Install & Compatibility
Where this runs
tested against v9.24.0.43 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
build_error
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 20.8s · import 0.000s · 1638.4MB
1638MB installed
● package 1638MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
No direct Python import
✓ This package does not expose a direct Python API for import.
nvidia-cudnn-cu13 installs shared libraries (e.g., .so, .dll) into your environment. Deep learning frameworks (like PyTorch, TensorFlow) then discover and link against these shared libraries at runtime via their C++ backends, rather than through Python's import mechanism. Attempting 'import cudnn' or similar will fail.
Since `nvidia-cudnn-cu13` provides low-level runtime libraries and not a direct Python API, its usage is implicitly managed by deep learning frameworks. This quickstart demonstrates how to check if cuDNN is detected and utilized by PyTorch, assuming a compatible NVIDIA GPU and driver are installed.
import torch
# This package itself has no direct Python API.
# To verify cuDNN is available and used, check a framework that depends on it.
# Example: PyTorch
if torch.cuda.is_available():
print(f"CUDA is available. Device: {torch.cuda.get_device_name(0)}")
if torch.backends.cudnn.is_available():
print(f"cuDNN is available and version: {torch.backends.cudnn.version()}")
print(f"cuDNN enabled: {torch.backends.cudnn.enabled}")
# Optional: Run a simple operation to ensure it uses cuDNN
x = torch.randn(128, 128, 3, 3).cuda()
w = torch.randn(256, 128, 3, 3).cuda()
y = torch.nn.functional.conv2d(x, w)
print("Successfully performed a CUDA/cuDNN operation with PyTorch.")
else:
print("CUDA is available, but cuDNN is NOT detected by PyTorch.")
else:
print("CUDA is not available. cuDNN requires an NVIDIA GPU.")
Debug
Known issues
gotchaThis package is a runtime dependency and does NOT expose a direct Python API for import or use. It installs shared libraries (`.so`, `.dll`) that deep learning frameworks dynamically link against.fixDo not attempt `import nvidia-cudnn-cu13` or similar. Verify its presence via framework-specific checks (e.g., `torch.backends.cudnn.is_available()` for PyTorch).
affects: All versions
breakingThe `cu13` suffix indicates compatibility with CUDA Toolkit 13.x. Installing this version requires a matching CUDA Toolkit and NVIDIA GPU driver on your system. Mismatched versions can lead to runtime errors or performance issues.fixEnsure your installed NVIDIA GPU driver and CUDA Toolkit version (if manually installed) are compatible with CUDA 13.x. For optimal compatibility, use the `pip install nvidia-cudnn-cu13` along with other `nvidia-*cu13` packages (e.g., `nvidia-cuda-runtime-cu13`, `nvidia-cublas-cu13`).
affects: All versions with `cuXY` suffix
gotchaThis package requires an NVIDIA GPU and a compatible NVIDIA driver. It will not provide any benefit or function correctly on systems without NVIDIA hardware.fixEnsure you have an NVIDIA GPU and the latest compatible drivers installed for your operating system. For Linux, ensure `libnvidia-compute`, `libnvidia-decode`, etc., are correctly installed.
affects: All versions
gotchaWhile `pip install nvidia-cudnn-cu13` installs the cuDNN runtime libraries, it does not install the full CUDA Toolkit or its development headers. Deep learning frameworks usually bring their own CUDA dependencies or assume a system-wide CUDA installation.fixFor development or compiling custom CUDA extensions, you might still need to install the full CUDA Toolkit from NVIDIA. For most users, `pip install`ing the `nvidia-*cuXX` packages (runtime, cublas, cudnn) is sufficient for frameworks.
affects: All versions
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Version history
9.24.0.43latest on PyPI · released Jul 2, 2026
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Dependencies
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