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       "name": "CUDA Support",
       "headline": "CUDA Support",
       "description": "PyTorch CUDA C++ API \u2014 device management, streams, guards, and cuDNN/cuBLAS utilities.",
       "url": "/api/cuda/index.html",
       "articleBody": "CUDA Support# PyTorch provides comprehensive CUDA support for GPU-accelerated tensor operations and neural network training. The CUDA API allows you to manage GPU devices, streams for asynchronous execution, and memory efficiently. When to use CUDA APIs: When you need explicit control over which GPU device to use When implementing custom CUDA kernels or operations When optimizing performance with asynchronous stream execution When managing multi-GPU workloads Basic usage: #include \u003ctorch/torch.h\u003e #include \u003cc10/cuda/CUDAGuard.h\u003e // Check if CUDA is available if (torch::cuda::is_available()) { // Create tensor on GPU auto tensor = torch::randn({2, 3}, torch::device(torch::kCUDA)); // Switch to a specific GPU c10::cuda::CUDAGuard guard(0); // Use GPU 0 // Get the current CUDA stream auto stream = c10::cuda::getCurrentCUDAStream(); // Move model to GPU model-\u003eto(torch::kCUDA); } Header Files# c10/cuda/CUDAStream.h - CUDA stream management c10/cuda/CUDAGuard.h - CUDA device guards ATen/cuda/CUDAContext.h - CUDA context management ATen/cudnn/Descriptors.h - cuDNN tensor descriptors CUDA Categories# CUDA Streams CUDA Guards CUDA Utility Functions",
       "author": {
         "@type": "Organization",
         "name": "PyTorch Contributors",
         "url": "https://pytorch.org"
       },
       "image": "https://pytorch.org/docs/stable/_static/img/pytorch_seo.png",
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         "@id": "/api/cuda/index.html"
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       "datePublished": "2023-01-01T00:00:00Z",
       "dateModified": "2023-01-01T00:00:00Z"
     }