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Quickstart: Launch Unattended Training Workloads

Introduction

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.

Prerequisites

To complete this Quickstart you must have:

Step by Step Walkthrough

Setup

  • Login to the Projects area of the Run:AI Administration user interface at https://app.run.ai/projects
  • 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 gcr.io/run-ai-demo/quickstart -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 gcr.io/run-ai-demo/quickstart. We named the job train1

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

The result:

mceclip00.png

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:

mceclip1.png

View status on the Run:AI User Interface

mceclip2.png

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:

mceclip5.png

Stop Workload

Run the following:

runai delete train1

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

Next Steps


Last update: December 1, 2020