How to Install NVIDIA cuDNN on Ubuntu 22.04

Updated on October 6, 2023
How to Install NVIDIA cuDNN on Ubuntu 22.04 header image


CUDA Deep Neural Network (cuDNN) is an NVIDIA library that enables GPU-accelerated computations for deep neural networks. cuDNN allows developers to directly invoke functions to train and run inference on neural networks without having to write the base functions. Neural networks are the building blocks of most modern deep learning applications, such as generative AI models.

This guide explains how to install the NVIDIA cuDNN library on a Ubuntu 22.04 server.


Before you begin:

Install cuDNN

To install cuDNN, you can either use an archived release file or Conda. It's recommended to use the release file as it offers more stability and does not overwrite any system files. Depending on your choice, install cuDNN as described in the steps below.

Install cuDNN Natively (Recommended)

In this section, install cuDNN version 8.9.4 for the CUDA version 12.x natively using the official release file.

  • Using a web browser such as Chrome, visit the cuDNN download page

  • Agree to the cuDNN license agreement

  • Click the Download cuDNN, for CUDA resource link

    Download a cuDNN release file

  • In the open dropdown dialog, click the Local Installer for Linux x86_64 (Tar) link to download the latest release file to your computer

    You can download a .deb release file, but it may overwrite system files upon installation

    This article uses the cuDNN version 8.9.4 for CUDA 12.x with the following filename

  1. In a new terminal window, switch to your downloads directory

     $ cd Downloads/
  2. Using a secure transfer protocol such as SCP, upload the cuDNN release file to your remote server

     $ scp cudnn-linux-x86_64- pythonuser@SERVER-IP:/home/pythonuser/

    Replace pythonuser and SERVER-IP with your actual Vultr server details

  3. When the transfer is successful, navigate to your SSH session and switch to your user home directory

     $ cd /home/pythonuser/
  4. Long list files in the directory

     $ ls -l


     -rw-rw-r-- 1 pythonuser pythonuser 887509908 Sep  9 07:42 cudnn-linux-x86_64-

    Verify that the cuDNN release file is available

  5. Extract files from the cuDNN release file

     $ tar -xf cudnn-linux-x86_64- 
  6. Copy the cuDNN header files to the CUDA include directory

     $ sudo cp cudnn-linux-x86_64-*.h /usr/local/cuda/include/
  7. Copy the cuDNN library files to the CUDA library

     $ cp -P cudnn-linux-x86_64-* /usr/local/cuda/lib64/
  8. Change the library files directory permissions to grant all system users read access to the directory

     $ sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*

Install cuDNN using Conda

To install cuDNN using Conda, verify the latest supported version that matches the CUDA version number. In this section, install cuDNN version for CUDA 11.x using Conda as described below.

  1. Using Conda, install the CUDA toolkit version 11.8.0

     $ conda install -c "nvidia/label/cuda-11.8.0" cuda

    > When installed, follow the Post-installation steps to activate the CUDA Toolkit on your server

  2. Install the latest cuDNN version from the default channel

     $ conda install cudnn=="" -c default

    To install a specific version from a particular channel, use the command syntax conda install cudnn=="x.y.z.w" -c channel-name. The above command installs the cuDNN version from the default channel.

Verify the cuDNN Installation

To test your cuDNN installation, download and run the NVIDIA verification program using the .deb release file as described below.

  • In your web browser, visit the cuDNN download page
  • Agree to the cuDNN software license agreement
  • Find and click the Local installer for Ubuntu22.04 x86_64 (Deb) file link to download the file on your computer

This article uses the cuDNN version 8.9.4 with the following filename

  1. In a new terminal session, switch to your downloads file directory

     $ cd Downloads
  2. Using SCP, upload the file to your remote server

     $ scp cudnn-local-repo-ubuntu2204- pythonuser@SERVER-IP:/home/pythonuser/
  3. In your SSH session, switch to your user's home directory

     $ cd /home/pythonuser/
  4. Install the required dependency libraries

     $ sudo apt install libfreeimage3 libfreeimage-dev
  5. Long list files in the directory

     $ ls -l

    Verify that the .deb file is available

  6. Create a new temporary directory such as deb

     $ mkdir deb
  7. Move the uploaded .deb installer package to the directory

     $ mv cudnn-local-repo-ubuntu2204- deb/
  8. Switch to the directory

     $ cd deb
  9. Using the ar utility, extract the contents from the deb file

     $ ar x cudnn-local-repo-ubuntu2204-
  10. When the extraction is successful, long list files in the directory

     $ ls -l


     -rw-r--r-- 1 pythonuser  pythonuser       1252 Sep  9 08:44 control.tar.xz
     -rw-rw-r-- 1 pythonuser  pythonuser  904535554 Sep  9 08:43 cudnn-local-repo-ubuntu2204-
     -rw-r--r-- 1 pythonuser  pythonuser  904532772 Sep  9 08:44 data.tar.xz

    Verify that a new data.tar.xz is available

  11. Extract files from the data.tar.xz archive

     $ tar -xf data.tar.xz

    When the extraction is complete, the etc, usr, and var subdirectories are added to the directory.

  12. Switch to the cudnn-local-repo-ubuntu2204- sub-directory within the var directory

     $ cd var/cudnn-local-repo-ubuntu2204-
  13. Within the directory, extract files from the libcudnn8-samples_8.9.4.25-1+cuda12.2_amd64.deb file

     $ ar x libcudnn8-samples_8.9.4.25-1+cuda12.2_amd64.deb
  14. Extract files from the new data.tar.xz archive file

     $ tar -xf data.tar.xz 
  15. When the extraction is complete, switch to the new usr directory that contains source code, and sample program files

     $ cd usr/src/cudnn_samples_v8/
  16. Switch to the mnistCUDNN program directory

     $ cd mnistCUDNN
  17. Clean any previous build artifacts

     $ make clean
  18. Compile the MNIST program

     $ make

    When successful, your output should look like the one below:

     CUDA_VERSION is 12000
     Linking agains cublasLt = true
     TARGET ARCH: x86_64
     /usr/local/cuda/bin/nvcc -I/usr/local/cuda/include -I/usr/local/cuda/include 
     -IFreeImage/include -ccbin g++ -m64 -gencode 
     arch=compute_50,code=sm_50 -gencode 
     arch=compute_90,code=compute_90 -o fp16_dev.o -c
     g++ -I/usr/local/cuda/include -I/usr/local/cuda/include -IFreeImage/include   -o mnistCUDNN.o -c mnistCUDNN.cpp
     /usr/local/cuda/bin/nvcc -ccbin g++ -m64 -gencode arch=compute_50,code=sm_50 -gencode 
     arch=compute_90,code=compute_90 -o mnistCUDNN fp16_dev.o fp16_emu.o mnistCUDNN.o -I/usr/local/cuda/include -I/usr/local/cuda/include -IFreeImage/include -L/usr/local/cuda/lib64 -L/usr/local/cuda/lib64 -L/usr/local/cuda/lib64 -lcublasLt -LFreeImage/lib/linux/x86_64 -LFreeImage/lib/linux -lcudart -lcublas -lcudnn -lfreeimage -lstdc++ -lm

    If the command returns compilation errors, run the mnistCUDNN instead

     $ ./mnistCUDNN

    When successful, your output should look like the one below:

     Executing: mnistCUDNN
     Testing single precision
     Loading binary file data/conv1.bin
     Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
     ^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory
     Testing cudnnFindConvolutionForwardAlgorithm ...
     ^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.045248 time requiring 0 memory
     ^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 5.640960 time requiring 184784 memory
     Test passed!

When the test is successful, cuDNN is active and installed on your server

Verify the System Compatibility with cuDNN Versions

To successfully install and use cuDNN on your server, verify that the following necessary drivers and minimum required versions are available on the system:

  • Kernel version
  • GCC version
  • NVIDIA driver version

Verify the installed drivers and versions on your system to use cuDNN as described in the following sections.

Verify the Installed NVIDIA Drivers

NVIDIA GPU Drivers are essential for the system to access and use the GPU. On Vultr Cloud GPU servers, the drivers are pre-installed during system initialization. When installing NVIDIA cuDNN, verify if the NVIDIA GPU drivers are correctly installed using the following command

$ nvidia-smi

The NVIDIA System Management Interface (NVIDIA SMI) utility displays information about the GPU like the one below:

| NVIDIA-SMI 525.125.06   Driver Version: 525.125.06   CUDA Version: 12.0     |
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|   0  NVIDIA A40-1Q       On   | 00000000:06:00.0 Off |                    0 |
| N/A   N/A    P8    N/A /  N/A |      0MiB /  1024MiB |      0%      Default |
|                               |                      |             Disabled |

In the above output, 525.125.06 is the NVIDIA accelerated graphics driver version. You can install a cuDNN version when the minimum required NVIDIA driver version is lower than the available variant on your system

Verify the Installed CUDA Toolkit Version

Applications that use cuDNN require the CUDA Toolkit to work correctly. Verify if the CUDA compiler is available on your server using the following command

$ nvcc --version

Your output should look like the one below:

nvcc: NVIDIA (R) Cuda compiler driver 
Copyright (c) 2005-2023 NVIDIA Corporation 
Built on Tue_Jul_11_02:20:44_PDT_2023 
Cuda compilation tools, release 12.0, V12.0.140
Build cuda_12.0.r12.0/compiler.32267302_0

As displayed in the above output, the CUDA 12.x. version is available on the system

Verify the Available Kernel Version

cuDNN requires a recent kernel version to work on your system. The kernel version must be more recent than the minimum version required by the cuDNN version you intend to install. Run the following command to view the available kernel version on your system

$ uname -r



The above output displays the installed kernel version 5.15.0-75

Verify the GCC Version

The GNU Compiler Collection (GCC) is necessary when compiling application programs. To use cuDNN, your system must have a GCC version higher than the required cuDNN version.

Verify the installed GCC version

$ gcc --version

Your output should look like the one below:

gcc (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0 
Copyright (C) 2021 Free Software Foundation, Inc. 
This is free software; see the source for copying conditions.  

As displayed in the above output, the GCC version 11.3.0 is available on the server


In this guide, you installed the NVIDIA cuDNN package on a Ubuntu 22.04 server using two options, native and Conda. You also verified the system configuration to install the matching cuDNN version. For more information about cuDNN, visit the official documentation. To develop applications using cuDNN, visit the cuDNN API Reference that discusses the available cuDNN functions such as routines for training and inference using neural networks.