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Quickstart: Launch Unattended Training Workloads


Deep learning workloads can be divided into two generic types:

  • Interactive "build" sessions. With these types of workloads, the data scientist opens an interactive session, via bash, Jupyter notebook, remote PyCharm, or similar and accesses GPU resources directly.
  • Unattended "training" sessions. With these types of workloads, the data scientist prepares a self-running workload and sends it for execution. During the execution, the customer can examine the results.

With this Quickstart you will learn how to:

  • Use the Run:ai command-line interface (CLI) to start a deep learning training workload.
  • View training status and resource consumption using the Run:ai user interface and the Run:ai CLI.
  • View training logs.
  • Stop the training.


To complete this Quickstart you must have:

Step by Step Walkthrough


  • Login to the Projects area of the Run:ai user interface.
  • Add a Project named "team-a".
  • Allocate 2 GPUs to the Project.

Run Workload

  • At the command-line run:
    runai config project team-a
    runai submit train1 -i -g 1

This would start an unattended training Job for team-a with an allocation of a single GPU. The Job is based on a sample docker image We named the Job train1

  • Follow up on the Job's progress by running:
    runai list jobs

The result:


Typical statuses you may see:

  • ContainerCreating - The docker container is being downloaded from the cloud repository
  • Pending - the Job is waiting to be scheduled
  • Running - the Job is running
  • Succeeded - the Job has ended

A full list of Job statuses can be found here

To get additional status on your Job run:

runai describe job train1

View Logs

Run the following:

runai logs train1

You should see a log of a running deep learning session:


View status on the Run:ai User Interface

  • Open the Run:ai user interface.
  • Under "Jobs" you can view the new Workload:


The image we used for training includes the Run:ai Training library. Among other features, this library allows the reporting of metrics from within the deep learning Job. Metrics such as progress, accuracy, loss, and epoch and step numbers.

  • Progress can be seen in the status column above.
  • To see other metrics, press the settings wheel on the top right mceclip4.png and select additional deep learning metrics from the list

Under Nodes you can see node utilization:


Stop Workload

Run the following:

runai delete job train1

This would stop the training workload. You can verify this by running runai list jobs again.

Next Steps