cuDNN

The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN is part of the NVIDIA Deep Learning SDK.

Usage

Warning

Only GPU-enabled nodes are able to run the cuDNN library.

See Using GPUs on Stanage for more information on how to request a GPU-enabled node for an interactive session or job submission.

Load the appropriate cuDNN version (and implicitly load a specified CUDA version) with one of the following commands:

module load cuDNN/8.9.2.26-CUDA-12.1.1
module load cuDNN/8.8.0.121-CUDA-12.0.0
module load cuDNN/8.7.0.84-CUDA-11.8.0
module load cuDNN/8.6.0.163-CUDA-11.8.0
module load cuDNN/8.4.1.50-CUDA-11.7.0
module load cuDNN/8.0.4.30-CUDA-11.1.1
module load cuDNN/7.6.4.38-gcccuda-2019b
module load cuDNN/7.6.4.38-gcccuda-2019a
module load cuDNN/7.6.4.38-CUDA-10.0.130
module load cuDNN/7.6.2.24-CUDA-10.1.243
module load cuDNN/7.4.2.24-CUDA-10.0.130

Installation notes

This section is primarily for administrators of the system.

All cuDNN installs were installed using eponymous EasyBuild easyconfigs.

It’s possible to download and compile some sample cuDNN programs from the nVIDIA Developer portal for the purposes of testing a cuDNN install, but it’s easier to build and test a program from a third party.

Save the C++_ program from this blog post as hw.cpp then compile and execute it by running the following on a GPU node (ideally from a batch job):

module purge
module load cuDNN/8.8.0.121-CUDA-12.0.0

g++ -o hw.o -c hw.cpp
nvcc -ccbin g++ -m64 -gencode arch=compute_80,code=sm_80 -o hw hw.o -lcublasLt -lcudart -lcublas -lcudnn -lstdc++ -lm
./hw