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<@ULVA73B9P> I use prefect server 2.20.4 deployed ...
# ask-marvin
h
@Marvin I use prefect server 2.20.4 deployed in AWS EKS. I use k8s job infrastructure block and S3 storage block for my deployments. Once each deployment starts it moves to schedule status and then to crashed. After that it moves on to running status and completed successfully. How come?
m
✅ thought for 40.1 seconds
Based on the research and the GitHub issues, I can explain what's happening with your deployment's state transitions and provide some recommendations: 1. Root Cause: - This behavior is a known issue, particularly in Kubernetes environments where resource allocation and node provisioning take some time. - The state transition (Schedule → Crashed → Running → Completed) occurs when: 1. The flow is initially scheduled 2. The pod fails to start within the 
pod_watch_timeout_seconds
(causing the CRASHED state) 3. Kubernetes continues trying to schedule the pod (due to its retry mechanism) 4. Once resources become available (e.g., when Karpenter provisions new nodes), the pod starts successfully 5. The flow then runs and completes normally 2. Why This Happens: - The Kubernetes job remains active even after Prefect marks the flow run as "crashed" - When resources become available, the pod starts successfully, and Prefect updates the state to "running" - This can happen particularly when using auto-scaling solutions like Karpenter that take time to provision new nodes 3. Recommendations: a. Increase 
pod_watch_timeout_seconds
: - If your cluster typically takes longer to provision nodes, increase this timeout - This can be set in your Kubernetes infrastructure block b. Review Resource Requirements: - Ensure your pod resource requests are appropriate - Check if you have proper node selectors and tolerations configured c. Monitor Cluster Capacity: - Ensure your cluster has enough capacity or can scale quickly enough - Consider pre-warming your cluster if you have predictable workload patterns d. Consider Upgrading: - If possible, consider upgrading to Prefect 3.x which has improved infrastructure handling - In Prefect 3.x, you can use the more robust worker-based deployment model 4. Workaround: If you need to maintain the current version, you can: - Implement additional monitoring to track these state transitions - Add proper node affinity rules to ensure pods land on appropriate nodes - Configure Karpenter or your auto-scaler for faster node provisioning This behavior, while unexpected, doesn't affect the actual execution of your flows (they still complete successfully). However, it can make monitoring and tracking flow states more challenging. The main thing to focus on would be either increasing the pod watch timeout or ensuring your cluster can provision resources more quickly. Would you like me to provide more specific details about any of these recommendations or help you implement a particular solution?
h
@Marvin how to set this in my k8s job block in python?
m
✅ thought for 24.2 seconds
Based on the documentation and available information, here's how to set the 
pod_watch_timeout_seconds
in your Kubernetes Job infrastructure block:
Copy code
python
from prefect_kubernetes.infrastructure import KubernetesJob

# Create a new K8s Job block
k8s_job_block = KubernetesJob(
    pod_watch_timeout_seconds=300,  # 5 minutes, adjust as needed
    # ... your other configurations ...
    image="your-image:tag",
    namespace="your-namespace",
    # Add any other required configurations
)

# Save the block
k8s_job_block.save("your-block-name")
If you're modifying an existing block, you can load and update it:
Copy code
python
from prefect.blocks.kubernetes import KubernetesJob

# Load existing block
k8s_job_block = KubernetesJob.load("your-existing-block-name")

# Update the timeout
k8s_job_block.pod_watch_timeout_seconds = 300  # 5 minutes, adjust as needed

# Save the updated block
k8s_job_block.save("your-block-name")
Some recommendations for setting the timeout value: 1. Start with a value that's slightly longer than the typical time your cluster takes to provision nodes 2. Consider your auto-scaling group's warm-up period 3. Factor in any known delays in your environment 4. Common values range from 300 seconds (5 minutes) to 900 seconds (15 minutes) For example, if your Karpenter typically takes 3-4 minutes to provision nodes, you might want to set this to 360 seconds (6 minutes) to provide adequate buffer:
Copy code
python
k8s_job_block = KubernetesJob(
    pod_watch_timeout_seconds=360,
    # ... rest of your configuration ...
)
Remember that setting this timeout too high might delay error reporting when there are genuine issues, while setting it too low might cause false "crashed" states as you're experiencing now. The ideal value depends on your specific infrastructure characteristics. Would you like me to provide more specific guidance based on your particular setup or environment?
h
@Marvin turns out I have this param already set to 64000. Considering I want to keep using this prefect version, what else can I do?
m
✅ thought for 46.0 seconds
Based on the research and issues found, here are several concrete steps you can take to improve the reliability of your Kubernetes jobs while staying on Prefect 2.20.4: 1. Kubernetes Job Configuration Improvements:
Copy code
python
   from prefect_kubernetes.infrastructure import KubernetesJob

   k8s_job_block = KubernetesJob(
       image="your-image:tag",
       namespace="your-namespace",
       image_pull_policy="IfNotPresent",  # Prevent unnecessary pulls
       kubernetes_job_template={
           "spec": {
               "backoffLimit": 0,  # Prevent automatic retries by K8s
               "activeDeadlineSeconds": 3600,  # Set maximum runtime
               "template": {
                   "spec": {
                       "containers": [{
                           "name": "flow",
                           "resources": {
                               "requests": {
                                   "cpu": "500m",
                                   "memory": "512Mi"
                               },
                               "limits": {
                                   "cpu": "1000m",
                                   "memory": "1Gi"
                               }
                           }
                       }]
                   }
               }
           }
       }
   )
2. Node Affinity and Anti-Affinity Rules:
Copy code
python
   k8s_job_block = KubernetesJob(
       kubernetes_job_template={
           "spec": {
               "template": {
                   "spec": {
                       "affinity": {
                           "nodeAffinity": {
                               "requiredDuringSchedulingIgnoredDuringExecution": {
                                   "nodeSelectorTerms": [{
                                       "matchExpressions": [{
                                           "key": "prefectflows",
                                           "operator": "In",
                                           "values": ["true"]
                                       }]
                                   }]
                               }
                           }
                       }
                   }
               }
           }
       }
   )
3. Improve Pod Scheduling:
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python
   k8s_job_block = KubernetesJob(
       kubernetes_job_template={
           "spec": {
               "template": {
                   "spec": {
                       "priorityClassName": "high-priority",  # If you have priority classes configured
                       "tolerations": [{
                           "key": "dedicated",
                           "operator": "Equal",
                           "value": "prefect",
                           "effect": "NoSchedule"
                       }]
                   }
               }
           }
       }
   )
4. Environment Configuration: - Ensure your EKS cluster has proper node groups configured - Set up proper resource quotas for your namespace:
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yaml
   apiVersion: v1
   kind: ResourceQuota
   metadata:
     name: prefect-quota
     namespace: your-namespace
   spec:
     hard:
       cpu: "20"
       memory: 100Gi
       pods: "10"
5. Monitoring and Debugging: Add labels to help with monitoring:
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python
   k8s_job_block = KubernetesJob(
       kubernetes_job_template={
           "metadata": {
               "labels": {
                   "<http://prefect.io/flow-name|prefect.io/flow-name>": "${flow_name}",
                   "<http://prefect.io/deployment-name|prefect.io/deployment-name>": "${deployment_name}"
               }
           }
       }
   )
6. Infrastructure Cleanup: - Implement a cleanup policy for completed jobs:
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python
   k8s_job_block = KubernetesJob(
       kubernetes_job_template={
           "spec": {
               "ttlSecondsAfterFinished": 100  # Automatically delete job after completion
           }
       }
   )
7. Karpenter Configuration (if you're using Karpenter):
- Ensure your Karpenter provisioner is configured with appropriate settings:
Copy code
yaml
   apiVersion: <http://karpenter.sh/v1alpha5|karpenter.sh/v1alpha5>
   kind: Provisioner
   metadata:
     name: prefect
   spec:
     requirements:
       - key: <http://karpenter.sh/capacity-type|karpenter.sh/capacity-type>
         operator: In
         values: ["on-demand"]
     limits:
       resources:
         cpu: "100"
     ttlSecondsAfterEmpty: 30
Would you like me to elaborate on any of these solutions or provide more specific guidance for your particular use case? Also, it would be helpful to know: 1. Are you using Karpenter or standard EKS auto-scaling? 2. What kind of resource constraints are you typically seeing in the pod events when the crashes occur?
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