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 run.ai website.

The Run:ai documentation is targeting three personas:

Run:ai Administrator - Is 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:

On the Run:ai user interface at <company-name>.run.ai, use the 'Contact Support' link on the top right.
Or submit a ticket by clicking the button below:

Submit a Ticket

Community¶

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 customer support for credentials.

Run:ai Cloud Status Page¶

Run:ai cloud availability is monitored at status.run.ai.

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.
Run runai-adm collect-logs. The command will generate a compressed file containing all of the existing Run:ai log files.

Note

The tar file packages the logs of Run:ai components only. It does 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 https://github.com/run-ai/docs/tree/master/quickstart.

The following images are used throughout the documentation:

Image	Description	Source
gcr.io/run-ai-demo/quickstart	Basic training image. Multi-GPU support	https://github.com/run-ai/docs/tree/master/quickstart/main
gcr.io/run-ai-demo/quickstart-distributed	Distributed training using MPI and Horovod	https://github.com/run-ai/docs/tree/master/quickstart/distributed
zembutsu/docker-sample-nginx	Build (interactive) with Connected Ports	https://github.com/zembutsu/docker-sample-nginx
gcr.io/run-ai-demo/quickstart-hpo	Hyperparameter Optimization	https://github.com/run-ai/docs/tree/master/quickstart/hpo
gcr.io/run-ai-demo/quickstart-x-forwarding	Use X11 forwarding from Docker image	https://github.com/run-ai/docs/tree/master/quickstart/x-forwarding
gcr.io/run-ai-demo/pycharm-demo	Image used for tool integration (PyCharm and VSCode)	https://github.com/run-ai/docs/tree/master/quickstart/python%2Bssh
gcr.io/run-ai-demo/example-triton-client and gcr.io/run-ai-demo/example-triton-server	Basic Inference	https://github.com/run-ai/models/tree/main/models/triton

Contributing to the documentation¶

This documentation is made better by a number of individuals from our customer and partner community. If you see something worth fixing, please comment at the bottom of the page or create a pull request via GitHub. The public GitHub repository can be found on the top-right of this page.