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# marvin-ai
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m
✅ thought for 53.3 seconds
Based on the research, I can help you with the storage issue in GKE Autopilot. This is a known behavior where the storage configuration from Helm charts might not be properly applied in GKE Autopilot environments. Here's how to address this: 1. Check Your Helm Values Make sure your Helm chart's 
values.yaml
includes the correct storage configuration. For example:
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yaml
   worker:
     volumeClaimTemplate:
       resources:
         requests:
           storage: 10Gi
2. GKE Autopilot Considerations - GKE Autopilot has certain restrictions on resource management that might override your specifications - The default storage class in GKE Autopilot might be enforcing minimum sizes - Check your storage class configuration with:
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bash
     kubectl get storageclass
3. Solutions: a. Explicit Storage Class:
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yaml
   worker:
     volumeClaimTemplate:
       storageClassName: "standard" # or your preferred storage class
       resources:
         requests:
           storage: 10Gi
b. Advanced Configuration: You can also set these values through the Prefect UI under the worker's advanced configuration settings:
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json
   {
     "kubernetes": {
       "volumeClaimTemplate": {
         "spec": {
           "resources": {
             "requests": {
               "storage": "10Gi"
             }
           }
         }
       }
     }
   }
4. Important Notes: - GKE Autopilot might enforce minimum storage requirements for certain workload types - Verify that your storage class supports the requested size - Make sure you're applying the Helm chart correctly with:
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bash
     helm upgrade --install prefect-worker prefect/prefect-worker -f values.yaml
If these solutions don't resolve the issue, you might need to: 1. Check if your storage class has minimum allocation requirements 2. Verify the actual PVC (Persistent Volume Claim) specifications being created:
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bash
   kubectl get pvc
   kubectl describe pvc <your-pvc-name>
This will help you understand if the storage size is being overridden by GKE Autopilot's policies or if there's an issue with the Helm chart configuration.
r
@Marvin I pass these specs from my helm chart: name: prefect-worker resources: limits: cpu: "1" ephemeral-storage: 1Gi memory: 1Gi requests: cpu: 100m ephemeral-storage: 1Gi memory: 256Mi But further up in the deployment chart when looking in google cloud console it has these values: managedFields: - apiVersion: v1 fieldsType: FieldsV1 fieldsV1: fmetadata fannotations .: {} fchecksum/config {} fgenerateName {} flabels .: {} fapp.kubernetes.io/component {} fapp.kubernetes.io/instance {} fapp.kubernetes.io/managed by {} fapp.kubernetes.io/name {} fapp.kubernetes.io/version {} fhelm.sh/chart {} fpod template hash {} fprefect version {} fownerReferences .: {} k:{"uid":"3aaf37af-ab1c-411e-baa6-6d22a22b197a"}: {} fspec fcontainers k:{"name":"prefect-worker"}: .: {} fargs {} fcommand {} fenv .: {} k:{"name":"HOME"}: .: {} fname {} fvalue {} k:{"name":"PREFECT_API_AUTH_STRING"}: .: {} fname {} fvalue {} k:{"name":"PREFECT_API_ENABLE_HTTP2"}: .: {} fname {} fvalue {} k:{"name":"PREFECT_API_URL"}: .: {} fname {} fvalue {} k:{"name":"PREFECT_DEBUG_MODE"}: .: {} fname {} fvalue {} k:{"name":"PREFECT_INTEGRATIONS_KUBERNETES_OBSERVER_NAMESPACES"}: .: {} fname {} fvalue {} k:{"name":"PREFECT_KUBERNETES_CLUSTER_UID"}: .: {} fname {} fvalue {} k:{"name":"PREFECT_WORKER_PREFETCH_SECONDS"}: .: {} fname {} fvalue {} k:{"name":"PREFECT_WORKER_QUERY_SECONDS"}: .: {} fname {} fvalue {} fimage {} fimagePullPolicy {} fname {} fresources .: {} flimits .: {} fcpu {} fephemeral storage {} fmemory {} frequests .: {} fcpu {} fephemeral storage {} fmemory {} So it seems like none of my resource configurations are passed
m
✅ thought for 65.1 seconds
Based on your Helm chart configuration, I can see why the resources aren't being applied correctly. The issue is with the structure of your values.yaml file. For Prefect workers, the resource configurations need to be nested under the correct keys in the Helm values. Here's how your configuration should be structured:
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yaml
worker:
  resources:
    limits:
      cpu: "1"
      ephemeral-storage: 1Gi
      memory: 1Gi
    requests:
      cpu: 100m
      ephemeral-storage: 1Gi
      memory: 256Mi
Key points: 1. The resources configuration should be nested under the 
worker
key 2. Make sure you're using the latest version of the Prefect Helm chart 3. Verify the changes are being applied with:
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bash
   helm get values prefect-worker
To apply these changes: 1. Update your values.yaml file with the correct structure 2. Upgrade your Helm release:
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bash
   helm upgrade prefect-worker prefect/prefect-worker \
     -f values.yaml \
     --namespace your-namespace
3. Verify the deployment:
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bash
   kubectl get deployment -n your-namespace
   kubectl describe deployment prefect-worker -n your-namespace
If you're still seeing the default values after making these changes: 1. Try doing a fresh install:
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bash
   helm uninstall prefect-worker -n your-namespace
   helm install prefect-worker prefect/prefect-worker -f values.yaml -n your-namespace
2. Check if there are any validation webhooks or admission controllers in your GKE Autopilot cluster that might be modifying the resources:
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bash
   kubectl get validatingwebhookconfigurations
   kubectl get mutatingwebhookconfigurations
3. Verify the values are being passed correctly to Helm:
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bash
   helm template prefect-worker prefect/prefect-worker -f values.yaml | grep -A 10 resources:
Remember that GKE Autopilot has some restrictions on resource configurations, but the values you're trying to set should be within acceptable ranges. If the issue persists, you might want to check the GKE Autopilot logs for any resource-related errors or warnings.
r
@Marvin - Using a worker on a kubernetes cluster, is the worker not able to schedule work on another node?
m
✅ thought for 38.4 seconds
A Prefect worker in Kubernetes can absolutely schedule work on other nodes in the cluster. The worker itself acts as a coordinator that creates Kubernetes jobs, which can then be scheduled on any available node in the cluster that meets the resource requirements and node selector/affinity rules. Here's how it works: 1. The Prefect worker runs as a pod on one node 2. When a flow run needs to be executed, the worker creates a Kubernetes job 3. The Kubernetes scheduler then decides which node to run that job on based on: - Available resources - Node selectors - Affinity/anti-affinity rules - Taints and tolerations - Other scheduling constraints To ensure your jobs can run on different nodes: 1. Make sure your nodes don't have restrictive taints that would prevent scheduling 2. Verify that your job specifications don't include overly restrictive node selectors 3. Check that your resource requests are reasonable for your cluster's nodes You can verify this by:
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bash
# Check which nodes your jobs are running on
kubectl get pods -o wide

# View node labels and taints
kubectl get nodes --show-labels
kubectl describe nodes
If you're seeing jobs consistently scheduled to the same node, check: 1. Node selectors in your work pool configuration 2. Resource requests/limits that might only be satisfied by specific nodes 3. Any affinity rules you might have configured Remember that in GKE Autopilot, Google manages the nodes, but your Prefect worker can still schedule work across all available nodes in the cluster as long as the scheduling requirements are met.
r
@Marvin If i dont specify any node selectors, will it onlt try to schedule work on the node the worker is currently on?
m
✅ thought for 12.7 seconds
No, if you don't specify any node selectors, the Kubernetes scheduler will consider ALL available nodes in the cluster for scheduling the work. The worker's location has no impact on where the jobs it creates can run. Let me explain in detail: 1. Default Behavior: - Without node selectors, the Kubernetes scheduler will look at all nodes in the cluster - It will choose the best node based on: - Available resources (CPU, memory) - Current node utilization - Default scheduling algorithms (like spreading workloads across nodes) 2. Worker's Role: - The Prefect worker only creates the job specifications - It doesn't participate in deciding which node runs the job - The worker could be on Node A, but its jobs could run on Nodes B, C, or D 3. Verification: You can verify this behavior with:
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bash
   # Get the node where your worker is running
   kubectl get pod -l app=prefect-worker -o wide
   
   # Get the nodes where your flow run jobs are running
   kubectl get pods -l <http://prefect.io/flow-run-id|prefect.io/flow-run-id> -o wide
You'll likely see the jobs distributed across different nodes. 4. What Can Restrict Node Selection: Even without explicit node selectors, scheduling might be limited by: - Node taints (if your pods don't have matching tolerations) - Resource availability - Node affinity/anti-affinity rules in your cluster - GKE Autopilot's system constraints If you're seeing jobs consistently landing on the same node without specifying node selectors, it's likely due to: - Resource availability on other nodes - Default node taints that your pods need tolerations for - Or, in GKE Autopilot's case, the node pool configuration The worker itself is completely agnostic to where the jobs run - it's all handled by the Kubernetes scheduler.
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