Submit a job via UI
Learn how to submit an AI Training job via UI
Learn how to submit an AI Training job via UI
Last updated 18th May, 2021.
This guide covers the initialisation of AI Training and the submission of jobs through the OVHcloud Control Panel.
Log in to the OVHcloud Control Panel, go to the
Public Cloud section and then to the
AI Training section which is located under
AI & Machine Learning.
Once you have read the general information and validated this services's contract terms, you can start submitting your jobs. Upon activating the AI Training service you grant OVHcloud access to your Object Storage containers. This access is only used to synchronise your data within AI Training with your containers.
Once AI Training is activated you land on the dashboard service with several components.
You don't need a user to launch a new job from the OVHcloud manager but you will need one later if you want to use the CLI or access your jobs urls. Instructions for creating new users are described here.
From the jobs list in the dashboard you can start the job submission by clicking the
Launch a new Job button.
Each job is executed in an OVHcloud region. Each region has its own AI Training cluster with potentially varying capabilities. For more information see the capabilities.
Select the desired region and click
A job is basically a Docker container that is run within the OVHcloud infrastructure. You need to provide a Docker image to be executed. There are several options you can choose from:
OVHcloud provides a set of images from which you can choose to ease the submission of your first jobs. Provided images are essentially a JupyterLab environment bundled with some Deep Learning technology such as Tensorflow or MXNet.
Preset images cannot cover all your needs so you can specify your own image if necessary. You can use any image that is accessible from AI Training.
This includes public images (e.g. Dockerhub), images within the shared registry or images in your added private registry. For more information, see how to add a private registry.
Once your image is chosen, click
You can attach data objects to your job either as input for your training workload or as output for your results (e.g. model weights).
Before attaching a data object you need to create one. A data object cannot be attached to a running job.
To attach a data object, just select from the list on the left. Next to each data, within the parenthesis, you can check the mount path in the Docker container for the submitted job. If you wish to customise this mount path, you will need to use the
ovhai CLI, its installation procedure is available here.
To attach a data object you must click on the plus (+) button after filling the fields
Once the data is configured click
The Docker image you provided in Step 5 includes an entrypoint for your container. You can override this entrypoint by specifying your own command. Once the entrypoint is set up click
In this step you can either select the amount of GPUs or CPUs you need for your training workload.
The max amount of GPUs or CPUs you can select for your job is region dependent. If you choose a GPU a fixed ratio of CPU is applied based on the number of GPUs. Similarly, there is a fixed ratio of Memory based on the number of CPUs. For more information see the capabilities.
Once the amount of resources is set you can see a preview of the billing rate. Click
In the final step you get an overview of the job you configured before submission. You also get the equivalent command to use with the
The AI Training service is mainly supposed to be used through the
ovhai CLI. The OVHcloud Control Panel only offers a subset of the features and is meant to help you get started before using the CLI.
Submit to submit your job to the cluster.
A job will run indefinitely until completion or manual interruption.
Once the job is submitted you are redirected to the jobs list page.
From this list you can access your job details either by clicking on its
ID or by clicking on
... and selecting
Details. The details include several components:
8080. The URL is of the form
https://<JOB-ID>.job.<REGION>.training.ai.cloud.ovh.net/. If the service is not exposed on the port
8080it is still accessible by specifying the port in the URL this way:
https://<JOB-ID>-<PORT>.job.<REGION>.training.ai.cloud.ovh.net/. You can check the list of available ports in the capabilities.
If you are done using your job, if your model converged prematurely or if you just wish to interrupt your job you can do so from the jobs list.
From the list of jobs you can list the available actions at the far right of each entry and interrupt the job by clicking
Stop. Alternatively, from the job details you can also interrupt the job from the list of actions.
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