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Installing CUDA on a dedicated GPU server

Last updated 14/12/2017

Objective

You can install Compute Unified Device Architecture (CUDA) on a GPU server, but it requires a series of actions which we will explain to you in this guide.

Requirements:

  • You must own a GPU server.
  • You must be logged into your server via SSH

Instructions

Once you have reinstalled the distribution/operating system, follow the instructions below.

Update the kernel

wget http://kernel.ubuntu.com/~kernel-ppa/mainline/v4.10.17/linux-headers-4.10.17-041017_4.10.17-041017.201705201051_all.deb
wget http://kernel.ubuntu.com/~kernel-ppa/mainline/v4.10.17/linux-headers-4.10.17-041017-generic_4.10.17-041017.201705201051_amd64.deb
wget http://kernel.ubuntu.com/~kernel-ppa/mainline/v4.10.17/linux-headers-4.10.17-041017-generic_4.10.17-041017.201705201051_amd64.deb
sudo dpkg -i ./linux-*

Modify the grub.cfg file

Edit the following file, and comment out the OVH kernel part:

/boot/grub/grub.cfg
##BEGIN /etc/grub.d/06_OVHkernel###
#menuentry "GNU/Linux with OVH Kernel, OVH kernel 4.9.58-xxxx-std-ipv6-64" {

#insmod part_gpt

#insmod part_gpt

#insmod diskfilter

# insmod mdraid09

#insmod ext2

#set root='mduuid/cabadcc1a60ca488a4d2adc226fd5302'

#if [ x$feature_platform_search_hint = xy ]; then

#search --no-floppy --fs-uuid --set=root --hint='mduuid/cabadcc1a60ca488a4d2adc226fd5302' b43e4008-da62-482e-a5c8-8384c40b69db

#else

#search --no-floppy --fs-uuid --set=root b43e4008-da62-482e-a5c8-8384c40b69db

#fi

#linux /boot/bzImage-4.9.58-xxxx-std-ipv6-64 root=/dev/md2 ro rootdelay=10 noquiet nosplash net.ifnames=0 biosdevname=0
###END /etc/grub.d/06_OVHkernel###

At this point, you will need to reboot the server. It should reboot on a new kernel.

To check this, you can type the following commands:

uname -a
Linux ns3065593 4.10.17-041017-generic #201705201051 SMP Sat May 20 14:53:33 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux

Then install a library and stop lightdm:

/etc/init.d/lightdm stop
apt-get install ncurses-base
apt-get install nvidia-384-dev
ln -sf /usr/lib/x86_64-linux-gnu/libGL.so.1 /usr/lib/libGL.so.1

Install CUDA

Now, you just need to install CUDA by following the procedure below:

cd /root
mkdir cuda
cd cuda
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb
mv cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb
dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb
apt-key add /var/cuda-repo-9-0-local/7fa2af80.pub
apt-get update
apt-get install cuda
nano /etc/environment
#Once nano is open edit the PATH variable to include /usr/local/cuda-8.0/bin folder. After editing the file, the screen would look like this.
source /etc/environment
export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}
nvcc --version

The nvidia-smi should now function properly:

nvidia-smi
Wed Nov 1 09:14:38 2017
+-----------------------------------------------------------------+
| NVIDIA-SMI 384.90 Driver Version: 384.90 |
+---------------------+---------------------+---------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|=================+=======================+=======================|
| 0 GeForce GTX 106... Off | 00000000:02:00.0 Off | N/A |
| 0% 27C P0 27W / 120W | 0MiB / 6072MiB | 0% Default |
+---------------------+---------------------+---------------------+
| 1 GeForce GTX 106... Off | 00000000:03:00.0 Off | N/A |
| 0% 29C P0 24W / 120W | 0MiB / 6072MiB | 2% Default |
+---------------------+---------------------+---------------------+
+-----------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=================+=======================+=======================|
| No running processes found |
+----------------------------------------------------------------+

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