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Deploying a GPU instance

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Find out how to deploy a GPU instance on Linux or Windows

Objective

GPU instances are technically similar to the instances from the older range, but they also have a graphics card (Graphic Processing Unit or GPU). The technology used (pci_passthrough) allows the instance’s operating system to control the GPU in exactly the same way a physical machine would.

Warning

At the moment, most of our old GPU instances (Tesla V100 and V100s) are only available in the GRA7, GRA9, GRA11, and BHS5 regions. The newer models (A100, H100, L4 and L40s) are only available in the GRA11 region for now.

This guide explains how to deploy a GPU instance on Linux or Windows

Requirements

  • A Public Cloud project with access to the regions where most GPUs are available (GRA7, GRA9, GRA11 and BHS5)
  • An SSH key created to deploy a linux GPU instance.

OVHcloud Console Access

  • Direct link:
  • Navigation path: Public Cloud > Select your project > Compute > Instances

Instructions

You will find the information needed to deploy a GPU instance on Linux or Windows below.

In the left-hand menu of your Public Cloud project, click Instances under Compute, then click Create instance. In the Select a model section, select Cloud GPU in the Instance model list, then choose a compatible GPU instance model, to benefit from resources suited to graphics or intensive computing workloads.

Next, follow the remaining steps as detailed in this guide. This process may take a few minutes.

On Linux
On Windows

All the images we offer can be used on a GPU instance.

During the image selection step, open the Unix distributions tab of the Select an image section, then choose a UNIX image that suits your needs.

Info

If you don’t feel comfortable with manually compiling a kernel module, we recommend using a distribution that is officially supported by Nvidia and for which they provide turnkey drivers: https://developer.nvidia.com/cuda-downloads.

Once the instance is delivered, you can then log in and check for the graphics card:

lspci | grep -i nvidia
00:05.0 VGA compatible controller: NVIDIA Corporation Device 1c03 (rev a1)
00:06.0 Audio device: NVIDIA Corporation Device 10f1 (rev a1)

The graphics card is there, but cannot be used yet. To do so, you must first install the NVIDIA driver. You can find the list of packages at this address: List of available Linux packages.

You will then need to enter the following commands:

wget URL_of_packet_to_download
sudo dpkg -i cuda-repo-XXXX-XXXXXX
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install cuda
sudo apt-get install -y cuda-drivers
sudo apt-get install linux-headers-$(uname -r)
sudo reboot
Info

The Linux command can vary based on your distribution. If in doubt, please check the official guide for your version of Linux.

Once the instance has been rebooted, the graphics card will appear in the NVIDIA utility program:

nvidia-smi
Wed Apr 26 13:05:25 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.51                 Driver Version: 375.51                    |
|-------------------------------+----------------------+----------------------+
| 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  | 0000:00:05.0     Off |                  N/A |
|  0%   22C    P0    26W / 120W |      0MiB /  6072MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

The GPU instance is now fully functional and usable.

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