Abstract

This guide provides step-by-step instructions on how to install and check for correct operation of NVIDIA cuDNN v7.6.1 on Linux, Mac OS X, and Microsoft Windows systems.

For previously released cuDNN installation documentation, see cuDNN Archives.

1. Overview

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.

Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning. cuDNN accelerates widely used deep learning frameworks, including Caffe, Caffe2, TensorFlow, Theano, Torch, PyTorch, MXNet, and Microsoft Cognitive Toolkit. cuDNN is freely available to members of the NVIDIA Developer Program.

2. Installing cuDNN on Linux

2.1. Prerequisites

Ensure you meet the following requirements before you install cuDNN.
  • A GPU of compute capability 3.0 or higher. To understand the compute capability of the GPU on your system, see: CUDA GPUs. Also see the cuDNN Support Matrix.
  • If you are using cuDNN with a Volta GPU, version 7 or later is required.
  • One of the following supported Architecture - OS combinations:
    • On x86_64 (for installing cuDNN with Debian files)
      • Ubuntu 18.04 or
      • Ubuntu 16.04 or
      • Ubuntu 14.04
    • On x86_64 (for installing tgz files)
      • Any Linux distribution
    • On POWER 9 and POWER 8
      • RHEL 7.5 or later, and Ubuntu 18.04
  • One of the following supported CUDA versions and NVIDIA graphics driver:
    • NVIDIA graphics driver R418 or newer for CUDA 10.1
    • NVIDIA graphics driver R410 or newer for CUDA 10.0
    • NVIDIA graphics driver R396 or newer for CUDA 9.2
    • NVIDIA graphics driver R384 or newer for CUDA 9
    For more information, see

2.1.1. Installing NVIDIA Graphics Drivers

Install up-to-date NVIDIA graphics drivers on your Linux system.

  1. Go to: NVIDIA download drivers
  2. Select the GPU and OS version from the drop down menus.
  3. Download and install NVIDIA graphics driver as indicated in that webpage. For more information, select the ADDITIONAL INFORMATION tab for step-by-step instructions for installing a driver.
  4. Restart your system to ensure the graphics driver takes effect.

2.1.2. Installing CUDA

Refer to the following instructions for installing CUDA on Linux, including the CUDA driver and toolkit: NVIDIA CUDA Installation Guide for Linux.

2.2. Downloading cuDNN

In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program.

  1. Go to: NVIDIA cuDNN home page.
  2. Click Download.
  3. Complete the short survey and click Submit.
  4. Accept the Terms and Conditions. A list of available download versions of cuDNN displays.
  5. Select the cuDNN version you want to install. A list of available resources displays.

2.3. Installing cuDNN on Linux

The following steps describe how to build a cuDNN dependent program. Choose the installation method that meets your environment needs. For example, the tar file installation applies to all Linux platforms, and the debian installation package applies to Ubuntu 14.04 and 16.04.

In the following sections:
  • your CUDA directory path is referred to as /usr/local/cuda/
  • your cuDNN download path is referred to as <cudnnpath>

2.3.1. Installing from a Tar File

  1. Navigate to your <cudnnpath> directory containing the cuDNN Tar file.
  2. Unzip the cuDNN package. 
    $ tar -xzvf cudnn-9.0-linux-x64-v7.tgz
  3. Copy the following files into the CUDA Toolkit directory, and change the file permissions. 
    $ sudo cp cuda/include/cudnn.h /usr/local/cuda/include
$ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
$ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

2.3.2. Installing from a Debian File

  1. Navigate to your <cudnnpath> directory containing cuDNN Debian file.
  2. Install the runtime library, for example:
    sudo dpkg -i libcudnn7_7.0.3.11-1+cuda9.0_amd64.deb
  3. Install the developer library, for example:
    sudo dpkg -i libcudnn7-devel_7.0.3.11-1+cuda9.0_amd64.deb
  4. Install the code samples and the cuDNN Library User Guide, for example:
    sudo dpkg -i libcudnn7-doc_7.0.3.11-1+cuda9.0_amd64.deb

2.4. Verifying

To verify that cuDNN is installed and is running properly, compile the mnistCUDNN sample located in the /usr/src/cudnn_samples_v7 directory in the debian file.

  1. Copy the cuDNN sample to a writable path.
    $cp -r /usr/src/cudnn_samples_v7/ $HOME
  2. Go to the writable path.
    $ cd  $HOME/cudnn_samples_v7/mnistCUDNN
  3. Compile the mnistCUDNN sample.
    $make clean && make
  4. Run the mnistCUDNN sample.
    $ ./mnistCUDNN
    If cuDNN is properly installed and running on your Linux system, you will see a message similar to the following:
    Test passed!

2.5. Upgrading from v6 to v7

cuDNN v7 can coexist with previous versions of cuDNN, such as v5 or v6.

2.6. Troubleshooting

Join the NVIDIA Developer Forum to post questions and follow discussions.

3. Installing cuDNN on Mac OS X

3.1. Prerequisites

Ensure you meet the following requirements before you install cuDNN.

3.1.1. Installing NVIDIA Graphics Drivers

Install up-to-date NVIDIA graphics drivers on your Mac OS X system.

  1. Go to: NVIDIA download drivers
  2. Select the GPU and OS version from the drop down menus.
  3. Download and install NVIDIA graphics driver 396 or newer. For more information, select the ADDITIONAL INFORMATION tab for step-by-step instructions for installing a driver.
  4. Restart your system to ensure the graphics driver takes effect.

3.1.2. Installing CUDA

Refer to the following instructions for installing CUDA on Mac OS X, including the CUDA driver and toolkit: NVIDIA CUDA Installation Guide for Mac OS X.

3.2. Downloading cuDNN

In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program.

  1. Go to: NVIDIA cuDNN home page.
  2. Click Download.
  3. Complete the short survey and click Submit.
  4. Accept the Terms and Conditions. A list of available download versions of cuDNN displays.
  5. Select the cuDNN version to want to install. A list of available resources displays.
  6. Extract the cuDNN archive to a directory of your choice.

4. Installing cuDNN on Windows

4.1. Prerequisites

Ensure you meet the following requirements before you install cuDNN.
  • A GPU of compute capability 3.0 or higher. To understand the compute capability of the GPU on your system, see: CUDA GPUs. Also see the cuDNN Support Matrix.
  • One of the following supported platforms:
    • Windows 10
    • Windows 7
    • Windows Server 2016 or Windows Server 2012
  • One of the following supported CUDA versions and NVIDIA graphics driver:
    • NVIDIA graphics driver R418 or newer for CUDA 10.1
    • NVIDIA graphics driver R410 or newer for CUDA 10.0
    • NVIDIA graphics driver R396 or newer for CUDA 9.2
    • NVIDIA graphics driver R384 or newer for CUDA 9
    For more information, see

4.1.1. Installing NVIDIA Graphics Drivers

Install up-to-date NVIDIA graphics drivers on your Windows system.

  1. Go to: NVIDIA download drivers
  2. Select the GPU and OS version from the drop down menus.
  3. Download and install NVIDIA driver as indicated in that webpage. For more information, select the ADDITIONAL INFORMATION tab for step-by-step instructions for installing a driver.
  4. Restart your system to ensure the graphics driver takes effect.

4.1.2. Installing CUDA

Refer to the following instructions for installing CUDA on Windows, including the CUDA driver and toolkit: NVIDIA CUDA Installation Guide for Windows.

4.2. Downloading cuDNN

In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program.

  1. Go to: NVIDIA cuDNN home page.
  2. Click Download.
  3. Complete the short survey and click Submit.
  4. Accept the Terms and Conditions. A list of available download versions of cuDNN displays.
  5. Select the cuDNN version to want to install. A list of available resources displays.
  6. Extract the cuDNN archive to a directory of your choice.

4.3. Installing cuDNN on Windows

The following steps describe how to build a cuDNN dependent program. In the following sections the CUDA v9.0 is used as example:
  • Your CUDA directory path is referred to as C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0
  • Your cuDNN directory path is referred to as <installpath>
  1. Navigate to your <installpath> directory containing cuDNN.
  2. Unzip the cuDNN package.
    cudnn-9.0-windows7-x64-v7.zip
    or 
    cudnn-9.0-windows10-x64-v7.zip
  3. Copy the following files into the CUDA Toolkit directory.
    1. Copy <installpath>\cuda\bin\cudnn64_7.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin.
    2. Copy <installpath>\cuda\ include\cudnn.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\include.
    3. Copy <installpath>\cuda\lib\x64\cudnn.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\lib\x64.
  4. Set the following environment variables to point to where cuDNN is located. To access the value of the $(CUDA_PATH) environment variable, perform the following steps:
    1. Open a command prompt from the Start menu.
    2. Type Run and hit Enter.
    3. Issue the control sysdm.cpl command.
    4. Select the Advanced tab at the top of the window.
    5. Click Environment Variables at the bottom of the window.
    6. Ensure the following values are set: 
      Variable Name: CUDA_PATH 
Variable Value: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0
  5. Include cudnn.lib in your Visual Studio project.
    1. Open the Visual Studio project and right-click on the project name.
    2. Click Linker > Input > Additional Dependencies.
    3. Add cudnn.lib and click OK.

4.4. Upgrading from v6 to v7

cuDNN v7 can coexist with previous versions of cuDNN, such as v5 or v6.

4.5. Troubleshooting

Join the NVIDIA Developer Forum to post questions and follow discussions.

5. Cross-compiling cuDNN

This section describes how to cross-compile cuDNN samples.

5.1. NVIDIA DRIVE OS Linux

Follow the below steps to cross-compile cuDNN samples on NVIDIA DRIVE OS Linux:

5.1.1. Install CUDA Toolkit

  1. Download the CUDA for Ubuntu package:cuda*ubuntu*_amd64.deb
  2. Download the cross compile package: cuda*-cross-aarch64*_all.deb

  3. Execute the following commands:

    1. $ sudo dpkg -i cuda*ubuntu*_amd64.deb
    2. $ sudo dpkg -i cuda*-cross-aarch64*_all.deb 
    3. $ sudo apt-get update
    4. $ sudo apt-get install cuda-toolkit-10-1 -y
    5. $ sudo apt-get install cuda-cross-aarch64* -y

5.1.2. Install cuDNN

  1. Download the cuDNN Ubuntu package for your preferred CUDA Toolkit version: *libcudnn7-cross-aarch64_*.deb
  2. Download the cross compile package: libcudnn7-devel-cross-aarch64_*.deb

  3. Execute the following commands:

    1. $ sudo dpkg -i *libcudnn7-cross-aarch64_*.deb

    2. $ sudo dpkg -i libcudnn7-devel-cross-aarch64_*.deb

5.1.3. Cross-compile cuDNN Samples

Copy the cudnn_samples_v7 directory to your home directory: 

$ cp -r /usr/src/cudnn_samples_v7 $HOME

For each sample, execute the following commands:

  1. $ cd $HOME/cudnn_samples_v7/(each sample)
  2. $ make TARGET_ARCH=aarch64

5.2. QNX

Follow the below steps to cross-compile cuDNN samples on QNX:

5.2.1. Install CUDA Toolkit

  1. Download the CUDA for Ubuntu package:cuda*ubuntu*_amd64.deb
  2. Download the cross compile package: cuda*-cross-aarch64*_all.deb

  3. Execute the following commands:

    1. $ sudo dpkg -i cuda*ubuntu*_amd64.deb
    2. $ sudo dpkg -i cuda*-cross-aarch64*_all.deb 
    3. $ sudo apt-get update
    4. $ sudo apt-get install cuda-toolkit-10-1 -y
    5. $ sudo apt-get install cuda-cross-aarch64* -y

5.2.2. Install cuDNN

  1. Download the cuDNN Ubuntu package for your preferred CUDA Toolkit version: *libcudnn7-cross-aarch64_*.deb
  2. Download the cross compile package: libcudnn7-devel-cross-aarch64_*.deb

  3. Execute the following commands:

    1. $ sudo dpkg -i *libcudnn7-cross-aarch64_*.deb

    2. $ sudo dpkg -i libcudnn7-devel-cross-aarch64_*.deb

5.2.3. Set Environment Variables

  1. export CUDA_PATH={PATH}/install/cuda/
  2. export QNX_HOST={PATH}/host/linux/x86_64
  3. export QNX_TARGET={PATH}/target/qnx7

5.2.4. Cross-compile cuDNN Samples

Copy the cudnn_samples_v7 directory to your home directory: 

$ cp -r /usr/src/cudnn_samples_v7 $HOME

For each sample, execute the following commands:

  1. $ cd $HOME/cudnn_samples_v7/(each sample)
  2. $ make TARGET_OS=QNX TARGET_ARCH=aarch64 HOST_COMPILER={SET FULL PATH to YOUR CROSS COMPILER}

    For example: make TARGET_OS=QNX TARGET_ARCH=aarch64 HOST_COMPILER=$QNX_HOST/usr/bin/aarch64-unknown-nto-qnx7.0.0-g++

6. Package Manager Installation

The Package Manager installation interfaces with your system's package manager. When using RPM or Deb, the downloaded package is a repository package, not the actual installation package. This repository package informs the package manager only where to find the actual installation packages, but will not install them.

If the actual installation packages are available in an online repository, they will be automatically downloaded in a later step. Otherwise, the repository package also installs a local repository containing the installation packages on the system.

Whether the repository is available online or installed locally, the installation procedure is identical and made of several steps. See below.

6.1. Local Installation

  1. Download the rpm package libcudnn*.rpm to the local path.

  2. Install the rpm package from the local path. This will install the cuDNN libraries.

    1. rpm -ivh libcudnn7-*.x86_64.rpm
    2. rpm -ivh libcudnn7-devel-*.x86_64.rpm
    3. rpm -ivh libcudnn7-doc-*.x86_64.rpm

6.2. Network Installation

6.2.1. Ubuntu

  1. Download and install the repository: sudo dpkg -ihttp://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb

  2. Execute the steps below to install cuDNN library: 

    1. $ sudo apt-get update && sudo apt-get install libcudnn7-devel
    2. $ sudo apt-get install libcudnn7=[cudnn_version+cuda_version]
    3. $ sudo apt-get install libcudnn7-devel=[cudnn_version+cuda_version]
      For example, for CUDA 9.0 and cuDNN 7.4.1: 
      $ sudo apt-get install libcudnn7=7.4.1.5-1+cuda9.0 sudo apt-get install libcudnn7-devel=7.4.1.5-1+cuda9.0

6.2.2. RHEL

  1. Download and install the repository: rpm -ivhhttp://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm

  2. Install the cuDNN library:

    1. For the latest version: 
      $ sudo yum install libcudnn7
      $ sudo yum install libcudnn7-devel
    2. For other versions:
      $ sudo yum install libcudnn7=[cudnn_version+cuda_version]
      $ sudo yum install libcudnn7-devel=[cudnn_version+cuda_version]
      For example, for CUDA 9.0 and cuDNN 7.4.2: 
      $ sudo yum install libcudnn7=7.4.2.24-1+cuda9.0
      $ sudo yum install libcudnn7-devel=7.4.2.24-1+cuda9.0

Notices

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