CuDNN Cu DNN Installation Guide

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CUDNN

DU-08670-001_v07 | September 2017

Installation Guide

TABLE OF CONTENTS
Chapter 1. Overview............................................................................................ 1
Chapter 2. Installing cuDNN on Linux....................................................................... 2
2.1. Prerequisites............................................................................................... 2
2.1.1. Installing NVIDIA Graphics Drivers................................................................ 2
2.1.2. Installing CUDA...................................................................................... 3
2.2. Downloading cuDNN...................................................................................... 3
2.3. Installing cuDNN on Linux............................................................................... 3
2.3.1. Installing from a Tar File.......................................................................... 3
2.3.2. Installing from a Debian File...................................................................... 3
2.4. Verifying.................................................................................................... 4
2.5. Upgrading from v6 to v7................................................................................ 4
2.6. Troubleshooting........................................................................................... 4
Chapter 3. Installing cuDNN on Mac OS X...................................................................5
3.1. Prerequisites............................................................................................... 5
3.1.1. Installing NVIDIA Graphics Drivers................................................................ 5
3.1.2. Installing CUDA...................................................................................... 5
3.2. Downloading cuDNN...................................................................................... 5
3.3. Installing cuDNN on Mac OS X.......................................................................... 6
3.4. Verifying.................................................................................................... 6
3.5. Upgrading from v6 to v7................................................................................ 7
3.6. Troubleshooting........................................................................................... 7
Chapter 4. Installing cuDNN on Windows................................................................... 8
4.1. Prerequisites............................................................................................... 8
4.1.1. Installing NVIDIA Graphics Drivers................................................................ 8
4.1.2. Installing CUDA...................................................................................... 8
4.2. Downloading cuDNN...................................................................................... 9
4.3. Installing cuDNN on Windows........................................................................... 9
4.4. Upgrading from v6 to v7............................................................................... 10
4.5. Troubleshooting.......................................................................................... 10

www.nvidia.com
cuDNN

DU-08670-001_v07 | ii

Chapter 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.

www.nvidia.com
cuDNN

DU-08670-001_v07 | 1

Chapter 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.
If you are using cuDNN with a Volta GPU, version 7 or later is required.
One of the following supported platforms:

‣

‣ Ubuntu 14.04
‣ Ubuntu 16.04
‣ POWER8
One of the following supported CUDA versions and NVIDIA graphics driver:
‣
‣

NVIDIA graphics driver 375.88 or newer for CUDA 8
NVIDIA graphics driver 384.81 or newer for CUDA 9

For more information, see
‣
‣

Installing NVIDIA Graphics Drivers
Installing CUDA

2.1.1. Installing NVIDIA Graphics Drivers
Install up-to-date NVIDIA graphics drivers on your Linux system.
Go to: NVIDIA download drivers
Select the GPU and OS version from the drop down menus.
3. Download and install NVIDIA graphics driver 384.81 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.
1.
2.

www.nvidia.com
cuDNN

DU-08670-001_v07 | 2

Installing cuDNN on Linux

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.
Go to: NVIDIA cuDNN home page.
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.
1.
2.

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. 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

2.3.1. Installing from a Tar File
Navigate to your directory containing the cuDNN Tar file.
2. Unzip the cuDNN package.
1.

$ tar -xzvf cudnn-9.0-linux-x64-v7.tgz

Copy the following files into the CUDA Toolkit directory.
$ 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

www.nvidia.com
cuDNN

DU-08670-001_v07 | 3

Installing cuDNN on Linux

1.
2.

Navigate to your directory containing cuDNN Debian file.
Install the runtime library, for example:
sudo dpkg -i libcudnn7_7.0.3.11-1+cuda9.0_amd64.deb

Install the developer library, for example:

sudo dpkg -i libcudnn7-dev_7.0.3.11-1+cuda9.0_amd64.deb

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

Go to the writable path.
$ cd

$HOME/cudnn_samples_v7/mnistCUDNN

Compile the mnistCUDNN sample.
$make clean &&& make

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.

www.nvidia.com
cuDNN

DU-08670-001_v07 | 4

Chapter 3.
INSTALLING CUDNN ON MAC OS X

3.1. 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.
Mac OS X 10.11 or later
NVIDIA graphics driver 378.05.05.25f01 or newer. For more information, see
Installing NVIDIA Graphics Drivers.
CUDA 9.0 RC. For more information, see Installing CUDA.

3.1.1. Installing NVIDIA Graphics Drivers
Install up-to-date NVIDIA graphics drivers on your Mac OS X system.
Go to: NVIDIA download drivers
2. Select the GPU and OS version from the drop down menus.
3. Download and install NVIDIA graphics driver 378.05 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.
1.

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

www.nvidia.com
cuDNN

DU-08670-001_v07 | 5

Installing cuDNN on Mac OS X

In order to download cuDNN, ensure you are registered for the NVIDIA Developer
Program.
1.
2.
3.
4.
5.
6.

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

3.3. Installing cuDNN on Mac OS X
The following steps describe how to build a cuDNN dependent program. In the
following sections:
‣
‣

your CUDA directory path is referred to as /usr/local/cuda/
your cuDNN directory path is referred to as

Navigate to your directory containing cuDNN.
2. Unzip the cuDNN package.
1.

$ tar -xzvf cudnn-9.0-osx-x64-v7.tgz

Copy the following files into the CUDA Toolkit directory.
$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include
$ sudo cp cuda/lib/libcudnn* /usr/local/cuda/lib
$ sudo chmod a+r /usr/local/cuda/include/cudnn.h
/usr/local/cuda/lib/libcudnn*

Set the following environment variables to point to where cuDNN is located.
$ export

DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH

3.4. Verifying
To verify that cuDNN is working properly on your Mac OS X system, perform the
following step.
Run the following command.
$ echo -e '#include"cudnn.h"\n void main(){}' | nvcc -x c - -o /dev/null -I/
usr/local/cuda/include -L/usr/local/cuda/lib -lcudnn

If no error occurs, both the header and library are installed and can be located by the
nvcc compiler.

www.nvidia.com
cuDNN

DU-08670-001_v07 | 6

Installing cuDNN on Mac OS X

3.5. Upgrading from v6 to v7
cuDNN v7 can coexist with previous versions of cuDNN, such as v5 or v6.

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

www.nvidia.com
cuDNN

DU-08670-001_v07 | 7

Chapter 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.
One of the following supported platforms:

‣

‣ Windows 7
‣ Windows 10
One of the following supported CUDA versions and NVIDIA graphics driver:
‣
‣

NVIDIA graphics driver 377.55 or newer for CUDA 8
NVIDIA graphics driver 385.54 or newer for CUDA 9

For more information, see
‣
‣

Installing NVIDIA Graphics Drivers
Installing CUDA

4.1.1. Installing NVIDIA Graphics Drivers
Install up-to-date NVIDIA graphics drivers on your Windows system.
Go to: NVIDIA download drivers
2. Select the GPU and OS version from the drop down menus.
3. Download and install NVIDIA graphics driver 385.08 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.
1.

4.1.2. Installing CUDA

www.nvidia.com
cuDNN

DU-08670-001_v07 | 8

Installing cuDNN on Windows

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.
2.
3.
4.
5.
6.

4.3. Installing cuDNN on Windows
The following steps describe how to build a cuDNN dependent program. In the
following sections:
‣
‣
1.
2.

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
Navigate to your directory containing cuDNN.
Unzip the cuDNN package.
cudnn-9.0-windows7-x64-v7.zip

or
cudnn-9.0-windows10-x64-v7.zip

Copy the following files into the CUDA Toolkit directory.
a) Copy \cuda\bin\cudnn64_7.dll to C:\Program Files
\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin.
b) Copy \cuda\ include\cudnn.h to C:\Program Files
\NVIDIA GPU Computing Toolkit\CUDA\v9.0\include.
c) Copy \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:
a) Open a command prompt from the Start menu.
3.

www.nvidia.com
cuDNN

DU-08670-001_v07 | 9

Installing cuDNN on Windows

b)
c)
d)
e)
f)

Type Run and hit Enter.
Issue the control sysdm.cpl command.
Select the Advanced tab at the top of the window.
Click Environment Variables at the bottom of the window.
Ensure the following values are set:
Variable Name: CUDA_PATH
Variable Value: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0

Include cudnn.lib in your Visual Studio project.
a) Open the Visual Studio project and right-click on the project name.
b) Click Linker > Input > Additional Dependencies.
c) 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.

www.nvidia.com
cuDNN

DU-08670-001_v07 | 10

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THE INFORMATION IN THIS GUIDE AND ALL OTHER INFORMATION CONTAINED IN NVIDIA DOCUMENTATION
REFERENCED IN THIS GUIDE IS PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED,
STATUTORY, OR OTHERWISE WITH RESPECT TO THE INFORMATION FOR THE PRODUCT, AND EXPRESSLY
DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A
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NVIDIA’s aggregate and cumulative liability towards customer for the product described in this guide shall
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THE NVIDIA PRODUCT DESCRIBED IN THIS GUIDE IS NOT FAULT TOLERANT AND IS NOT DESIGNED,
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IN PART, FOR ANY CLAIMS OR DAMAGES ARISING FROM SUCH HIGH RISK USES.
NVIDIA makes no representation or warranty that the product described in this guide will be suitable for
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Trademarks
NVIDIA, the NVIDIA logo, and cuBLAS, CUDA, cuDNN, cuFFT, cuSPARSE, DIGITS, DGX, DGX-1, Jetson, Kepler,
NVIDIA Maxwell, NCCL, NVLink, Pascal, Tegra, TensorRT, and Tesla are trademarks and/or registered
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