{"library":"nvidia-cuda-cupti-cu11","title":"NVIDIA CUDA CUPTI Runtime (CUDA 11)","description":"This package provides the CUDA Profiling Tools Interface (CUPTI) runtime libraries specifically for CUDA 11.x. It's a low-level dependency, typically used by higher-level deep learning frameworks like PyTorch and TensorFlow to enable GPU acceleration and profiling capabilities. The current version is 11.8.87. Releases are tied to NVIDIA CUDA Toolkit updates.","language":"python","status":"active","last_verified":"Sat Apr 11","install":{"commands":["pip install nvidia-cuda-cupti-cu11"],"cli":null},"imports":[],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import os\ntry:\n    import torch\n    if torch.cuda.is_available():\n        print(\"SUCCESS: CUDA is available via PyTorch!\")\n        print(f\"  CUDA device name: {torch.cuda.get_device_name(0)}\")\n        print(\"  This indicates underlying CUDA libraries (like CUPTI) are functional.\")\n    else:\n        print(\"FAILURE: CUDA is NOT available via PyTorch.\")\n        print(\"  Check NVIDIA drivers, CUDA Toolkit installation, and compatibility.\")\nexcept ImportError:\n    print(\"WARNING: PyTorch not installed. Cannot directly verify CUDA availability.\")\n    print(\"  The 'nvidia-cuda-cupti-cu11' package provides C-level libraries, not direct Python imports.\")\n    print(\"  Its functionality is typically observed indirectly when deep learning frameworks use CUDA.\")","lang":"python","description":"This package itself does not expose direct Python symbols for import. Its successful installation enables other libraries, like PyTorch, to utilize CUDA. This quickstart demonstrates how to verify CUDA availability using PyTorch, which indirectly confirms the underlying CUDA libraries are functioning.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}