5. Managing GPUs

CloudVeneto provides Unipd Physics Dept. and INFN Padova users with some GPUs (Graphics Processing Units). These are:

  • 4 GPU Nvidia Tesla T4
  • 1 GPU Nvidia Quadro RTX 6000
  • 2 GPU Nvidia TITAN Xp
  • 1 GPU Nvidia GeForce GTX TITAN

Using a CloudVeneto GPU means accessing a virtual machine which has full access and direct control of such GPU device.

5.1. Creating a GPU instance

GPU instances, i.e. virtual machines which have access to one or more GPUs can be created only from the HPC-Physics project.

So, first of all, you need to request the affiliation to such project (see Apply for other projects for the relevant instructions).

The instructions to then create a GPU instance are the very same for the creation of a ‘standard’ virtual machine (see Creating Virtual Machines). You will only have to pay attention to use one of these special flavors:

  • cloudveneto.15cores90GB20GB1T4

    Flavor for an instance with 1 GPU Nvidia T4, 15 VCPUs, 90 GB of RAM, 20 GB of ephemeral disk space.

  • cloudveneto.15cores90GB500GB1T4

    Flavor for an instance with 1 GPU Nvidia T4, 15 VCPUs, 90 GB of RAM, 500 GB of ephemeral disk space.

  • cloudveneto.30cores180GB500GB2T4

    Flavor for an instance with 2 GPUs Nvidia T4, 30 VCPUs, 180 GB of RAM, 500 GB of ephemeral disk space.

  • cloudveneto.30cores180GB1000GB2T4

    Flavor for an instance with 2 GPUs Nvidia T4, 30 VCPUs, 180 GB of RAM, 1000 GB of ephemeral disk space.

  • cloudveneto.60cores360GB500GB4T4

    Flavor for an instance with 4 GPUs Nvidia T4, 60 VCPUs, 360 GB of RAM, 500 GB of ephemeral disk space.

  • cloudveneto.8cores40GB500GB1Quadro

    Flavor for an instance with 1 GPU Nvidia Quadro RTX 6000, 8 VCPUs, 40 GB of RAM, 500 GB of ephemeral disk space.

  • cloudveneto.8cores40GB500GB1TitanXP

    Flavor for an instance with 1 GPU Nvidia Titan Xp, 8 VCPUs, 40 GB of RAM, 500 GB of ephemeral disk space.

  • cloudveneto.16cores80GB500GB2TitanXP

    Flavor for an instance with 2 GPUs Nvidia Titan Xp, 16 VCPUs, 80 GB of RAM, 500 GB of ephemeral disk space.

  • cloudveneto.4cores20GB150GB1GeforceGtx

    Flavor for an instance with 1 GPU Nvidia GeForce GTX TITAN, 4 VCPUs, 20 GB of RAM, 150 GB of ephemeral disk space.

Warning

“Generic” flavors have a limited size for disk space. Flavors for GPUs have a bigger size to take advantage of the fast (SSD/NVME) disks installed on the servers hosting the GPUs. However, please remember that size limit for images and snapshots is 25 GB. This means that it is not possible to snapshot an instance created using a flavor with a big disk.

Note

Before allocation one or more GPUs, please register such allocation in this document. Please be sure to fill also the ‘estimated end date’ field.

5.1.1. Images for GPU instances

You are responsible to create the image to be used (see User Provided Images and Building Images).

These instructions explain how to install CUDA toolkit and the relevant drivers.

Note

For better performance, we suggest to create images:

  • in raw format

  • setting the properties hw_disk_bus=scsi and hw_scsi_model=virtio-scsi, i.e., using the command line tool:

    # glance image-update --property hw_disk_bus=scsi <image-id>
    # glance image-update --property hw_scsi_model=virtio-scsi <image-id>
    

Just for reference, we provide a CentOS7.x image (GPU-CentOS7-INFNPadova-x86_64-<date>) which has the same content of the CentOS7x-INFNPadova-x86-64-<date> public images, and in addition provides the CUDA toolkit and the needed drivers. This image was tested with Nvidia T4 GPUs.

Warning

On a VM instantiated using this image, cuda is installed at the first boot (and its installation can take several minutes). You may understand if the installation has been done if the following command:

# rpm -q cuda

returns something like.

cuda-10.2.89-1.x86_64

A further reboot might then be needed.

5.2. Monitoring

Unfortunately it is not straightforward to see which GPUs are being used and which ones are available using the CloudVeneto Openstack dashboard.

You can refer to this page for such information (please note that this page is updated every 30 minutes).

5.3. Policies

Please consider the following policies when using GPU instances:

  • Since there is a high request to use GPUs, please delete your instance as soon as you don’t need it anymore. This is because virtual machines, even if idle or in shutdown state, allocate resources (GPUs in particular) which therefore aren’t available to other users.
  • Once activated, your virtual instance is managed by you.
  • Before allocation one or more GPUs, please register such allocation in this document. Please be sure to fill also the ‘estimated end date’ field. Instances for which there isn’t a reservation in this document can be deleted by the Cloud administrators.
  • Please don’t reserve the GPU(s) for long (i.e > 1 week) periods.