Registry / ai-ml / nvidia-nvshmem-cu13

nvidia-nvshmem-cu13

library3.7.2pypypi✓ verified 21d ago

nvidia-nvshmem-cu13 is a binary distribution package providing the NVIDIA NVSHMEM library, compiled specifically for CUDA Toolkit 13.x. NVSHMEM is a communication library that creates a global address space, enabling efficient and scalable data exchange across NVIDIA GPU clusters. This package itself does not contain Python-level bindings; instead, it serves as a foundational runtime dependency for other Python libraries (like `nvshmem-python`, Dask-CUDA, or CuPy) that utilize NVSHMEM's underlying C/C++ capabilities. The current version is 3.6.5.

ai-ml data

pip install nvidia-nvshmem-cu13

package ↗homepage ↗

INSTALL

IMPORT

SIG · NVIDIA-NVSHMEM-CU1

nvidia-nvshmem-cu13

ai-mlpythonv3.7.2

Install

4.1s avg

Import

—

Disk

122MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.0.0a0 · 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

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.000s · 17.8MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 4.1s · import 0.000s · 231MB

122MB installed

● package 122MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

No direct Python imports

✓ N/A

This package provides native NVSHMEM binaries for CUDA 13; it does not contain direct Python modules for import (`import nvidia-nvshmem-cu13` will fail). Python applications requiring NVSHMEM functionality typically use higher-level libraries (e.g., `nvshmem-python`, Dask-CUDA, CuPy) that link against these native libraries.

This quickstart clarifies that `nvidia-nvshmem-cu13` is a binary distribution and does not offer direct Python APIs. Its primary function is to satisfy the underlying native library dependency for other Python libraries that enable NVSHMEM functionality.

# This package (nvidia-nvshmem-cu13) provides native NVSHMEM binaries for CUDA 13. # It does not expose direct Python modules for import or direct usage. # Its role is to serve as a runtime dependency for other Python libraries # (e.g., nvshmem-python, Dask-CUDA, CuPy) that internally leverage NVSHMEM # for high-performance GPU-aware communication. print("`nvidia-nvshmem-cu13` is installed as a system-level dependency.") print("Python applications will use other libraries (e.g., `nvshmem-python`)") print("that link against the NVSHMEM binaries provided by this package.") print("\nTo truly 'use' NVSHMEM, you would typically write code using a library like `nvshmem-python`: ") print("e.g., `pip install nvshmem-python` and then `import nvshmem; nvshmem.init()`") # This quickstart does not include actual NVSHMEM operations because # this specific package does not provide a direct Python API.

Debug

Known issues

gotchaThis `nvidia-nvshmem-cu13` package provides native NVSHMEM binaries for CUDA 13. It does NOT contain direct Python modules or APIs for import (e.g., `import nvidia-nvshmem-cu13` will fail). Its primary role is to act as a system-level runtime dependency for other Python libraries (e.g., `nvshmem-python`, Dask-CUDA, CuPy) that internally utilize NVSHMEM.

fix

Do not attempt to directly import this package in Python. Install higher-level Python libraries (like `nvshmem-python`) to access NVSHMEM functionality via Python, ensuring this package is installed to provide the underlying native library.

affects: All versions

gotchaCUDA Version Specificity: This package (`-cu13`) is built specifically for CUDA Toolkit 13.x. Ensure your system's CUDA installation and any other dependent libraries (e.g., PyTorch, TensorFlow, CuPy, Dask-CUDA) are compatible with CUDA 13.x. Mismatched CUDA versions can lead to runtime errors or crashes.

fix

Verify your CUDA environment (`nvcc --version` or `nvidia-smi`) and ensure all related packages are compiled for the same CUDA major version (e.g., CUDA 13 for `nvidia-nvshmem-cu13`). If necessary, use a different `nvidia-nvshmem-cuXX` package corresponding to your CUDA version.

affects: All versions

gotchaRequires MPI and Multi-GPU Environment: NVSHMEM is designed for high-performance communication across multiple GPUs, typically in a cluster environment. Meaningful usage often requires an MPI runtime (e.g., OpenMPI, MPICH) and multiple NVIDIA GPUs accessible to your application. Running NVSHMEM applications on a single GPU without MPI might still work but won't leverage its full potential and adds unnecessary complexity.

fix

Set up a multi-GPU environment with a compatible MPI implementation (e.g., `mpiexec -n 2 python your_script.py`). Refer to NVSHMEM documentation for proper setup and execution of distributed applications.

affects: All versions

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Version history

3.7.2latest on PyPI · released Jul 17, 2026

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Dependencies

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Agent activity

3 hits · last 30 days

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Resources

packagehttps://pypi.org/project/nvidia-nvshmem-cu13/ ↗

homepagehttps://developer.nvidia.com/cuda-zone ↗