

Co-authored-by: albanD <6359743+albanD@users.noreply.github.com>

Author: pytorchbot

additional-platforms.json

@@ -1,8 +1,20 @@
 {
   "schema_version": 1,
-  "generated_at": "2026-07-27T15:56:17+00:00",
-  "generator_commit": "a665fcd",
+  "generated_at": "2026-07-28T15:06:30+00:00",
+  "generator_commit": "4fbc9b4",
   "platforms": {
+    "xpu": {
+      "name": "XPU",
+      "support_channel": "https://github.com/pytorch/pytorch/issues",
+      "stable": {
+        "linux": "pip3 install torch torchvision --index-url https://download.pytorch.org/whl/xpu",
+        "windows": "pip3 install torch torchvision --index-url https://download.pytorch.org/whl/xpu"
+      },
+      "preview": {
+        "linux": "pip3 install torch torchvision --pre --index-url https://download.pytorch.org/whl/nightly/xpu",
+        "windows": "pip3 install torch torchvision --pre --index-url https://download.pytorch.org/whl/nightly/xpu"
+      }
+    },
     "ascend_npu": {
       "name": "Ascend NPU",
       "support_channel": "https://github.com/Ascend/pytorch/issues",
@@ -12,6 +24,7 @@
     }
   },
   "html": {
+    "xpu": "<h1 id=\"installing-on-intel-gpu-xpu-platform\">Installing on Intel GPU (XPU) Platform</h1>\n<p>XPU device backend brings native Intel GPU support to PyTorch, enabling performant training and inference on both Linux and Windows.</p>\n<h2 id=\"prerequisites\">Prerequisites</h2>\n<p>The system with configured Intel GPU card is required. For detailed list of supported devices and driver install instructions refer to <a href=\"https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html\">Getting Started on Intel GPU</a>.</p>\n<h2 id=\"installation\">Installation</h2>\n<h3 id=\"pip\">pip</h3>\n<p>Use the pip package manager to install PyTorch with XPU support. Select your preferred options in the selector above to get the installation command.</p>\n<h2 id=\"verification\">Verification</h2>\n<p>To ensure that PyTorch was installed correctly with XPU support, run the following code:</p>\n<div class=\"highlight\"><pre><span></span><code><span class=\"kn\">import</span><span class=\"w\"> </span><span class=\"nn\">torch</span>\n<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">__version__</span><span class=\"p\">)</span>\n\n<span class=\"c1\"># Check XPU availability</span>\n<span class=\"k\">if</span> <span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">xpu</span><span class=\"o\">.</span><span class=\"n\">is_available</span><span class=\"p\">():</span>\n    <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s2\">&quot;XPU is available!&quot;</span><span class=\"p\">)</span>\n    <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;XPU devices: </span><span class=\"si\">{</span><span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">xpu</span><span class=\"o\">.</span><span class=\"n\">device_count</span><span class=\"p\">()</span><span class=\"si\">}</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n<span class=\"k\">else</span><span class=\"p\">:</span>\n    <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s2\">&quot;XPU is not available.&quot;</span><span class=\"p\">)</span>\n</code></pre></div>\n\n<p>The following, or a similar output, indicates successful installation:</p>\n<div class=\"highlight\"><pre><span></span><code><span class=\"m\">2</span>.13.0+xpu\nXPU<span class=\"w\"> </span>is<span class=\"w\"> </span>available!\nXPU<span class=\"w\"> </span>devices:<span class=\"w\"> </span><span class=\"m\">4</span>\n</code></pre></div>\n\n<h2 id=\"documentation\">Documentation</h2>\n<p>For more information, please visit the <a href=\"https://docs.pytorch.org/docs/stable/xpu.html\">torch.xpu</a>.</p>",
     "ascend_npu": "<h1 id=\"installing-on-ascend-npu\">Installing on Ascend NPU</h1>\n<p><strong>Ascend for PyTorch (TorchNPU)</strong> is a deep learning adaptation framework built on Ascend. It enables Ascend NPUs to support the PyTorch framework, delivering the powerful computing capabilities of Ascend AI processors to PyTorch developers and users.</p>\n<h2 id=\"prerequisites\">Prerequisites</h2>\n<h3 id=\"hardware-requirements\">Hardware Requirements</h3>\n<ul>\n<li>Ascend for PyTorch supports most Ascend platforms. Please check the hardware compatibility before installing.</li>\n</ul>\n<h3 id=\"software-requirements\">Software Requirements</h3>\n<ul>\n<li>Python &gt;= 3.10</li>\n<li>CANN (Compute Architecture for Neural Networks) toolkit installed &gt;= 8.0</li>\n<li>Ascend driver and firmware installed</li>\n</ul>\n<p>Before installing PyTorch with Ascend NPU support, you must install the CANN toolkit. Download it from the <a href=\"https://www.hiascend.com/en\">Ascend Community</a> and follow the <a href=\"https://www.hiascend.com/en/cann/download\">CANN Installation Guide</a>.</p>\n<h2 id=\"installation\">Installation</h2>\n<h3 id=\"pip\">pip</h3>\n<div class=\"highlight\"><pre><span></span><code>pip3<span class=\"w\"> </span>install<span class=\"w\"> </span><span class=\"nv\">torch</span><span class=\"o\">==</span><span class=\"m\">2</span>.10.0<span class=\"w\"> </span>--index-url<span class=\"w\"> </span>https://download.pytorch.org/whl/cpu<span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span>pip3<span class=\"w\"> </span>install<span class=\"w\"> </span>torch-npu<span class=\"o\">==</span><span class=\"m\">2</span>.10.0\n</code></pre></div>\n\n<p>Use the pip package manager to install PyTorch with Ascend NPU support. Please refer to the <a href=\"https://www.hiascend.com/en/developer/software/ai-frameworks/pytorch/download\">installation page</a> and select your preferred options in the selector above to get the installation command.</p>\n<h2 id=\"verification\">Verification</h2>\n<p>To ensure that PyTorch was installed correctly with Ascend NPU support, run the following code:</p>\n<div class=\"highlight\"><pre><span></span><code><span class=\"kn\">import</span><span class=\"w\"> </span><span class=\"nn\">torch</span>\n<span class=\"kn\">import</span><span class=\"w\"> </span><span class=\"nn\">torch_npu</span>\n\n<span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">randn</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">device</span><span class=\"o\">=</span><span class=\"s2\">&quot;npu&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">y</span> <span class=\"o\">=</span> <span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">randn</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">device</span><span class=\"o\">=</span><span class=\"s2\">&quot;npu&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">z</span> <span class=\"o\">=</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">mm</span><span class=\"p\">(</span><span class=\"n\">y</span><span class=\"p\">)</span>\n\n<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">z</span><span class=\"p\">)</span>\n</code></pre></div>\n\n<p>The following, or a similar output, indicates successful installation:</p>\n<div class=\"highlight\"><pre><span></span><code>tensor<span class=\"o\">([[</span>-0.0515,<span class=\"w\">  </span><span class=\"m\">0</span>.3664<span class=\"o\">]</span>,\n<span class=\"w\">        </span><span class=\"o\">[</span>-0.1258,<span class=\"w\"> </span>-0.5425<span class=\"o\">]]</span>,<span class=\"w\">  </span><span class=\"nv\">device</span><span class=\"o\">=</span><span class=\"s1\">&#39;npu:0&#39;</span><span class=\"o\">)</span>\n</code></pre></div>\n\n<h2 id=\"documentation\">Documentation</h2>\n<p>For more information, please visit:</p>\n<ul>\n<li>Ascend for PyTorch Official Documentation (<a href=\"https://www.hiascend.com/document/detail/en/Pytorch/latest/index/index.html\">English</a>, <a href=\"https://www.hiascend.com/document/detail/zh/Pytorch/2600/index/index.html\">Chinese</a>)</li>\n<li>Ascend Community Portal (<a href=\"https://www.hiascend.com/en\">English</a>, <a href=\"https://www.hiascend.com/\">Chinese</a>)</li>\n<li>CANN Main Page (<a href=\"https://www.hiascend.com/eng/cann\">English</a>, <a href=\"https://www.hiascend.com/cann\">Chinese</a>)</li>\n<li>PyTorch Adaptation for Ascend Portal (<a href=\"https://www.hiascend.com/en/developer/software/ai-frameworks/pytorch\">English</a>, <a href=\"https://www.hiascend.com/cn/developer/software/ai-frameworks/pytorch\">Chinese</a>)</li>\n<li>Ascend Hardware Compatibility (English <em>coming soon</em>, <a href=\"https://www.hiascend.com/hardware/compatibility\">Chinese</a>)</li>\n<li><a href=\"https://github.com/Ascend/pytorch\">GitHub Repository</a></li>\n<li><a href=\"https://pypi.org/project/torch-npu/\">PyPI</a></li>\n</ul>"
   }
 }

assets/quick-start-additional-platforms.js

@@ -4,6 +4,18 @@
 
 // Platform data loaded from JSON files (generated by gen_additional_platforms.py)
 var ecosystemPlatformData = {
+  "xpu": {
+    "name": "XPU",
+    "support_channel": "https://github.com/pytorch/pytorch/issues",
+    "stable": {
+      "linux": "pip3 install torch torchvision --index-url https://download.pytorch.org/whl/xpu",
+      "windows": "pip3 install torch torchvision --index-url https://download.pytorch.org/whl/xpu"
+    },
+    "preview": {
+      "linux": "pip3 install torch torchvision --pre --index-url https://download.pytorch.org/whl/nightly/xpu",
+      "windows": "pip3 install torch torchvision --pre --index-url https://download.pytorch.org/whl/nightly/xpu"
+    }
+  },
   "ascend_npu": {
     "name": "Ascend NPU",
     "support_channel": "https://github.com/Ascend/pytorch/issues",
@@ -16,6 +28,7 @@ var ecosystemPlatformData = {
 // HTML content loaded from _get_started/additional_platforms/ directory
 // (pre-converted by Python script with syntax highlighting)
 var ecosystemHtmlContent = {
+  "xpu": "<h1 id=\"installing-on-intel-gpu-xpu-platform\">Installing on Intel GPU (XPU) Platform</h1>\n<p>XPU device backend brings native Intel GPU support to PyTorch, enabling performant training and inference on both Linux and Windows.</p>\n<h2 id=\"prerequisites\">Prerequisites</h2>\n<p>The system with configured Intel GPU card is required. For detailed list of supported devices and driver install instructions refer to <a href=\"https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html\">Getting Started on Intel GPU</a>.</p>\n<h2 id=\"installation\">Installation</h2>\n<h3 id=\"pip\">pip</h3>\n<p>Use the pip package manager to install PyTorch with XPU support. Select your preferred options in the selector above to get the installation command.</p>\n<h2 id=\"verification\">Verification</h2>\n<p>To ensure that PyTorch was installed correctly with XPU support, run the following code:</p>\n<div class=\"highlight\"><pre><span></span><code><span class=\"kn\">import</span><span class=\"w\"> </span><span class=\"nn\">torch</span>\n<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">__version__</span><span class=\"p\">)</span>\n\n<span class=\"c1\"># Check XPU availability</span>\n<span class=\"k\">if</span> <span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">xpu</span><span class=\"o\">.</span><span class=\"n\">is_available</span><span class=\"p\">():</span>\n    <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s2\">&quot;XPU is available!&quot;</span><span class=\"p\">)</span>\n    <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;XPU devices: </span><span class=\"si\">{</span><span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">xpu</span><span class=\"o\">.</span><span class=\"n\">device_count</span><span class=\"p\">()</span><span class=\"si\">}</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n<span class=\"k\">else</span><span class=\"p\">:</span>\n    <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s2\">&quot;XPU is not available.&quot;</span><span class=\"p\">)</span>\n</code></pre></div>\n\n<p>The following, or a similar output, indicates successful installation:</p>\n<div class=\"highlight\"><pre><span></span><code><span class=\"m\">2</span>.13.0+xpu\nXPU<span class=\"w\"> </span>is<span class=\"w\"> </span>available!\nXPU<span class=\"w\"> </span>devices:<span class=\"w\"> </span><span class=\"m\">4</span>\n</code></pre></div>\n\n<h2 id=\"documentation\">Documentation</h2>\n<p>For more information, please visit the <a href=\"https://docs.pytorch.org/docs/stable/xpu.html\">torch.xpu</a>.</p>",
   "ascend_npu": "<h1 id=\"installing-on-ascend-npu\">Installing on Ascend NPU</h1>\n<p><strong>Ascend for PyTorch (TorchNPU)</strong> is a deep learning adaptation framework built on Ascend. It enables Ascend NPUs to support the PyTorch framework, delivering the powerful computing capabilities of Ascend AI processors to PyTorch developers and users.</p>\n<h2 id=\"prerequisites\">Prerequisites</h2>\n<h3 id=\"hardware-requirements\">Hardware Requirements</h3>\n<ul>\n<li>Ascend for PyTorch supports most Ascend platforms. Please check the hardware compatibility before installing.</li>\n</ul>\n<h3 id=\"software-requirements\">Software Requirements</h3>\n<ul>\n<li>Python &gt;= 3.10</li>\n<li>CANN (Compute Architecture for Neural Networks) toolkit installed &gt;= 8.0</li>\n<li>Ascend driver and firmware installed</li>\n</ul>\n<p>Before installing PyTorch with Ascend NPU support, you must install the CANN toolkit. Download it from the <a href=\"https://www.hiascend.com/en\">Ascend Community</a> and follow the <a href=\"https://www.hiascend.com/en/cann/download\">CANN Installation Guide</a>.</p>\n<h2 id=\"installation\">Installation</h2>\n<h3 id=\"pip\">pip</h3>\n<div class=\"highlight\"><pre><span></span><code>pip3<span class=\"w\"> </span>install<span class=\"w\"> </span><span class=\"nv\">torch</span><span class=\"o\">==</span><span class=\"m\">2</span>.10.0<span class=\"w\"> </span>--index-url<span class=\"w\"> </span>https://download.pytorch.org/whl/cpu<span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span>pip3<span class=\"w\"> </span>install<span class=\"w\"> </span>torch-npu<span class=\"o\">==</span><span class=\"m\">2</span>.10.0\n</code></pre></div>\n\n<p>Use the pip package manager to install PyTorch with Ascend NPU support. Please refer to the <a href=\"https://www.hiascend.com/en/developer/software/ai-frameworks/pytorch/download\">installation page</a> and select your preferred options in the selector above to get the installation command.</p>\n<h2 id=\"verification\">Verification</h2>\n<p>To ensure that PyTorch was installed correctly with Ascend NPU support, run the following code:</p>\n<div class=\"highlight\"><pre><span></span><code><span class=\"kn\">import</span><span class=\"w\"> </span><span class=\"nn\">torch</span>\n<span class=\"kn\">import</span><span class=\"w\"> </span><span class=\"nn\">torch_npu</span>\n\n<span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">randn</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">device</span><span class=\"o\">=</span><span class=\"s2\">&quot;npu&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">y</span> <span class=\"o\">=</span> <span class=\"n\">torch</span><span class=\"o\">.</span><span class=\"n\">randn</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">device</span><span class=\"o\">=</span><span class=\"s2\">&quot;npu&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">z</span> <span class=\"o\">=</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">mm</span><span class=\"p\">(</span><span class=\"n\">y</span><span class=\"p\">)</span>\n\n<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">z</span><span class=\"p\">)</span>\n</code></pre></div>\n\n<p>The following, or a similar output, indicates successful installation:</p>\n<div class=\"highlight\"><pre><span></span><code>tensor<span class=\"o\">([[</span>-0.0515,<span class=\"w\">  </span><span class=\"m\">0</span>.3664<span class=\"o\">]</span>,\n<span class=\"w\">        </span><span class=\"o\">[</span>-0.1258,<span class=\"w\"> </span>-0.5425<span class=\"o\">]]</span>,<span class=\"w\">  </span><span class=\"nv\">device</span><span class=\"o\">=</span><span class=\"s1\">&#39;npu:0&#39;</span><span class=\"o\">)</span>\n</code></pre></div>\n\n<h2 id=\"documentation\">Documentation</h2>\n<p>For more information, please visit:</p>\n<ul>\n<li>Ascend for PyTorch Official Documentation (<a href=\"https://www.hiascend.com/document/detail/en/Pytorch/latest/index/index.html\">English</a>, <a href=\"https://www.hiascend.com/document/detail/zh/Pytorch/2600/index/index.html\">Chinese</a>)</li>\n<li>Ascend Community Portal (<a href=\"https://www.hiascend.com/en\">English</a>, <a href=\"https://www.hiascend.com/\">Chinese</a>)</li>\n<li>CANN Main Page (<a href=\"https://www.hiascend.com/eng/cann\">English</a>, <a href=\"https://www.hiascend.com/cann\">Chinese</a>)</li>\n<li>PyTorch Adaptation for Ascend Portal (<a href=\"https://www.hiascend.com/en/developer/software/ai-frameworks/pytorch\">English</a>, <a href=\"https://www.hiascend.com/cn/developer/software/ai-frameworks/pytorch\">Chinese</a>)</li>\n<li>Ascend Hardware Compatibility (English <em>coming soon</em>, <a href=\"https://www.hiascend.com/hardware/compatibility\">Chinese</a>)</li>\n<li><a href=\"https://github.com/Ascend/pytorch\">GitHub Repository</a></li>\n<li><a href=\"https://pypi.org/project/torch-npu/\">PyPI</a></li>\n</ul>"
 };