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GPU Overview

GPU resource requests are handled slightly differently from what was described before in the Projects section. In case you need to request GPUs, the first step is to open a ticket to the GPU Platform Consultancy functional element. The consultants will help you decide which of the services below suits your needs.

OpenStack Project with GPU Flavors

This option is identical to the one described in the Projects section, except that GPU flavors will be assigned to your project. You can then launch instances with GPUs. The available flavors are:

Flavor Name GPU RAM vCPUs Disk Ephemeral Comments
g1.xlarge V100 16 GB 4 56 GB 96 GB -
g1.4xlarge V100 (4x) 64 GB 16 80 GB 528 GB -
g2.xlarge T4 16 GB 4 64 GB 192 GB -
g3.xlarge V100S 16 GB 4 64 GB 192 GB -
g3.4xlarge V100S (4x) 64 GB 16 128 GB 896 GB -
vg1.xlarge T4 (vGPU) 16 GB 4 64 GB 192 GB See below for license configuration

Container Service Clusters

After having GPU resources allocated to you project, you can deploy clusters with GPUs by setting a label (explained here).

Batch Service GPU jobs

The Batch service at CERN already allows the submission of GPU jobs (examples here). Batch not only allows to submit jobs in the typical batch system form, but also using docker, singularity and interactive jobs.

Virtual GPUs

For the vGPUs to operate at full capacity, licensing is required. This can be setup automatically when creating a VM by passing a user data file that we provide (download here). Example vGPU VM creation command:

$ wget https://clouddocs.web.cern.ch/gpu/vgpu-config.sh
$ openstack server create --user-data vgpu-config.sh --flavor vg1.xlarge --image <LINUX_IMAGE> --key-name <KEY_NAME> <VM_NAME>

Virtual GPU VMs can also be created through the OpenStack dashboard by loading the same user data file in the "Configuration" tab.