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Walk-through: Launch Interactive Build 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 Walk-through you will learn how to:

  • Use the Run:AI command-line interface (CLI) to start a deep learning Build workload
  • Open an ssh session to the Build workload
  • Stop the Build workload

It is also possible to open ports to specific services within the container. See "Next Steps" at the end of this article.


To complete this walk-through you must have:

Step by Step Walk-through


  • Open the Run:AI user interface at
  • Login
  • Go to "Projects"
  • Add a project named "team-a"
  • Allocate 2 GPUs to the project

Run Workload

  • At the command-line run:

    runai project set team-a
    runai submit build1 -i python -g 1 --interactive --command sleep --args infinity
  • The job is based on a sample docker image python

  • We named the job build1.
  • Note the interactive flag which means the job will not have a start or end. It is the researcher's responsibility to close the job.
  • The job is assigned to team-a with an allocation of a single GPU.
  • The command provided is --command sleep --args infinity. You must provide a command or the container will start and then exit immediately.

Follow up on the job's status by running:

runai list

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

A full list of Job statuses can be found here

To get additional status on your job run:

runai get build1

Get a Shell to the container


runai bash build1

This should provide a direct shell into the computer

View status on the Run:AI User Interface


Stop Workload

Run the following:

runai delete build1

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

Next Steps

Last update: August 26, 2020