From 677cc6be6e86ad0017327451b6b965d0c2ea33b1 Mon Sep 17 00:00:00 2001 From: atalman <7563158+atalman@users.noreply.github.com> Date: Mon, 24 Aug 2026 18:45:50 +0000 Subject: [PATCH] Regenerate additional-platforms assets --- additional-platforms.json | 8 ++++---- assets/quick-start-additional-platforms.js | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/additional-platforms.json b/additional-platforms.json index 93ee7b512f15..6bc1a5603fb1 100644 --- a/additional-platforms.json +++ b/additional-platforms.json @@ -1,7 +1,7 @@ { "schema_version": 1, - "generated_at": "2026-07-28T18:56:10+00:00", - "generator_commit": "5acc4ac", + "generated_at": "2026-08-24T18:45:49+00:00", + "generator_commit": "02a3185", "platforms": { "xpu": { "name": "XPU", @@ -24,7 +24,7 @@ } }, "html": { - "xpu": "

Installing on Intel GPU (XPU) Platform

\n

XPU device backend brings native Intel GPU support to PyTorch, enabling performant training and inference on both Linux and Windows.

\n

Prerequisites

\n

The system with configured Intel GPU card is required. For detailed list of supported devices and driver install instructions refer to Getting Started on Intel GPU.

\n

Installation

\n

pip

\n

Use the pip package manager to install PyTorch with XPU support. Select your preferred options in the selector above to get the installation command.

\n

Verification

\n

To ensure that PyTorch was installed correctly with XPU support, run the following code:

\n
import torch\nprint(torch.__version__)\n\n# Check XPU availability\nif torch.xpu.is_available():\n    print("XPU is available!")\n    print(f"XPU devices: {torch.xpu.device_count()}")\nelse:\n    print("XPU is not available.")\n
\n\n

The following, or a similar output, indicates successful installation:

\n
2.13.0+xpu\nXPU is available!\nXPU devices: 4\n
\n\n

Documentation

\n

For more information, please visit the torch.xpu.

", - "ascend_npu": "

Installing on Ascend NPU

\n

Ascend for PyTorch (TorchNPU) 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.

\n

Prerequisites

\n

Hardware Requirements

\n\n

Software Requirements

\n\n

Before installing PyTorch with Ascend NPU support, you must install the CANN toolkit. Download it from the Ascend Community and follow the CANN Installation Guide.

\n

Installation

\n

pip

\n
pip3 install torch==2.10.0 --index-url https://download.pytorch.org/whl/cpu && pip3 install torch-npu==2.10.0\n
\n\n

Use the pip package manager to install PyTorch with Ascend NPU support. Please refer to the installation page and select your preferred options in the selector above to get the installation command.

\n

Verification

\n

To ensure that PyTorch was installed correctly with Ascend NPU support, run the following code:

\n
import torch\nimport torch_npu\n\nx = torch.randn(2, 2, device="npu")\ny = torch.randn(2, 2, device="npu")\nz = x.mm(y)\n\nprint(z)\n
\n\n

The following, or a similar output, indicates successful installation:

\n
tensor([[-0.0515,  0.3664],\n        [-0.1258, -0.5425]],  device='npu:0')\n
\n\n

Documentation

\n

For more information, please visit:

\n" + "ascend_npu": "

Installing on Ascend NPU

\n

Ascend for PyTorch (TorchNPU) 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.

\n

Prerequisites

\n

Hardware Requirements

\n\n

Software Requirements

\n\n

Before installing PyTorch with Ascend NPU support, you must install the CANN toolkit. Download it from the Ascend Community and follow the CANN Installation Guide.

\n

Installation

\n

pip

\n
pip3 install torch==2.10.0 --index-url https://download.pytorch.org/whl/cpu && pip3 install torch-npu==2.10.0\n
\n\n

Use the pip package manager to install PyTorch with Ascend NPU support. Please refer to the installation page and select your preferred options in the selector above to get the installation command.

\n

Verification

\n

To ensure that PyTorch was installed correctly with Ascend NPU support, run the following code:

\n
import torch\nimport torch_npu\n\nx = torch.randn(2, 2, device=\"npu\")\ny = torch.randn(2, 2, device=\"npu\")\nz = x.mm(y)\n\nprint(z)\n
\n\n

The following, or a similar output, indicates successful installation:

\n
tensor([[-0.0515,  0.3664],\n        [-0.1258, -0.5425]],  device='npu:0')\n
\n\n

Documentation

\n

For more information, please visit:

\n", + "xpu": "

Installing on Intel GPU (XPU) Platform

\n

XPU device backend brings native Intel GPU support to PyTorch, enabling performant training and inference on both Linux and Windows.

\n

Prerequisites

\n

The system with configured Intel GPU card is required. For detailed list of supported devices and driver install instructions refer to Getting Started on Intel GPU.

\n

Installation

\n

pip

\n

Use the pip package manager to install PyTorch with XPU support. Select your preferred options in the selector above to get the installation command.

\n

Verification

\n

To ensure that PyTorch was installed correctly with XPU support, run the following code:

\n
import torch\nprint(torch.__version__)\n\n# Check XPU availability\nif torch.xpu.is_available():\n    print(\"XPU is available!\")\n    print(f\"XPU devices: {torch.xpu.device_count()}\")\nelse:\n    print(\"XPU is not available.\")\n
\n\n

The following, or a similar output, indicates successful installation:

\n
2.13.0+xpu\nXPU is available!\nXPU devices: 4\n
\n\n

Documentation

\n

For more information, please visit the torch.xpu.

" } } \ No newline at end of file diff --git a/assets/quick-start-additional-platforms.js b/assets/quick-start-additional-platforms.js index 6997c33ae1fd..bef6c203a0a8 100644 --- a/assets/quick-start-additional-platforms.js +++ b/assets/quick-start-additional-platforms.js @@ -28,8 +28,8 @@ var ecosystemPlatformData = { // HTML content loaded from _get_started/additional_platforms/ directory // (pre-converted by Python script with syntax highlighting) var ecosystemHtmlContent = { - "xpu": "

Installing on Intel GPU (XPU) Platform

\n

XPU device backend brings native Intel GPU support to PyTorch, enabling performant training and inference on both Linux and Windows.

\n

Prerequisites

\n

The system with configured Intel GPU card is required. For detailed list of supported devices and driver install instructions refer to Getting Started on Intel GPU.

\n

Installation

\n

pip

\n

Use the pip package manager to install PyTorch with XPU support. Select your preferred options in the selector above to get the installation command.

\n

Verification

\n

To ensure that PyTorch was installed correctly with XPU support, run the following code:

\n
import torch\nprint(torch.__version__)\n\n# Check XPU availability\nif torch.xpu.is_available():\n    print("XPU is available!")\n    print(f"XPU devices: {torch.xpu.device_count()}")\nelse:\n    print("XPU is not available.")\n
\n\n

The following, or a similar output, indicates successful installation:

\n
2.13.0+xpu\nXPU is available!\nXPU devices: 4\n
\n\n

Documentation

\n

For more information, please visit the torch.xpu.

", - "ascend_npu": "

Installing on Ascend NPU

\n

Ascend for PyTorch (TorchNPU) 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.

\n

Prerequisites

\n

Hardware Requirements

\n\n

Software Requirements

\n\n

Before installing PyTorch with Ascend NPU support, you must install the CANN toolkit. Download it from the Ascend Community and follow the CANN Installation Guide.

\n

Installation

\n

pip

\n
pip3 install torch==2.10.0 --index-url https://download.pytorch.org/whl/cpu && pip3 install torch-npu==2.10.0\n
\n\n

Use the pip package manager to install PyTorch with Ascend NPU support. Please refer to the installation page and select your preferred options in the selector above to get the installation command.

\n

Verification

\n

To ensure that PyTorch was installed correctly with Ascend NPU support, run the following code:

\n
import torch\nimport torch_npu\n\nx = torch.randn(2, 2, device="npu")\ny = torch.randn(2, 2, device="npu")\nz = x.mm(y)\n\nprint(z)\n
\n\n

The following, or a similar output, indicates successful installation:

\n
tensor([[-0.0515,  0.3664],\n        [-0.1258, -0.5425]],  device='npu:0')\n
\n\n

Documentation

\n

For more information, please visit:

\n" + "ascend_npu": "

Installing on Ascend NPU

\n

Ascend for PyTorch (TorchNPU) 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.

\n

Prerequisites

\n

Hardware Requirements

\n\n

Software Requirements

\n\n

Before installing PyTorch with Ascend NPU support, you must install the CANN toolkit. Download it from the Ascend Community and follow the CANN Installation Guide.

\n

Installation

\n

pip

\n
pip3 install torch==2.10.0 --index-url https://download.pytorch.org/whl/cpu && pip3 install torch-npu==2.10.0\n
\n\n

Use the pip package manager to install PyTorch with Ascend NPU support. Please refer to the installation page and select your preferred options in the selector above to get the installation command.

\n

Verification

\n

To ensure that PyTorch was installed correctly with Ascend NPU support, run the following code:

\n
import torch\nimport torch_npu\n\nx = torch.randn(2, 2, device=\"npu\")\ny = torch.randn(2, 2, device=\"npu\")\nz = x.mm(y)\n\nprint(z)\n
\n\n

The following, or a similar output, indicates successful installation:

\n
tensor([[-0.0515,  0.3664],\n        [-0.1258, -0.5425]],  device='npu:0')\n
\n\n

Documentation

\n

For more information, please visit:

\n", + "xpu": "

Installing on Intel GPU (XPU) Platform

\n

XPU device backend brings native Intel GPU support to PyTorch, enabling performant training and inference on both Linux and Windows.

\n

Prerequisites

\n

The system with configured Intel GPU card is required. For detailed list of supported devices and driver install instructions refer to Getting Started on Intel GPU.

\n

Installation

\n

pip

\n

Use the pip package manager to install PyTorch with XPU support. Select your preferred options in the selector above to get the installation command.

\n

Verification

\n

To ensure that PyTorch was installed correctly with XPU support, run the following code:

\n
import torch\nprint(torch.__version__)\n\n# Check XPU availability\nif torch.xpu.is_available():\n    print(\"XPU is available!\")\n    print(f\"XPU devices: {torch.xpu.device_count()}\")\nelse:\n    print(\"XPU is not available.\")\n
\n\n

The following, or a similar output, indicates successful installation:

\n
2.13.0+xpu\nXPU is available!\nXPU devices: 4\n
\n\n

Documentation

\n

For more information, please visit the torch.xpu.

" }; // Get platform IDs from loaded data