Submit a Run:ai Job via YAML¶

You can use YAML files to submit jobs directly to Kubernetes. A frequent scenario for using the Kubernetes YAML syntax to submit Jobs is integrations. Researchers may already be working with an existing system that submits Jobs, and want to continue working with the same system.

Terminology¶

We differentiate between three types of Workloads:

• Train workloads. Train workloads are characterized by a deep learning session that has a start and an end. A Training session can take anywhere from a few minutes to a couple of weeks. It can be interrupted in the middle and later restored. Training workloads typically utilize large percentages of GPU computing power and memory.
• Build workloads. Build workloads are interactive. They are used by data scientists to write machine learning code and test it against subsets of the data. Build workloads typically do not maximize usage of the GPU.
• Inference workloads. Inference workloads are used for serving models in production. For details on how to submit Inference workloads via YAML see here.

The internal Kubernetes implementation of a Run:ai Job is a CRD (Customer Resource) named RunaiJob which is similar to a Kubernetes Job.

Run:ai extends the Kubernetes Scheduler. A Kubernetes Scheduler is the software that determines which workload to start on which node. Run:ai provides a custom scheduler named runai-scheduler.

The Run:ai scheduler schedules computing resources by associating Workloads with Run:ai Projects:

• A Project is assigned with a GPU quota through the Run:ai Run:ai User Interface.
• A workload must be associated with a Project name and will receive resources according to the defined quota for the Project and the currently running Workloads

Internally, Run:ai Projects are implemented as Kubernetes namespaces. The scripts below assume that the code is being run after the relevant namespace has been set.

Submit Workloads¶

• <JOB-NAME>. The name of the Job.
• <IMAGE-NAME>. The name of the docker image to use. Example: gcr.io/run-ai-demo/quickstart.
• <USER-NAME>. The name of the user submitting the Job. The name is used for display purposes only when Run:ai is installed in an unauthenticated mode.
• <REQUESTED-GPUs>. An integer number of GPUs you request to be allocated for the Job. Examples: 1, 2.
• <NAMESAPCE>. The name of the Project's namespace. This is usually runai-<PROJECT-NAME>.

Regular Jobs¶

Copy the following into a file and change the parameters:

apiVersion: run.ai/v1
kind: RunaiJob (* see note below)
metadata:
  name: <JOB-NAME>
  namespace: <NAMESPACE>
  labels:
    priorityClassName: "build" (* see note below)
spec:
  template:
    metadata:
      labels:
        user: <USER-NAME>
    spec:
      containers:
      - name: <JOB-NAME>
        image: <IMAGE-NAME>
        resources:
          limits:
            nvidia.com/gpu: <REQUESTED-GPUs>
      restartPolicy: Never
      schedulerName: runai-scheduler

To submit the job, run:

kubectl apply -f <FILE-NAME>

Using Fractional GPUs¶

To submit a Job with fractions of a GPU, replace <REQUESTED-GPUs> with a fraction in quotes. e.g.

limits:
  nvidia.com/gpu: "0.5"

where "0.5" is the requested GPU fraction.

Mapping Additional Flags¶

Run:ai Command-Line runai submit has a significant number of flags. The easiest way to find out the mapping from a flag to the correct YAML attribute is to use the --dry-run flag.

For example, to find the location of the --large-shm flag, run:

> runai submit -i ubuntu --large-shm --dry-run
Template YAML file can be found at:
/var/folders/xb/rnf9b1bx2jg45c7jprv71d9m0000gn/T/job.yaml185826190

Delete Workloads¶

To delete a Run:ai workload, delete the Job:

kubectl delete runaijob <JOB-NAME>

See Also¶

• See how to use the above YAML syntax with Kubernetes API
• Use the Researcher REST API to submit, list and delete Jobs.