AI Training - Capabilities and Limitations

Find out about current capabilities and limitations of AI Training

Last updated 20th April, 2021.

Capabilities and known limits

Jobs resources and limitations

  • No duration limitation on job execution
  • No limitation on number of parallel execution

You can either choose the number of GPU or CPU for a job, not both. By default, a job uses one GPU. The memory resource is not customisable.

If you chose the number of GPU:

  • CPU, memory and local storage resources are not customisable but scaled linearly with each additional GPU.

If you chose the number of CPU:

  • Memory and local storage resource is not customisable but scaled linearly with each additional CPU.

Information about maximum number of CPU/GPU, memory per CPU/GPU and local storage are available with the ovhai CLI.

ovhai capabilities flavor list

Local storage refers to a drive mounted on the node. It's limited and not the recommended way to handle data, see OVHcloud documentation on data

Available ports to public network

Each job has a public URL, by default this URL accesses the port 8080 of the job. The default port can be configured when you submit a new job.

You can also access other ports by appending them to the URL.

Job URL for accessing the default port (starting with the job's ID):


Job URL for accessing the port 9000 (starting with the job's ID followed by the port number):


Only the HTTP layer is accessible.

OVHcloud vRack

  • AI Training cannot use or access OVHcloud vRack.

Available hardware for jobs

Available regions for jobs

  • GRA (Gravelines in France)
  • BHS (Beauharnois in Canada)

Available storage for data volumes

  • Public Cloud Object Storage (Any Region)


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