Skip to content

README.md

Latest commit

 

History

History
71 lines (43 loc) · 2.57 KB

README.md

File metadata and controls

71 lines (43 loc) · 2.57 KB
Raw

Quick Start Examples

This directory contains basic examples to get you started with KAI Scheduler.

Scheduling Queues

A queue represents a job queue in the cluster. Queues are an essential scheduling primitive and can reflect different scheduling guarantees, such as resource quota and priority. Queues are typically assigned to different consumers in the cluster (users, groups, or initiatives). A workload must belong to a queue in order to be scheduled.

KAI Scheduler supports multi-level hierarchical scheduling queues.

Default Queues

After installing KAI Scheduler, a default queue hierarchy is automatically created:

  • default-parent-queue – Top-level (parent) queue. By default, this queue has no reserved resource quotas, allowing governance of resource distribution for its leaf queues.
  • default-queue – Leaf (child) queue under the default-parent-queue top-level queue. Workloads should reference this queue.

The default queues are defined in default-queues.yaml.

No manual queue setup is required. Both queues will exist immediately after installation, allowing you to start submitting workloads right away.

Creating Additional Queues

To add custom queues, apply your queue configuration:

kubectl apply -f queues.yaml

For detailed configuration options, refer to the Scheduling Queues documentation.

Assigning Pods to Queues

To schedule a pod using KAI Scheduler, ensure the following:

  1. Specify the queue name using the kai.scheduler/queue: default-queue label on the pod/workload.
  2. Set the scheduler name in the pod specification as kai-scheduler.

This ensures the pod is placed in the correct scheduling queue and managed by KAI Scheduler.

⚠️ Workload Namespaces

When submitting workloads, make sure to use a dedicated namespace. Do not use the kai-scheduler namespace for workload submission.

Submitting Example Pods

CPU-Only Pods

To submit a simple pod that requests CPU and memory resources:

kubectl apply -f pods/cpu-only-pod.yaml

GPU Pods

Before running GPU workloads, ensure the NVIDIA GPU-Operator is installed in the cluster.

To submit a pod that requests a GPU resource:

kubectl apply -f pods/gpu-pod.yaml

Files

File Description
default-queues.yaml Default parent and leaf queue configuration
pods/cpu-only-pod.yaml Example CPU-only pod
pods/gpu-pod.yaml Example GPU pod