SOL > Devices > NVIDIA GPUs

NVIDIA GPUs

Most of the frameworks have been build with a specific CUDA version. Please refer to PyTorch or TensorFlow to find the correct CUDA version.

Requirements

Requirement	Version	Comment
CUDA Toolkit	≥ 11.0
CUDNN	≥ 8.0	see FAQ how to install
`whereis` command	any
`file` command	any

Env Vars

EnvVar	Default	Description
CUDA_HOME	“/usr/local/cuda”	Path to CUDA home dir
NVCPATH		Used as include paths
NVC_INCLUDE_PATH		Used as include paths
NVCPLUS_INCLUDE_PATH		Used as include paths
NVLIBRARY_PATH		Used as library paths

FAQ

SOL tells me that support for NVIDIA is not available?
This is usually caused by a version of SOL without NVIDIA support. Please check if `pip3 list installed \| grep nec-sol-device-nvidia` shows that it is installed, and has the same version as `pip3 list installed \| grep nec-sol`

Which GPUs are supported?
In general all CUDA capable GPU starting from the Kepler architecture (i.e. Tesla K40) are supported.

SOL is unable to load CUDA/CUBLAS/CUDNN

SOL is unable to load CUDA/CUBLAS/CUDNN
The most likely reason is a version mismatch. Please run the following commands: `nvcc --version` `python3 -m pip freeze \| grep nvidia` The CUDA toolkit and the Python packages need to have same major version (e.g., “cu12” or “release 12.*”). Further, these also need to match the version your AI framework was compiled for. Please check the homepage of the AI framework for further details.

The most likely reason is a version mismatch. Please run the following commands:

nvcc --version
python3 -m pip freeze | grep nvidia

The CUDA toolkit and the Python packages need to have same major version (e.g., “cu12” or “release 12.*”). Further, these also need to match the version your AI framework was compiled for. Please check the homepage of the AI framework for further details.

SOL does not load CUDNN

SOL does not load CUDNN
We do not bundle CUDNN with SOL. PyTorch installs it automatically using PYPI. Please run tests described in previous FAQ entry. For TensorFlow or other frameworks you need to install these manually. Either download it from https://developer.nvidia.com/cudnn (requires a free CUDA developer account) or by running `pip3 install nvidia-cudnn-cuXX` (replace XX with 11 or 12 depending on your CUDA version).

We do not bundle CUDNN with SOL. PyTorch installs it automatically using PYPI. Please run tests described in previous FAQ entry. For TensorFlow or other frameworks you need to install these manually. Either download it from https://developer.nvidia.com/cudnn (requires a free CUDA developer account) or by running pip3 install nvidia-cudnn-cuXX (replace XX with 11 or 12 depending on your CUDA version).