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Run:ai Documentation Library

Welcome to the Run:ai documentation area. For an introduction about what is the Run:ai Platform see Run:ai platform on the website

The Run:ai documentation is targeting three personas:

  • Run:ai Administrator - Responsible for the setup and the day-to-day administration of the product. Administrator documentation can be found here.

  • Researcher - Using Run:ai to submit Jobs. Researcher documentation can be found here.

  • Developer - Using various APIs to manipulate Jobs and integrate with other systems. Developer documentation can be found here.

How to get support

To get support use the following channels:

  • Write to

  • On the navigation bar of the Run:ai user interface at <company-name>, use the 'Support' button.

  • Or submit a ticket by clicking the button below:

Submit a Ticket


Run:AI provides its customers with access to the Run:AI Customer Community portal in order to submit tickets, track ticket progress and update support cases.

Customer Community Portal

Reach out to for credentials.

Run:ai Cloud Status Page

Run:ai cloud availabilty is monitored at

Collect Logs to Send to Support

As an IT Administrator, you can collect Run:ai logs to send to support:

  • Install the Run:ai Administrator command-line interface.
  • Use one of the two options:
    1. One time collection: Run runai-adm collect-logs. The command will generate a compressed file containing all of the existing Run:ai log files.
    2. Continuous send Run runai-adm -d <HOURS_DURATION>. The command will send Run:ai logs directly to Run:ai support for the duration stated. Data sent will not include current logs. Only logs created going forward will be sent.


Both options include logs of Run:ai components. They do not include logs of researcher containers that may contain private information.

Example Code

Code for the Docker images referred to on this site is available at

The following images are used throughout the documentation:

Image Description Source Basic training image. Multi-GPU support Distributed training using MPI and Horovod
zembutsu/docker-sample-nginx Build (interactive) with Connected Ports Hyperparameter Optimization Use X11 forwarding from Docker image Image used for tool integration (PyCharm and VSCode) and Basic Inference

Last update: 2022-11-15
Created: 2020-07-16