NVIDIA CUDA CUPTI Runtime (CUDA 11)

11.8.87 · active · verified Sat Apr 11

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.

Warnings

gotcha Version Mismatch: The '-cu11' suffix denotes compatibility with CUDA 11.x. Using this package with environments set up for CUDA 12.x (e.g., 'nvidia-cuda-runtime-cu12') or other major versions will lead to runtime errors or failed GPU detection. Ensure all 'nvidia-cuda-*' packages match your intended CUDA major version.
Fix: Align all `nvidia-cuda-*` packages (e.g., `nvidia-cuda-cupti-cu11`, `nvidia-cuda-runtime-cu11`) to the same CUDA major version (e.g., `cu11` for CUDA 11.x) that your deep learning framework expects.
gotcha No Direct Python Imports: This package provides C/C++ shared libraries (e.g., .so or .dll files) that deep learning frameworks link against at runtime. It is not intended for direct Python 'import' statements (e.g., `import cupti` will fail).
Fix: Do not attempt to import this package directly in Python code. Its presence is verified indirectly when other CUDA-dependent libraries function correctly.
gotcha Incomplete CUDA Environment: `nvidia-cuda-cupti-cu11` provides only the CUPTI component. A complete CUDA runtime environment typically requires other `nvidia-cuda-*` packages, such as `nvidia-cuda-runtime-cu11`, `nvidia-cuda-nvcc-cu11`, etc., depending on the framework's needs.
Fix: Refer to the documentation of your deep learning framework (e.g., PyTorch, TensorFlow) for a list of recommended `nvidia-cuda-*` packages required for full CUDA functionality.
gotcha PyPI vs. System CUDA Toolkit: These `nvidia-cuda-*` PyPI packages are designed to provide necessary CUDA libraries within your Python environment, often avoiding a full system-wide CUDA Toolkit installation. However, mixing these PyPI packages with a separate, manually installed system-wide NVIDIA CUDA Toolkit can lead to library conflicts (e.g., `LD_LIBRARY_PATH` issues).
Fix: Prefer either a purely PyPI-based CUDA environment (if your framework supports it) or a system-wide CUDA Toolkit. Avoid installing both or ensure proper isolation (e.g., virtual environments) and careful management of library paths if combining them.

Install

pip install nvidia-cuda-cupti-cu11 Install via pip

Quickstart

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.

import os
try:
    import torch
    if torch.cuda.is_available():
        print("SUCCESS: CUDA is available via PyTorch!")
        print(f"  CUDA device name: {torch.cuda.get_device_name(0)}")
        print("  This indicates underlying CUDA libraries (like CUPTI) are functional.")
    else:
        print("FAILURE: CUDA is NOT available via PyTorch.")
        print("  Check NVIDIA drivers, CUDA Toolkit installation, and compatibility.")
except ImportError:
    print("WARNING: PyTorch not installed. Cannot directly verify CUDA availability.")
    print("  The 'nvidia-cuda-cupti-cu11' package provides C-level libraries, not direct Python imports.")
    print("  Its functionality is typically observed indirectly when deep learning frameworks use CUDA.")

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