nvidia-cusolver-cu11 provides the native runtime libraries for NVIDIA's cuSOLVER, a high-performance GPU-accelerated library for dense and sparse direct linear solvers and eigenvalue problems. It is a fundamental component of the CUDA Toolkit, enabling accelerated numerical computations on NVIDIA GPUs. This package is intended for runtime use rather than direct development of GPU kernels, typically serving as a foundational dependency for higher-level Python libraries like PyTorch or CuPy in CUDA 11 environments. It is maintained by the Nvidia CUDA Installer Team and generally follows a slow release cadence for new versions.
pip install nvidia-cusolver-cu11No compatibility data collected yet for this library.
This library does not expose a direct Python API for import. Its primary role is to provide native shared libraries that are used by other GPU-accelerated Python packages (e.g., PyTorch, CuPy) or C++/CUDA applications with Python bindings. The quickstart demonstrates how to verify that the underlying CUDA environment, which includes cuSOLVER, is correctly set up and accessible by a common dependent library like PyTorch.
Use a virtual environment and ensure all CUDA-dependent packages specify the same CUDA major version (e.g., all `*-cu11` or all `*-cu12`). For PyTorch, explicitly install the version for your target CUDA (e.g., `pip install torch==X.Y.Z+cu118`).
Integrate with frameworks or libraries that provide Python bindings to cuSOLVER functionalities, such as `torch.linalg` for PyTorch, `cupy.linalg` for CuPy, or the NVIDIA Warp library.
Manually install the appropriate NVIDIA GPU driver for your hardware and operating system. Verify driver compatibility with your CUDA version using `nvidia-smi`.
Run this library only on systems with compatible NVIDIA GPUs and Linux or Windows operating systems.
Review the NVIDIA Proprietary Software License to understand its terms and conditions before using the library in commercial or restricted environments.
Ensure the correct CUDA Toolkit version is installed and its `bin` and `lib64` (or `lib`) directories are added to the system's PATH and LD_LIBRARY_PATH (Linux) or PATH (Windows) environment variables. Verify that the `cusolver` library file (e.g., `cusolver64_10.dll` or `libcusolver.so.11`) exists in one of these paths.
Update your NVIDIA GPU drivers to the latest stable version, ensure your CUDA Toolkit installation is complete and properly linked (not against stub libraries), and consider reinstalling CUDA and dependent libraries (like PyTorch, CuPy, JAX) to resolve underlying conflicts.
Ensure that the `nvidia-cusolver-cu11` package (or the system-wide CUDA Toolkit) provides a cuSOLVER version compatible with the library attempting to use it; this often requires matching the CUDA runtime version precisely with the version used to compile the higher-level library.
Review the input arguments and dimensions for the cuSOLVER function being called (e.g., in CuPy's `linalg.svd`), especially for 'large' matrices, and ensure they adhere to cuSOLVER's specified limits and requirements. Updating CuPy and CUDA Toolkit might also resolve known issues with specific input ranges.