NVIDIA CUDA Runtime (CUDA 12)

12.9.79 · active · verified Sat Mar 28

This package provides the native CUDA Runtime libraries as Python Wheels, enabling Python applications to leverage GPU acceleration by providing core runtime functionalities. It is part of NVIDIA's initiative to offer native Python support for CUDA, simplifying GPU-based parallel processing for high-performance computing, data science, and AI workloads. The current version is 12.9.79, with releases generally aligning with the NVIDIA CUDA Toolkit.

Warnings

breaking Incompatible NVIDIA GPU drivers with the installed CUDA version can lead to runtime errors (e.g., 'CUDA driver version is insufficient'). The GPU driver must be sufficiently new to support the installed CUDA toolkit version.
Fix: Ensure your NVIDIA GPU drivers are updated to a version compatible with CUDA 12.x. Check NVIDIA's official documentation for driver requirements corresponding to your specific CUDA version.
gotcha This package provides CUDA runtime libraries, not the full CUDA development toolkit (e.g., it does not include `nvcc`). If you need to compile CUDA code (e.g., custom kernels or certain libraries from source), a separate, full CUDA Toolkit installation is required.
Fix: For compilation, download and install the complete NVIDIA CUDA Toolkit from developer.nvidia.com/cuda-downloads alongside this runtime package. Ensure `nvcc` is in your system's PATH.
gotcha CUDA binaries and libraries (like `cudart64_12.dll` on Windows or `libcudart.so.12` on Linux) need to be correctly discoverable via system environment variables (`PATH`, `LD_LIBRARY_PATH` on Linux, or `CUDA_PATH` on Windows). Incorrect setup can result in applications failing to find the CUDA runtime.
Fix: Manually add the CUDA binary and library paths (e.g., `/usr/local/cuda/bin`, `/usr/local/cuda/lib64` on Linux, or `%CUDA_PATH%\bin`, `%CUDA_PATH%\lib\x64` on Windows) to your system's environment variables. Restart your shell or IDE after changes.
gotcha When upgrading CUDA (especially major versions), some older APIs or functions might be deprecated or behave differently, potentially requiring modifications to existing CUDA C/C++ code.
Fix: Consult the NVIDIA CUDA Toolkit Release Notes and Programming Guide for details on deprecated features and API changes during major upgrades. Thoroughly test your applications after upgrading.
gotcha For CUDA versions 12.2 and newer, applications that exhibit hanging during the first kernel launch might resolve the issue by setting the `CUDA_MODULE_LOADING` environment variable to `EAGER`.
Fix: Set `CUDA_MODULE_LOADING=EAGER` in your environment before running the application. For example, `export CUDA_MODULE_LOADING=EAGER && python my_script.py` on Linux/macOS.

Install

pip install nvidia-cuda-runtime-cu12 Install via pip

Imports

Not directly imported

This package primarily provides native shared libraries. High-level Python libraries (e.g., PyTorch, TensorFlow, or the `cuda-python` project's `cuda.core` module) implicitly link against and utilize these runtime components, rather than requiring direct imports of `nvidia_cuda_runtime_cu12` itself in user code.

Users typically interact with CUDA through higher-level frameworks that depend on this runtime. To check for CUDA availability, you would use functions provided by those frameworks.

Quickstart

While `nvidia-cuda-runtime-cu12` itself doesn't offer direct high-level Python APIs, its successful installation allows frameworks like PyTorch to leverage the CUDA runtime. This snippet demonstrates how to verify that a CUDA-enabled PyTorch (which depends on this runtime) can detect and utilize your GPU.

import torch

if torch.cuda.is_available():
    print(f"CUDA is available! Version: {torch.version.cuda}")
    print(f"Number of GPUs: {torch.cuda.device_count()}")
    print(f"Current GPU name: {torch.cuda.get_device_name(0)}")
else:
    print("CUDA is not available. Please check your installation and drivers.")

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