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 - 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 firstname.lastname@example.org.
On the navigation bar of the Run:ai user interface at
<company-name>.run.ai, use the 'Support' button.
Or submit a ticket by clicking the button below:
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.
Reach out to email@example.com for credentials.
Run:ai Cloud Status Page¶
Run:ai cloud availabilty 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.
- Use one of the two options:
- One time collection: Run
runai-adm collect-logs. The command will generate a compressed file containing all of the existing Run:ai log files.
- 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.
- One time collection: Run
Both options include logs of Run:ai components. They do not include logs of researcher containers that may contain private information.
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:
|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-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|