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Submitting Workloads via YAML

You can use YAML to submit Workloads directly to Run:ai. Below are examples of how to create training, interactive and inference workloads via YAML.

Submit Workload Example

Create a file named training1.yaml with the following text:

training1.yaml
apiVersion: run.ai/v2alpha1
kind: TrainingWorkload # (1)
metadata:
  name: job-1  # (2) 
  namespace: runai-team-a # (3)
spec:
  gpu:
    value: "1"
  image:
    value: gcr.io/run-ai-demo/quickstart
  name:
    value: job-1 # (4)
  1. This is a Training workload.
  2. Kubernetes object name. Mandatory, but has no functional significance.
  3. Namespace: Replace runai-team-a with the name of the Run:ai namespace for the specific Project (typically runai-<Project-Name>).
  4. Job name as appears in Run:ai. Can provide name, or create automatically if name prefix is configured.

Change the namespace and run: kubectl apply -f training1.yaml

Run: runai list jobs to see the new Workload.

Delete Workload Example

Run: kubectl delete -f training1.yaml to delete the Workload.

Creating a YAML syntax from a CLI command

An easy way to create a YAML for a workload is to generate it from the runai submit command by using the --dry-run flag. For example, run:

runai submit build1 -i ubuntu -g 1 --interactive --dry-run \
     -- sleep infinity 

The result will be the following Kubernetes object declaration:

apiVersion: run.ai/v2alpha1
kind: InteractiveWorkload  # (1)
metadata:
  creationTimestamp: null
  labels:
    PreviousJob: "true"
  name: job-0-2022-05-02t08-50-57
  namespace: runai-team-a
spec:
  command:
    value: sleep infinity
  gpu:
    value: "1"
  image:
    value: ubuntu
  imagePullPolicy:
    value: Always
  name:
    value: job-0

... Additional internal and status properties...
  1. This is an Interactive workload.

Inference Workload Example

Creating an inference workload is similar to the above two examples.

apiVersion: run.ai/v2alpha1
kind: InferenceWorkload
metadata:
  name: inference1
  namespace: runai-team-a
spec:
  name:
    value: inference1
  gpu:
    value: "0.5"
  image:
    value: "gcr.io/run-ai-demo/example-triton-server"
  minScale:
    value: 1
  maxScale:
    value: 2
  metric:
    value: concurrency # (1)
  target:
    value: 80  # (2)
  ports:
      items:
        port1:
          value:
            container: 8000
  1. Possible metrics can be cpu-utilization, latency, throughput, concurrency, gpu-utilization, custom. Different metrics may require additional installations at the cluster level.
  2. Inference requires a port to receive requests.

See Also


Last update: 2022-06-08
Created: 2022-05-01