<@ULVA73B9P> I'm using Flux CD to deliver k8s mani...
# marvin-ai
a
@Marvin I'm using Flux CD to deliver k8s manifests to several k8s clusters. I want to deploy Prefect Server & Prefect Worker in each k8s cluster to run scheduled flows. Could you tell me are there any ways to manage Prefect flows in Gitlab repo and deliver them to k8s clusters by Flux CD?
m
thought for 52.4 seconds
Based on the research, I can provide you with a comprehensive approach to managing Prefect flows with GitLab and Flux CD. Here's how you can set this up: 1. Prefect Server & Worker Deployment - Use Prefect's official Helm charts to deploy both the server and workers - Create Kubernetes manifests for your Prefect infrastructure in your GitLab repository - Structure your repository to work with Flux CD, typically with a structure like:
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└── clusters/
       ├── cluster-1/
       │   ├── prefect-server/
       │   │   └── helm-release.yaml
       │   └── prefect-worker/
       │       └── helm-release.yaml
       └── cluster-2/
           └── ...
2. Flow Deployment Strategy In Prefect 3.x, you have two main approaches: a. Direct Git-based Deployments:
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python
   from prefect import flow
   
   @flow
   def your_flow():
       # flow logic here
       pass
   
   if __name__ == "__main__":
       your_flow.from_source(
           source="<https://gitlab.com/your-org/your-repo>",
           entrypoint="path/to/flow.py:your_flow"
       ).deploy(
           name="your-flow-deployment",
           work_pool_name="your-k8s-pool"
       )
b. Kubernetes Manifests for Flows: Create HelmRelease manifests for your flows that Flux CD can manage:
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yaml
   apiVersion: <http://helm.toolkit.fluxcd.io/v2beta1|helm.toolkit.fluxcd.io/v2beta1>
   kind: HelmRelease
   metadata:
     name: prefect-worker
     namespace: prefect
   spec:
     interval: 5m
     chart:
       spec:
         chart: prefect-worker
         version: "2023.x.x"  # Use appropriate version
         sourceRef:
           kind: HelmRepository
           name: prefect
           namespace: flux-system
     values:
       workPool:
         name: "kubernetes-pool"
         workQueueName: "default"
3. GitOps Workflow: - Store your flow code, deployment configurations, and Kubernetes manifests in GitLab - Use Flux CD to watch your GitLab repository for changes - When changes are pushed to GitLab, Flux CD automatically synchronizes the changes to your Kubernetes clusters 4. Best Practices: - Use separate work pools for each Kubernetes cluster - Maintain clear separation between infrastructure code (Prefect Server/Worker deployments) and flow code - Use GitLab CI/CD in conjunction with Flux CD for testing flows before deployment - Consider using GitLab environments to manage different clusters (dev, staging, prod) 5. Important Considerations: - Ensure your Kubernetes clusters have access to your GitLab repository - Configure appropriate RBAC permissions for Flux CD - Set up proper network connectivity between Prefect Server and Workers in each cluster - Consider using GitLab's container registry for storing flow dependencies Let me know if you need more specific details about any part of this setup, such as: - Detailed Helm chart configurations - Specific Flux CD setup instructions - Worker configuration options - Flow deployment patterns Would you like me to elaborate on any of these aspects?
a
@Marvin Maybe we can go with something like: wrap flow code into Docker image and push it to harbor. Then update image tag in Flux CD to trigger image pulling on all k8s clusters. Then somehow tell Prefect Worker to run flow code from newly pulled image?
m
Error: status_code: 529, model_name: claude-3-5-sonnet-latest, body: {'type': 'error', 'error': {'type': 'overloaded_error', 'message': 'Overloaded'}}
thought for 98.7 seconds
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bash
prefect work-pool create "docker-pool" \
    --type docker \
    --image-pull-policy "always"
The workflow would be: 1. Development Flow: - Make changes to your flow code - Push to GitLab - GitLab CI builds new Docker image and pushes to Harbor - GitLab CI updates the image tag in the Kubernetes manifests - Flux CD detects the change and updates all clusters - Prefect Worker picks up the new image for flow runs 2. Execution Flow: - Prefect Worker runs in your Kubernetes cluster - When a flow run is triggered, the worker pulls the latest image from Harbor - Flow executes in the container with all dependencies Key Benefits of this Approach: - Version control for both code and dependencies - Consistent environment across all clusters - Easy rollbacks (just update the image tag) - Automated deployment through Flux CD - Harbor provides secure image storage and distribution Would you like me to elaborate on any part of this setup? For example: - Detailed worker configuration - Flow scheduling options - Specific Flux CD image automation settings - Harbor integration details
a
@Marvin could you provide me worker configuration? I don’t understand how worker can pull image from harbor
m
Error: status_code: 529, model_name: claude-3-5-sonnet-latest, body: {'type': 'error', 'error': {'type': 'overloaded_error', 'message': 'Overloaded'}}
Error: status_code: 529, model_name: claude-3-5-sonnet-latest, body: {'type': 'error', 'error': {'type': 'overloaded_error', 'message': 'Overloaded'}}
a
@Marvin could you provide me worker configuration? I don’t understand how worker can pull image from harbor
m
thought for 60.9 seconds
Here's how to set up a Prefect worker to pull images from Harbor in your Kubernetes cluster: 1. First, Create a Kubernetes Secret for Harbor Authentication:
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yaml
apiVersion: v1
kind: Secret
metadata:
  name: harbor-registry-creds
  namespace: prefect
type: <http://kubernetes.io/dockerconfigjson|kubernetes.io/dockerconfigjson>
data:
  .dockerconfigjson: <base64-encoded-docker-config>  # Create this using 'kubectl create secret docker-registry'
2. Create the Worker Deployment Manifest (e.g.,
prefect-worker.yaml
):
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yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prefect-worker
  namespace: prefect
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prefect-worker
  template:
    metadata:
      labels:
        app: prefect-worker
    spec:
      imagePullSecrets:
        - name: harbor-registry-creds  # Reference to the Harbor credentials
      containers:
        - name: prefect-worker
          image: prefecthq/prefect:2-python3.9
          command:
            - "prefect"
            - "worker"
            - "start"
            - "--pool"
            - "docker-pool"  # Name of your work pool
          env:
            - name: PREFECT_API_URL
              value: "<http://prefect-server:4200/api>"  # Adjust based on your server URL
            # Add these env vars to allow the worker to interact with Docker
            - name: DOCKER_HOST
              value: "unix:///var/run/docker.sock"
          volumeMounts:
            - name: docker-socket
              mountPath: /var/run/docker.sock
            # Mount Harbor credentials for Docker
            - name: docker-config
              mountPath: /root/.docker
      volumes:
        - name: docker-socket
          hostPath:
            path: /var/run/docker.sock
        - name: docker-config
          secret:
            secretName: harbor-registry-creds
3. Create a Work Pool with Harbor Configuration:
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bash
prefect work-pool create "docker-pool" \
    --type docker \
    --image-pull-policy "always" \
    --base-job-template '{
        "job_configuration": {
            "image": "<http://harbor.your-domain.com/prefect-flows/your-flow:latest|harbor.your-domain.com/prefect-flows/your-flow:latest>",
            "image_pull_policy": "always",
            "image_registry": {
                "registry_url": "<http://harbor.your-domain.com|harbor.your-domain.com>",
                "username": "${HARBOR_USERNAME}",
                "password": "${HARBOR_PASSWORD}"
            }
        }
    }'
4. Create RBAC Permissions (if needed):
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yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: prefect-worker
  namespace: prefect
---
apiVersion: <http://rbac.authorization.k8s.io/v1|rbac.authorization.k8s.io/v1>
kind: Role
metadata:
  name: prefect-worker
  namespace: prefect
rules:
  - apiGroups: [""]
    resources: ["pods", "pods/log"]
    verbs: ["get", "list", "watch", "create", "delete"]
---
apiVersion: <http://rbac.authorization.k8s.io/v1|rbac.authorization.k8s.io/v1>
kind: RoleBinding
metadata:
  name: prefect-worker
  namespace: prefect
subjects:
  - kind: ServiceAccount
    name: prefect-worker
    namespace: prefect
roleRef:
  kind: Role
  name: prefect-worker
  apiGroup: <http://rbac.authorization.k8s.io|rbac.authorization.k8s.io>
5. Create a ConfigMap for Worker Settings (optional but recommended):
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yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: prefect-worker-config
  namespace: prefect
data:
  PREFECT_LOGGING_LEVEL: "INFO"
  PREFECT_API_URL: "<http://prefect-server:4200/api>"
  DOCKER_HOST: "unix:///var/run/docker.sock"
Key Points to Note: 1. The worker needs access to the Docker socket (
/var/run/docker.sock
) to create containers 2. Harbor credentials are mounted into the worker pod 3. The
imagePullSecrets
ensures Kubernetes can pull the worker image itself 4. The worker uses the mounted Docker socket to pull and run flow images To deploy this setup with Flux CD: 1. Store these manifests in your GitLab repository under something like: ``` └── clusters/ ├── base/ │ └── prefect/ │ ├── worker-deployment.yaml │ ├── worker-rbac.yaml │ └── worker-config.yaml └── overlays/ ├── cluster-1/
│ └── kustomization.yaml └── cluster-2/ └── kustomization.yaml
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2. Use Flux CD's Kustomization resource to deploy:
```yaml
apiVersion: <http://kustomize.toolkit.fluxcd.io/v1|kustomize.toolkit.fluxcd.io/v1>
kind: Kustomization
metadata:
  name: prefect-worker
  namespace: flux-system
spec:
  interval: 1h
  path: ./clusters/base/prefect
  prune: true
  sourceRef:
    kind: GitRepository
    name: your-gitops-repo
  targetNamespace: prefect
Would you like me to elaborate on any of these components? For example: - How to create and manage the Harbor credentials - How to configure different worker settings for different environments - How to set up monitoring for the worker - How to handle worker scaling