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Quickstart: Launch Interactive Build Workloads

Introduction

The purpose of this article is to provide a quick ramp-up to running an interactive Workspace to allow building data science programs. data scientists typically use various tools such as Jupyter Notebook, PyCharm, or Visual Studio code. However, in this quickstart, we will start by launching a bare-bones Workspace without such tools.

With this Quickstart you will learn how to:

  • Start a workspace.
  • Open an ssh session to the workspace.
  • Stop the workspace.

Prerequisites

To complete this Quickstart, the Platform Administrator will need to provide you with:

  • Researcher access to Project in Run:ai named "team-a"
  • The project should be assigned a quota of at least 1 GPU.
  • A URL of the Run:ai Console. E.g. https://acme.run.ai.

To complete this Quickstart via the CLI, you will need to have the Run:ai CLI installed on your machine. There are two available CLI variants:

  • The older V1 CLI. See installation here
  • A newer V2 CLI, supported with clusters of version 2.18 and up. See installation here

Step by Step Quickstart

Login

Run runai login and enter your credentials.

Run runai login and enter your credentials.

Browse to the provided Run:ai user interface and log in with your credentials.

To use the API, you will need to obtain a token. Please follow the api authentication article.

Create a Workspace

Open a terminal and run:

runai config project team-a   
runai submit build1 -i ubuntu -g 1 --interactive -- sleep infinity

Note

For more information on the workload submit command, see cli documentation.

Open a terminal and run:

runai project set team-a
runai workspace submit build1 -i ubuntu -g 1 --command -- sleep infinity

Note

For more information on the workspace submit command, see cli documentation.

  • In the Run:ai UI select Workloads
  • Select New Workload and then Workspace
  • You should already have Cluster, Project and a start from scratch Template selected. Enter build1 as the name and press CONTINUE.
  • Select NEW ENVIRONMENT. Enter ubuntu as the name and ubuntu as the image. Then select CREATE ENVIRONMENT.
  • When the previous screen comes up, select one-gpu under the Compute resource.
  • Select CREATE WORKSPACE.

Note

For more information on submitting Workloads and creating Assets via the user interface, see Workload documentation.

curl -L 'https://<COMPANY-URL>/api/v1/workloads/workspaces' \ # (1)
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer <TOKEN>' \ # (2)
-d '{ 
    "name": "build1", 
    "projectId": "<PROJECT-ID>", '\ # (3)
    "clusterId": "<CLUSTER-UUID>", \ # (4)
    "spec": {
        "command" : "sleep",
        "args" : "infinity"
        "image": "ubuntu",
        "compute": {
        "gpuDevicesRequest": 1
        }
    }
}'
  1. <COMPANY-URL> is the link to the Run:ai user interface. For example acme.run.ai
  2. <TOKEN> is an API access token. see above on how to obtain a valid token.
  3. <PROJECT-ID> is the the ID of the team-a Project. You can get the Project ID via the Get Projects API
  4. <CLUSTER-UUID> is the unique identifier of the Cluster. You can get the Cluster UUID by adding the "Cluster ID" column to the Clusters view.

Note

  • The above API snippet will only work with Run:ai clusters of 2.18 and above. For older clusters, use, the now deprecated Cluster API.
  • For more information on the Workspace Submit API see API Documentation
  • This would start a workload of type Workspace for team-a with an allocation of a single GPU.
  • We named the Workload build1.
  • Note that, unlike a Training workload, a Workspace workload will not end automatically. It is the Researcher's responsibility to stop the Workload.
  • The command provided is sleep infinity. You must provide a command or the container will start and then exit immediately. Alternatively, when using the command line, replace these flags with --attach to attach immediately to a session.

List Workloads

Follow up on the Workload's progress by running:

runai list jobs
The result: mceclip20.png

runai workspace list

The result:

Workload     Type        Status      Project     Preemptible      Running/Requested Pods     GPU Allocation
─────────────────────────────────────────────────────────────────────────────────────────────────────────────
vs1          Workspace   Running     team-a      No               1/1                        1.00
  • Open the Run:ai user interface.
  • Under "Workloads" you can view the new Workspace:

Select the Workloads and press Show Details to see the Workload details

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

Describe Workload

To get additional status on your Workload run:

runai describe job build1

runai workspace describe build1

Workload parameters can be viewed by adding more columns to the Workload list and by reviewing the Event History tab for the specific Workload.

Get a Shell to the container

Run: 

runai bash build1


runai workspace bash build1

This should provide a direct shell into the computer

Stop Workload

Run the following:

runai delete job build1

runai workspace delete build1

Select the Workspace and press DELETE.

This would stop the workspace. You can verify this by running the list command again.