{"library":"nvidia-nvjitlink-cu12","code":"import os\n\ntry:\n    import cupy as cp\n    print(f\"CuPy is installed. CUDA available: {cp.cuda.is_available()}\")\n    if cp.cuda.is_available():\n        print(f\"CuPy CUDA Device Count: {cp.cuda.runtime.getDeviceCount()}\")\nexcept ImportError:\n    print(\"CuPy not installed. Install with `pip install cupy-cuda12x` to verify CUDA environment.\")\n\ntry:\n    import numba.cuda\n    print(f\"Numba CUDA is installed. CUDA available: {numba.cuda.is_available()}\")\n    if numba.cuda.is_available():\n        print(f\"Numba CUDA Device Count: {numba.cuda.count_devices()}\")\nexcept ImportError:\n    print(\"Numba-CUDA not installed. Install with `pip install numba-cuda` to verify CUDA environment.\")\n\nprint(\"\\nThis output indicates whether higher-level Python libraries can detect and use the CUDA environment, which includes the nvJitLink library provided by nvidia-nvjitlink-cu12.\")","lang":"python","description":"Since `nvidia-nvjitlink-cu12` is a metapackage for native CUDA components, its functionality is indirectly exposed through other Python libraries that depend on the `nvJitLink` library. This quickstart demonstrates how to check for the availability of a CUDA-capable GPU and the CUDA runtime environment using `CuPy` and `Numba-CUDA`, which would implicitly rely on `nvJitLink` if their features that use it are invoked. This verifies that the underlying CUDA toolkit, which `nvidia-nvjitlink-cu12` helps install, is correctly set up.","tag":null,"tag_description":null,"last_tested":"2026-04-24","results":[{"runtime":"python:3.10-alpine","exit_code":1},{"runtime":"python:3.10-slim","exit_code":0},{"runtime":"python:3.11-alpine","exit_code":1},{"runtime":"python:3.11-slim","exit_code":0},{"runtime":"python:3.12-alpine","exit_code":1},{"runtime":"python:3.12-slim","exit_code":0},{"runtime":"python:3.13-alpine","exit_code":1},{"runtime":"python:3.13-slim","exit_code":0},{"runtime":"python:3.9-alpine","exit_code":1},{"runtime":"python:3.9-slim","exit_code":0}]}