Quickstart: Launch Unattended Training Workloads¶
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.
To complete this Quickstart you must have:
- Run:ai software installed on your Kubernetes cluster. See: Installing Run:ai on a Kubernetes Cluster
- Run:ai CLI installed on your machine. See: Installing the Run:ai Command-Line Interface
Step by Step Walkthrough¶
- Login to the Projects area of the Run:ai user interface.
- Add a Project named "team-a".
- Allocate 2 GPUs to the Project.
- 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
- Follow up on the Job's progress by running:
runai list jobs
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
Run the following:
runai logs train1
You should see a log of a running deep learning session:
View status on the Run:ai User Interface¶
- Open the Run:ai user interface.
- Under "Jobs" you can view the new Workload:
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 and select additional deep learning metrics from the list
Under Nodes you can see node utilization:
Run the following:
runai delete job train1
This would stop the training workload. You can verify this by running
runai list jobs again.
- Follow the Quickstart document: Launch Interactive Workloads
- Use your container to run an unattended training workload