Based on the research, there are several important considerations and solutions for running Prefect workers on GKE Autopilot with CPU throttling issues: 1. Worker Concurrency Management - Use the

--limit

flag when starting your worker to restrict concurrent flow runs - Example:

prefect worker start -p 'your-pool' --limit 5

- This helps prevent CPU oversubscription by controlling how many flows run simultaneously 2. Resource Optimization - GKE Autopilot has fixed resource allocations, so you'll need to: - Configure appropriate CPU requests/limits in your flow deployments - Consider splitting workloads across multiple work pools if you need more concurrent execution - Monitor CPU utilization to find the optimal concurrency settings 3. Performance Tuning - Adjust

prefetch-seconds

to optimize infrastructure setup times - Configure

PREFECT_WORKER_QUERY_SECONDS

to control how frequently workers poll for new work - These settings can help manage resource utilization and reduce throttling 4. Best Practices - Implement proper monitoring and observability to identify bottlenecks - Consider using separate work pools for different types of workloads with different resource requirements - Review your flow's resource requirements and adjust them according to actual usage To implement these solutions:

python
# In your deployment configuration
deployment = Deployment(
    name="my-deployment",
    flow=flow,
    work_pool_name="my-k8s-pool",
    work_queue_name="default",
    infrastructure=KubernetesJob(
        resources={
            "requests": {"cpu": "500m", "memory": "512Mi"},
            "limits": {"cpu": "1000m", "memory": "1Gi"}
        }
    )
)

When starting your worker:

bash
prefect worker start -p 'your-pool' --limit 5 --prefetch-seconds 60

You can also set environment variables for fine-tuning:

bash
export PREFECT_WORKER_QUERY_SECONDS=30

If you continue to experience issues after implementing these solutions, you might want to consider: 1. Using multiple smaller work pools instead of one large pool 2. Implementing a queueing strategy to handle peak loads 3. Upgrading to a standard GKE cluster instead of Autopilot for more control over resource allocation For more detailed information, you can refer to the GCP worker guide and worker concepts documentation.