Learn the concept behind AI Training notebooks

Last updated 27th May, 2021.


A notebook in AI Training is used to easily work with one of the well-known machine learning frameworks on either JupyterLab or VSCode and powerful hardware.

The advantage compared to doing your own setup is that everything is already installed for you, and that you pay only for your notebooks while they are running.

Each notebook is linked to a Public Cloud project and specifies hardware resources along with a machine learning framework and an editor among those available.

The easiest way to create and manage notebooks currently is to use the ovhai CLI. You can start learning to use it by reading the Getting Started page.

Notebooks also provide an easy way to access data from your Object Storage, you can read more about it here.


  • A notebook will run indefinitely until manual interruption.
  • If you do not customise your resource request, the default requested is 1 GPU. Memory is not customisable.
  • Billing for notebooks is minute-based. Each commenced minute is billed completely.

Notebook lifecycle

During its lifetime the notebook will transition between the following statuses:

  • Billing starts once a notebook is RUNNING and ends when its status switches to STOPPING.
  • Only notebooks in states STARTING and RUNNING are included in the resource quota computation.
  • STARTING: the notebook is starting, and volumes are synchronised from the Object Storage
  • RUNNING: the notebook is running and can be accessed from your browser
  • STOPPING: the notebook is still running, but an interruption order was received and RW volumes are uploaded to your Object Storage
  • STOPPED: the notebook is stopped and RW volumes have been synchronised back to your Object Storage


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