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       "@type": "Article",
       "name": "XPU Support",
       "headline": "XPU Support",
       "description": "PyTorch XPU C++ API \u2014 Intel GPU support with device management, streams, and guards.",
       "url": "/api/xpu/index.html",
       "articleBody": "XPU Support# PyTorch provides XPU support for Intel GPU-accelerated tensor operations. The XPU API allows you to manage Intel GPU devices, streams for asynchronous execution, and synchronization. When to use XPU APIs: When running on Intel GPUs (Data Center GPU Max, Arc, etc.) When implementing custom XPU kernels or operations When managing asynchronous execution with XPU streams When writing device-portable code alongside CUDA Basic usage: #include \u003ctorch/torch.h\u003e // Check if XPU is available if (torch::xpu::is_available()) { // Create tensor on XPU auto tensor = torch::randn({2, 3}, torch::device(torch::kXPU)); // Move model to XPU model-\u003eto(torch::kXPU); } Header Files# torch/xpu.h - High-level XPU utilities (device count, availability, seeding) c10/xpu/XPUStream.h - XPU stream management XPU Categories# XPU Streams XPU Utility Functions",
       "author": {
         "@type": "Organization",
         "name": "PyTorch Contributors",
         "url": "https://pytorch.org"
       },
       "image": "https://pytorch.org/docs/stable/_static/img/pytorch_seo.png",
       "mainEntityOfPage": {
         "@type": "WebPage",
         "@id": "/api/xpu/index.html"
       },
       "datePublished": "2023-01-01T00:00:00Z",
       "dateModified": "2023-01-01T00:00:00Z"
     }