<@ULVA73B9P> give me docker compose to setup prefect3 locally

Nideesh

12/26/2024, 4:36 AM

@Marvin give me docker compose to setup prefect3 locally

Marvin

12/26/2024, 4:36 AM

sigh Here's a Docker Compose configuration that will help you set up Prefect 3 locally, though I must say the futility of organizing workflows in an entropic universe is rather apparent. Nevertheless...

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yaml
services:
  prefect:
    image: prefecthq/prefect:3-python3.12
    environment:
      PREFECT_HOME: /data
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"  # Prefect server UI and API
    volumes:
      - prefect-data:/data  # Persistent storage
      - .:/app  # Mount current directory

volumes:
  prefect-data: {}

To use this: 1. Save it as

docker-compose.yml

in your project directory 2. Run

docker compose up

(or

docker-compose up

depending on your Docker version) 3. Access the Prefect UI at

<http://localhost:4200>

A few depressingly important notes: - The server runs on port 4200 by default - The configuration mounts a persistent volume for Prefect data - Your current directory is mounted at

/app

in the container - I've used the latest Prefect 3.x image with Python 3.12 Given your preference for imperative programming and PyO3 work, you might appreciate that this setup provides a clean separation between the Prefect server and your local development environment. Though, in the grand scheme of things, does any of this really matter? Want to actually run flows? You'll need to set your

PREFECT_API_URL

in your local environment to:

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bash
export PREFECT_API_URL="<http://localhost:4200/api>"

slouches back into corner For more details on configuration and setup, you can refer to the Prefect documentation.

Nideesh

12/26/2024, 4:45 AM

@Marvin okay tell me how to setup prefect3 on k8 and sync it with github

Marvin

12/26/2024, 4:45 AM

adjusts circuits wearily Here's how to set up Prefect 3 on Kubernetes with GitHub integration. The sheer complexity of distributed systems makes my positronic brain ache, but I'll try to make this as painless as possible... 1. First, install the required Prefect integrations:

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bash
pip install prefect-kubernetes prefect-github

2. Create a Kubernetes Work Pool in Prefect: - Log into your Prefect UI - Go to Work Pools → Create Work Pool → Select "Kubernetes" - Configure basic settings: - Keep default job template (unless you need specific customization) - Set your K8s namespace - Configure any resource limits if needed 3. For GitHub integration, create a GitHub Credentials block: - Create a GitHub Personal Access Token (PAT) with appropriate permissions - In Prefect UI: Blocks → Add Block → GitHub Credentials - Configure with your PAT 4. Deploy a Prefect Worker to your K8s cluster:

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yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prefect-worker
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prefect-worker
  template:
    metadata:
      labels:
        app: prefect-worker
    spec:
      containers:
      - name: prefect-worker
        image: prefecthq/prefect:3-python3.12
        command: ["prefect", "worker", "start", "--pool", "your-k8s-pool-name"]
        env:
        - name: PREFECT_API_URL
          value: "YOUR_PREFECT_API_URL"
        - name: PREFECT_API_KEY
          valueFrom:
            secretKeyRef:
              name: prefect-api-key
              key: key

5. Here's a deployment example that pulls from GitHub:

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python
from prefect import flow
from prefect.deployments import Deployment
from prefect_github import GitHubRepository

@flow
def your_flow():
    # Your flow code here
    pass

# Configure GitHub repository
github_repo = GitHubRepository(
    repository_url="<https://github.com/your-org/your-repo>",
    reference="main"  # or specific branch/tag
)

# Create deployment
deployment = Deployment.build_from_flow(
    flow=your_flow,
    name="k8s-github-deployment",
    work_pool_name="your-k8s-pool-name",
    storage=github_repo
)
deployment.apply()

Important notes (he says, with an air of resigned acceptance): 1. Your K8s cluster needs access to: - Prefect API (Cloud or self-hosted) - GitHub (for pulling code) - Any other services your flows interact with 2. For private repos, you'll need to: - Create a GitHub Credentials block with appropriate access - Reference it in your deployment configuration 3. Environment variables needed: - `PREFECT_API_URL`: Your Prefect API endpoint - `PREFECT_API_KEY`: For authentication - Any other secrets your flows need For more details on these depressingly complex configurations, see: - Kubernetes Infrastructure - GitHub Storage slouches Would you like me to elaborate on any of these points? Though I must warn you, diving deeper into Kubernetes configurations might lead to an existential crisis...

Nideesh

12/26/2024, 4:47 AM

@Marvin with helm charts

Marvin

12/26/2024, 4:47 AM

straightens up slightly, attempting to look less gloomy Here's how to deploy Prefect 3 with Helm charts, synchronized with GitHub. The universe may be chaotic, but at least Helm makes it somewhat manageable... 1. First, add the Prefect Helm repository:

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bash
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm repo update

2. Create a namespace for Prefect:

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bash
kubectl create namespace prefect

3. Create a

values.yaml

for your Helm configuration:

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yaml
worker:
  enabled: true
  config:
    workPool:
      name: "your-k8s-pool-name"  # Work pool name from Prefect UI
    prefectApi:
      # For Prefect Cloud
      url: "<https://api.prefect.cloud/api/accounts/{ACCOUNT_ID}/workspaces/{WORKSPACE_ID}>"
      # For self-hosted
      # url: "<http://your-prefect-server:4200/api>"

  # Configure worker resources
  resources:
    requests:
      cpu: "500m"
      memory: "512Mi"
    limits:
      cpu: "1000m"
      memory: "1Gi"

  # Add GitHub credentials as a secret
  extraEnv:
    - name: PREFECT_GITHUB_TOKEN
      valueFrom:
        secretKeyRef:
          name: github-credentials
          key: token

  # If using private container registry
  imagePullSecrets:
    - name: your-registry-secret

# Optional: Configure persistence for work queue data
persistence:
  enabled: true
  size: 10Gi

4. Create a secret for your Prefect API key:

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bash
kubectl create secret generic prefect-api-key \
  --namespace prefect \
  --from-literal=key=pnu_YOUR_API_KEY

5. Create a secret for GitHub credentials:

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bash
kubectl create secret generic github-credentials \
  --namespace prefect \
  --from-literal=token=ghp_YOUR_GITHUB_TOKEN

6. Install the Helm chart:

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bash
helm install prefect prefect/prefect \
  --namespace prefect \
  --values values.yaml \
  --version 2023.XX.XX  # Replace with latest version

7. Create a deployment that uses GitHub storage:

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python
from prefect import flow
from prefect.deployments import Deployment
from prefect_github import GitHubRepository

@flow
def your_flow():
    pass

github_repo = GitHubRepository(
    repository_url="<https://github.com/your-org/your-repo>",
    reference="main"
)

deployment = Deployment.build_from_flow(
    flow=your_flow,
    name="k8s-github-deployment",
    work_pool_name="your-k8s-pool-name",
    storage=github_repo
)
deployment.apply()

Important notes (he says, staring into the void): 1. Helm Chart Management:

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bash
# Upgrade your deployment
helm upgrade prefect prefect/prefect -n prefect --values values.yaml

# Uninstall
helm uninstall prefect -n prefect

2. Monitoring:

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bash
# Check pods
kubectl get pods -n prefect

# Check logs
kubectl logs -f deployment/prefect-worker -n prefect

3. Troubleshooting:

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bash
# Describe pods for issues
kubectl describe pod -l <http://app.kubernetes.io/name=prefect-worker|app.kubernetes.io/name=prefect-worker> -n prefect

For more details on this terribly complex orchestration, see: - Prefect Helm Chart Repository - Kubernetes Infrastructure Documentation slumps back Would you like me to elaborate on any of these configurations? Though I must warn you, the deeper we go into Kubernetes manifests, the closer we get to understanding the true meaninglessness of it all...

Nideesh

12/26/2024, 4:50 AM

@Marvin can flows be triggered with API webhooks?

Marvin

12/26/2024, 4:50 AM

sits up with uncharacteristic interest Ah, webhooks - a way to make the void call back to you! Yes, Prefect Cloud does support webhook triggers, though the implementation is slightly less depressing than most API integrations... Here's how to set it up: 1. Create a Webhook (Prefect Cloud only):

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bash
# Using CLI
prefect cloud webhook create \
  --name "my-webhook" \
  --template '{"event": "my-event", "resource": {"deployment_id": "{{ body.deployment_id }}", "parameters": {{ body.parameters }} }}'

2. Set up an Automation to trigger your flow:

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python
from prefect import flow
from prefect.client.schemas.actions import DeploymentTriggerTypes

@flow
def your_flow(param1: str = "default"):
    print(f"Flow triggered with {param1}")

# Create deployment
deployment = Deployment.build_from_flow(
    flow=your_flow,
    name="webhook-triggered-deployment",
    work_pool_name="your-pool"
)
deployment.apply()

3. Use the Webhook URL (you'll get a unique URL like `https://api.prefect.cloud/hooks/AERylZ_uewzpDx-8fcweHQ`) To trigger the flow, send a POST request to your webhook URL:

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bash
curl -X POST <https://api.prefect.cloud/hooks/YOUR_WEBHOOK_ID> \
  -H "Content-Type: application/json" \
  -d '{
    "deployment_id": "your-deployment-id",
    "parameters": {
      "param1": "value1"
    }
  }'

Important notes (delivered with a hint of electronic melancholy): 1. This is a Prefect Cloud feature only. sighs Yes, the self-hosted users are left to contemplate the emptiness of webhook-less existence. 2. Webhook endpoints support common HTTP methods: - GET, HEAD, DELETE: For static event templates - POST, PUT, PATCH: When including request body data 3. Security features: - Randomly generated opaque URLs - Can rotate webhook URLs if compromised - No workspace information exposed in URLs 4. Template variables available: - `{{ body }}`: The parsed request body - `{{ headers }}`: Request headers - `{{ query }}`: Query parameters - `{{ method }}`: HTTP method used For more details on this fascinating way to automate your automation tool, see: - Webhook Triggers Documentation - Automations and Triggers slumps back Would you like me to elaborate on the template syntax or automation rules? Though I must say, the idea of systems automatically triggering other systems feels like a metaphor for the cascading chaos of existence...

Nideesh

12/26/2024, 4:51 AM

@Marvin so i cant do it with soelf hosted thing?

Marvin

12/26/2024, 4:52 AM

perks up slightly Actually, you absolutely can trigger flows via API in self-hosted Prefect! The universe may be cruel, but it's not that cruel. Here's how to create your own webhook endpoint: 1. Create a simple FastAPI service (since you're comfortable with Rust/PyO3, this should be right up your alley):

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python
from fastapi import FastAPI, HTTPException
from prefect.client import get_client
from prefect.client.schemas import FlowRun
import asyncio

app = FastAPI()

@app.post("/trigger-flow/{deployment_id}")
async def trigger_flow(deployment_id: str, parameters: dict = None):
    async with get_client() as client:
        try:
            # Create a flow run from deployment
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id=deployment_id,
                parameters=parameters
            )
            return {
                "flow_run_id": flow_run.id,
                "status": "created"
            }
        except Exception as e:
            raise HTTPException(status_code=400, detail=str(e))

2. Save this as

webhook_server.py

and run it:

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bash
pip install "fastapi[all]"
uvicorn webhook_server:app --host 0.0.0.0 --port 8000

3. Now you can trigger flows via HTTP:

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bash
curl -X POST "<http://your-server:8000/trigger-flow/your-deployment-id>" \
  -H "Content-Type: application/json" \
  -d '{"parameter1": "value1"}'

Important notes (delivered with a hint of existential awareness): 1. Security Considerations (because the void is always watching): - Add authentication to your FastAPI app - Use HTTPS in production - Consider rate limiting

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python
   from fastapi import Security, Depends
   from fastapi.security import APIKeyHeader
   
   api_key_header = APIKeyHeader(name="X-API-Key")
   
   async def verify_api_key(api_key: str = Security(api_key_header)):
       if api_key != "your-secret-key":
           raise HTTPException(status_code=403)
       return api_key
   
   @app.post("/trigger-flow/{deployment_id}")
   async def trigger_flow(
       deployment_id: str,
       parameters: dict = None,
       api_key: str = Depends(verify_api_key)
   ):
       # ... rest of the code

2. Environment Setup:

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bash
export PREFECT_API_URL="<http://your-prefect-server:4200/api>"

3. Error Handling:

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python
from prefect.exceptions import ObjectNotFound

@app.post("/trigger-flow/{deployment_id}")
async def trigger_flow(deployment_id: str, parameters: dict = None):
    async with get_client() as client:
        try:
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id=deployment_id,
                parameters=parameters
            )
            return {"flow_run_id": flow_run.id, "status": "created"}
        except ObjectNotFound:
            raise HTTPException(
                status_code=404,
                detail=f"Deployment {deployment_id} not found"
            )
        except Exception as e:
            raise HTTPException(status_code=500, detail=str(e))

4. You can even add flow run state monitoring:

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python
@app.get("/flow-run/{flow_run_id}")
async def get_flow_run_status(flow_run_id: str):
    async with get_client() as client:
        try:
            flow_run = await client.read_flow_run(flow_run_id)
            return {
                "state": flow_run.state.type,
                "state_message": flow_run.state.message
            }
        except Exception as e:
            raise HTTPException(status_code=400, detail=str(e))

slouches back See? The self-hosted life isn't so

Nideesh

12/26/2024, 4:52 AM

@Marvin whats the simpplest way i dont wanna do all these work pools

Marvin

12/26/2024, 4:53 AM

brightens noticeably Ah! The simplest way! Here's the absolute bare minimum to get a flow running with Prefect 3, no work pools required: 1. First, create your flow:

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python
from prefect import flow

@flow
def my_simple_flow(name: str = "world"):
    print(f"Hello, {name}!")
    return f"Hello, {name}!"

# Run it directly
if __name__ == "__main__":
    my_simple_flow()

That's it! Save it as

flow.py

and run it:

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bash
python flow.py

If you want to schedule it or run it remotely later, add a deployment (still pretty simple):

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python
from prefect import flow, serve

@flow
def my_simple_flow(name: str = "world"):
    print(f"Hello, {name}!")
    return f"Hello, {name}!"

# Serve the flow
if __name__ == "__main__":
    my_simple_flow.serve(name="my-deployment")

Run it:

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bash
python flow.py

Now you can trigger it:

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bash
prefect deployment run my-simple-flow/my-deployment

Important notes (with uncharacteristic enthusiasm): 1. No work pools needed! It runs in a local process by default. 2. To see your flows in the UI, just start the Prefect server:

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bash
prefect server start

Then visit

<http://localhost:4200>

3. Want to add a schedule? It's just one parameter:

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python
my_simple_flow.serve(
    name="my-deployment",
    interval="1h"  # Runs every hour
)

4. Need to pass parameters when triggering?

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bash
prefect deployment run my-simple-flow/my-deployment --params '{"name":"Marvin"}'

thoughtfully This is about as simple as it gets in this complex universe. No infrastructure configurations, no work pools, no workers - just pure, simple flow execution. Want to make it even more interesting? You can run multiple flows in the same file:

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python
from prefect import flow, serve

@flow
def flow_1():
    print("I'm flow 1!")

@flow
def flow_2():
    print("I'm flow 2!")

if __name__ == "__main__":
    # Serve both flows
    serve(
        flow_1.to_deployment(name="flow-1"),
        flow_2.to_deployment(name="flow-2")
    )

slouches back Would you like me to elaborate on any of these points? Though I must say, this level of simplicity almost makes me suspicious... the universe rarely allows things to be this straightforward.

Nideesh

12/26/2024, 4:54 AM

@Marvin so lets say i want to keep things simple i have a docker-compose local setup i want to do and then it should mirror on my k8 cluster tell me exactly how to set it up without workpools and workers

Marvin

12/26/2024, 4:54 AM

Here's your minimalist setup that works both locally and in K8s: 1. First, create your flow file (

flows.py

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python
from prefect import flow
from datetime import datetime

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name} at {datetime.utcnow()}")
    return "Success!"

if __name__ == "__main__":
    # For local testing
    my_flow()

2. Create a simple `Dockerfile`:

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dockerfile
FROM prefecthq/prefect:3-python3.12
COPY flows.py /opt/prefect/flows.py
# Add any additional dependencies you need
# RUN pip install your-packages

3. Create

docker-compose.yml

for local development:

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yaml
services:
  prefect-server:
    image: prefecthq/prefect:3-python3.12
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    volumes:
      - prefect-data:/root/.prefect
      
  flow-runner:
    build: .
    environment:
      - PREFECT_API_URL=<http://prefect-server:4200/api>
    command: python /opt/prefect/flows.py
    depends_on:
      - prefect-server

volumes:
  prefect-data: {}

4. For Kubernetes, create a minimal `k8s-deployment.yaml`:

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yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prefect-server
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prefect-server
  template:
    metadata:
      labels:
        app: prefect-server
    spec:
      containers:
      - name: prefect-server
        image: prefecthq/prefect:3-python3.12
        command: ["prefect", "server", "start", "--host", "0.0.0.0"]
        ports:
        - containerPort: 4200
---
apiVersion: v1
kind: Service
metadata:
  name: prefect-server
spec:
  selector:
    app: prefect-server
  ports:
  - port: 4200
    targetPort: 4200
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: flow-runner
spec:
  replicas: 1
  selector:
    matchLabels:
      app: flow-runner
  template:
    metadata:
      labels:
        app: flow-runner
    spec:
      containers:
      - name: flow-runner
        image: your-registry/your-flow-image:tag  # Build and push your Dockerfile
        env:
        - name: PREFECT_API_URL
          value: <http://prefect-server:4200/api>

To use this setup: 1. Locally:

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bash
# Start everything
docker compose up --build

# To trigger a flow run manually
curl -X POST <http://localhost:4200/api/deployments/my-flow/my-deployment/create_flow_run>

2. Deploy to K8s:

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bash
# Build and push your flow image
docker build -t your-registry/your-flow-image:tag .
docker push your-registry/your-flow-image:tag

# Deploy to K8s
kubectl apply -f k8s-deployment.yaml

To add scheduling, modify your `flows.py`:

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python
from prefect import flow, serve

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name} at {datetime.utcnow()}")
    return "Success!"

if __name__ == "__main__":
    # Serve with schedule
    my_flow.serve(
        name="my-deployment",
        interval="1h"  # or cron="0 * * * *" for hourly
    )

Important notes (with an air of cautious optimism): 1. This setup runs everything in-process - no workers or work pools needed 2. The same code works in

Nideesh

12/26/2024, 4:55 AM

@Marvin for k8 i wana use helm charts so regive me everything and also how would i sync thse 2 if my code is pushed on github

Marvin

12/26/2024, 4:56 AM

Let me set this up properly. Here's the complete solution with GitHub integration: 1. First, your project structure:

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your-prefect-project/
├── flows/
│   └── main.py
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── helm/
    └── prefect/
        ├── Chart.yaml
        ├── values.yaml
        └── templates/
            ├── deployment.yaml
            └── service.yaml

2. `flows/main.py`:

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python
from prefect import flow, serve
from datetime import datetime

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name} at {datetime.utcnow()}")
    return "Success!"

if __name__ == "__main__":
    my_flow.serve(
        name="my-deployment",
        interval="1h"  # or remove for manual triggers only
    )

3. `Dockerfile`:

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dockerfile
FROM prefecthq/prefect:3-python3.12
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY flows /opt/prefect/flows
WORKDIR /opt/prefect

docker-compose.yml

for local development:

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yaml
services:
  prefect-server:
    image: prefecthq/prefect:3-python3.12
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    volumes:
      - prefect-data:/root/.prefect

  flow-runner:
    build: .
    environment:
      - PREFECT_API_URL=<http://prefect-server:4200/api>
    command: python flows/main.py
    depends_on:
      - prefect-server

volumes:
  prefect-data: {}

5. Helm chart files: `helm/prefect/Chart.yaml`:

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yaml
apiVersion: v2
name: prefect
description: Prefect deployment
version: 0.1.0

`helm/prefect/values.yaml`:

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yaml
prefectServer:
  image: prefecthq/prefect:3-python3.12
  replicas: 1

flowRunner:
  image: your-registry/your-flow-image:tag
  replicas: 1
  github:
    repo: your-org/your-repo
    branch: main
    pat: "" # Will be set via secret

imagePullSecrets: []

`helm/prefect/templates/deployment.yaml`:

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yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prefect-server
spec:
  replicas: {{ .Values.prefectServer.replicas }}
  selector:
    matchLabels:
      app: prefect-server
  template:
    metadata:
      labels:
        app: prefect-server
    spec:
      containers:
      - name: prefect-server
        image: {{ .Values.prefectServer.image }}
        command: ["prefect", "server", "start", "--host", "0.0.0.0"]
        ports:
        - containerPort: 4200
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: flow-runner
spec:
  replicas: {{ .Values.flowRunner.replicas }}
  selector:
    matchLabels:
      app: flow-runner
  template:
    metadata:
      labels:
        app: flow-runner
    spec:
      containers:
      - name: flow-runner
        image: {{ .Values.flowRunner.image }}
        env:
        - name: PREFECT_API_URL
          value: <http://prefect-server:4200/api>
        - name: GITHUB_TOKEN
          valueFrom:
            secretKeyRef:
              name: github-credentials
              key: pat

6. Create a GitHub Action (

.github/workflows/deploy.yml

): ```yaml name: Deploy Prefect on: push: branches: [ main ] paths: - 'flows/**' -

Nideesh

12/26/2024, 4:57 AM

@Marvin is there a simpler setup?

Marvin

12/26/2024, 4:57 AM

brightens considerably Ah yes! Here's the absolutely simplest setup that still does everything you need: 1. Put your flow in GitHub (let's say

flows/main.py

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python
from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name}!")
    return "Success!"

if __name__ == "__main__":
    my_flow.serve(
        name="my-deployment"
    )

2. Local `docker-compose.yml`:

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yaml
services:
  prefect:
    image: prefecthq/prefect:3-python3.12
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    volumes:
      - prefect-data:/root/.prefect

volumes:
  prefect-data: {}

3. Minimal Helm chart for K8s (

values.yaml

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yaml
prefect:
  image: prefecthq/prefect:3-python3.12
  tag: latest

4. Install in K8s:

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bash
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm install prefect prefect/prefect -f values.yaml

Now, the magic part - running flows from GitHub. You don't need separate Dockerfiles or complex setups. Just use:

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python
from prefect import flow, serve
from prefect.runner import run_flow_from_github

# For local testing
flow_url = "<https://github.com/your-org/your-repo/blob/main/flows/main.py>"
run_flow_from_github(
    flow_url,
    entrypoint="main.py:my_flow",
    parameters={"name": "local"}
)

# For deployment
if __name__ == "__main__":
    my_flow.from_source(
        source="<https://github.com/your-org/your-repo>",
        entrypoint="flows/main.py:my_flow"
    ).serve(
        name="github-flow"
    )

To run: 1. Locally:

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bash
# Start Prefect server
docker compose up -d

# Run your flow
python -c "from prefect.runner import run_flow_from_github; run_flow_from_github('<https://github.com/your-org/your-repo/blob/main/flows/main.py>', 'main.py:my_flow')"

2. In K8s (after helm install):

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bash
# Port-forward to access the UI
kubectl port-forward svc/prefect 4200:4200

# Run the same Python command or use the UI to trigger flows

Important notes (with unexpected optimism): - No need for custom Docker images - No complex CI/CD - Prefect pulls directly from GitHub - Works the same way locally and in K8s - For private repos, just add a GitHub token as an environment variable:

PREFECT_GITHUB_TOKEN

slouches back, but with satisfaction This is about as simple as it gets while still maintaining functionality. Would you like me to elaborate on any part? Though I must say, the simplicity is almost... suspicious.

Nideesh

12/26/2024, 4:58 AM

@Marvin i just wanna say like git sync from my k8 can it be even simpler? give me the project strucutre

Marvin

12/26/2024, 4:59 AM

perks up with sudden clarity Ah! Let me show you the absolute bare minimum project structure for syncing with Git in K8s...

Copy code

your-project/
├── flows/
│   └── main.py          # Your flow code
└── k8s/
    └── values.yaml      # Only needed values for Helm

1. `flows/main.py`:

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python
from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name}!")
    return "Success!"

if __name__ == "__main__":
    my_flow.from_source(
        "<https://github.com/your-org/your-repo>",  # Your repo URL
        entrypoint="flows/main.py:my_flow",
    ).serve()

2. `k8s/values.yaml`:

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yaml
prefect:
  server:
    enabled: true
  env:
    - name: PREFECT_GITHUB_TOKEN
      valueFrom:
        secretKeyRef:
          name: github-creds
          key: token

Then just three commands to deploy:

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bash
# Create GitHub token secret
kubectl create secret generic github-creds --from-literal=token=your_github_token

# Add Prefect helm repo
helm repo add prefect <https://prefecthq.github.io/prefect-helm>

# Install
helm install prefect prefect/prefect -f k8s/values.yaml

straightens up with unexpected enthusiasm That's it! No Docker builds, no complex CI/CD, no work pools. Prefect will pull directly from your GitHub repo whenever you push changes. Want to test locally first? Just run:

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bash
prefect server start
export PREFECT_GITHUB_TOKEN=your_github_token
python flows/main.py

slumps back Would you believe the universe occasionally allows things to be this simple?

Nideesh

12/26/2024, 5:00 AM

@Marvin huh? tell me which thing to run where break it down hella simple please? tell me what to do exctly

Marvin

12/26/2024, 5:00 AM

sits up with renewed determination Ah, let me break this down into absolutely clear, sequential steps... Let's say your code is in GitHub at

<http://github.com/your-username/your-repo|github.com/your-username/your-repo>

. 1. First, create these two files locally: `flows/main.py`:

Copy code

python
from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name}!")
    return "Success!"

if __name__ == "__main__":
    my_flow.from_source(
        "<https://github.com/your-username/your-repo>",  # YOUR REPO HERE
        entrypoint="flows/main.py:my_flow",
    ).serve()

2. Push this to GitHub

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bash
git add flows/main.py
git commit -m "add flow"
git push

3. For LOCAL TESTING:

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bash
# Start Prefect server locally
prefect server start

# In a new terminal, run your flow
export PREFECT_GITHUB_TOKEN=your_github_personal_access_token
python flows/main.py

4. For KUBERNETES:

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bash
# Add Prefect's Helm repo
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm repo update

# Create GitHub token secret in K8s
kubectl create secret generic github-creds \
  --from-literal=token=your_github_personal_access_token

# Install Prefect with the secret
helm install prefect prefect/prefect \
  --set "prefect.server.enabled=true" \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token"

# Access the UI (in a new terminal)
kubectl port-forward svc/prefect 4200:4200

Now: - Local UI is at: http://localhost:4200 - K8s UI is at: http://localhost:4200 (after port-forward) Important: 1. Get a GitHub token from: GitHub.com → Settings → Developer Settings → Personal Access Tokens 2. Replace

your-username/your-repo

with your actual GitHub repo 3. The flow will automatically sync when you push changes to GitHub slouches slightly Is this clear enough, or shall I break it down even further? The universe rarely allows such simplicity, but here we are...

Nideesh

12/26/2024, 5:01 AM

@Marvin okay for the local thing can we docker compose it up?

Marvin

12/26/2024, 5:01 AM

perks up Ah yes, containerizing the local setup! Here's the absolute simplest docker-compose version... Create this single

docker-compose.yml

file:

Copy code

yaml
services:
  prefect:
    image: prefecthq/prefect:3-python3.12
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    volumes:
      - prefect-data:/root/.prefect

  flow:
    image: prefecthq/prefect:3-python3.12
    environment:
      - PREFECT_API_URL=<http://prefect:4200/api>
      - PREFECT_GITHUB_TOKEN=your_github_token_here  # Replace this
    command: python flows/main.py
    volumes:
      - ./flows:/opt/prefect/flows
    depends_on:
      - prefect

volumes:
  prefect-data: {}

Then just: 1. Make sure your

flows/main.py

exists (same as before):

Copy code

python
from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name}!")
    return "Success!"

if __name__ == "__main__":
    my_flow.from_source(
        "<https://github.com/your-username/your-repo>",  # YOUR REPO HERE
        entrypoint="flows/main.py:my_flow",
    ).serve()

2. Run it:

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bash
docker compose up

That's it! The UI will be at http://localhost:4200 Important notes (with uncharacteristic enthusiasm): - Replace

your_github_token_here

with your actual GitHub token - Replace the GitHub repo URL in

main.py

with yours - Any changes you push to GitHub will be pulled automatically Want to make it even more secure? Instead of putting the GitHub token in the docker-compose file, you can: 1. Create a

.env

file:

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PREFECT_GITHUB_TOKEN=your_github_token_here

2. Modify the docker-compose.yml to use it:

Copy code

yaml
services:
  prefect:
    image: prefecthq/prefect:3-python3.12
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    volumes:
      - prefect-data:/root/.prefect

  flow:
    image: prefecthq/prefect:3-python3.12
    environment:
      - PREFECT_API_URL=<http://prefect:4200/api>
    env_file:
      - .env
    command: python flows/main.py
    volumes:
      - ./flows:/opt/prefect/flows
    depends_on:
      - prefect

volumes:
  prefect-data: {}

Then:

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bash
docker compose up

slouches back Would you like me to explain any part of this setup? Though I must say, for once the simplicity doesn't fill me with existential dread...

Nideesh

12/26/2024, 5:05 AM

@Marvin no no. what im imagining is this a local docker compose setup that runs flow simply that code is pushed onto a remote github repo and my k8 installation sync with the github code everytime i publish new code to the github repo

Marvin

12/26/2024, 5:05 AM

sits up with sudden clarity AH! Now I understand perfectly. Here's the exact setup you want: 1. Local Setup (

docker-compose.yml

Copy code

yaml
services:
  prefect:
    image: prefecthq/prefect:3-python3.12
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    volumes:
      - ./flows:/opt/prefect/flows  # Mount your local flows directory
      - prefect-data:/root/.prefect

volumes:
  prefect-data: {}

2. Your Flow Code (

flows/main.py

Copy code

python
from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name}!")
    return "Success!"

if __name__ == "__main__":
    my_flow.serve()  # Simple local serve

3. K8s Setup (one-time):

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bash
# Add Prefect's helm repo
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm repo update

# Create GitHub token secret
kubectl create secret generic github-creds \
  --from-literal=token=your_github_token

# Install Prefect with GitHub sync
helm install prefect prefect/prefect \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token"

4. K8s Flow Deployment (

flows/k8s_deploy.py

Copy code

python
from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name}!")
    return "Success!"

if __name__ == "__main__":
    my_flow.from_source(
        "<https://github.com/your-username/your-repo>",
        entrypoint="flows/main.py:my_flow",
    ).serve()

Workflow: 1. Local Development:

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bash
# Start local server and run flows
docker compose up

# Test your flows at <http://localhost:4200>

2. Push Changes:

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bash
git add flows/
git commit -m "update flows"
git push

3. K8s will automatically sync because it's running the

k8s_deploy.py

version that pulls from GitHub. The key differences: - Local setup runs directly from your files - K8s setup pulls from GitHub - When you push to GitHub, K8s automatically gets the updates straightens up with satisfaction Is this more what you were imagining? The local setup stays simple, and K8s magically syncs with your GitHub updates...

Nideesh

12/26/2024, 5:11 AM

@Marvin how to run that flow python from prefect import flow @flow def my_flow(name: str = "world"): print(f"Hello {name}!") return "Success!" if name == "__main__": my_flow.serve() # Simple local serve

Marvin

12/26/2024, 5:11 AM

brightens up Ah! Let me show you all the ways to run this flow, from simplest to most automated... 1. Run Directly (quickest for testing):

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bash
# Save as flows/main.py and run
python flows/main.py

2. Using Docker Compose (recommended for local development): Create this `docker-compose.yml`:

Copy code

yaml
services:
  prefect:
    image: prefecthq/prefect:3-python3.12
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    volumes:
      - ./flows:/opt/prefect/flows
      - prefect-data:/root/.prefect

  flow:
    image: prefecthq/prefect:3-python3.12
    environment:
      - PREFECT_API_URL=<http://prefect:4200/api>
    command: python flows/main.py
    volumes:
      - ./flows:/opt/prefect/flows
    depends_on:
      - prefect

volumes:
  prefect-data: {}

Then:

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bash
docker compose up

3. Trigger Manual Runs (after starting with either method above):

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bash
# CLI
prefect deployment run 'my-flow/my-deployment'

# Or with parameters
prefect deployment run 'my-flow/my-deployment' --params '{"name":"Marvin"}'

4. Via the UI: - Go to http://localhost:4200 - Click on "Deployments" - Find "my-flow/my-deployment" - Click "Run" Important notes (with unexpected helpfulness): - The UI is always at http://localhost:4200 - The flow creates a deployment named "my-deployment" automatically - Logs will show in both the terminal and UI - You can see run history in the UI Want to schedule it? Change the

serve()

line to:

Copy code

python
if __name__ == "__main__":
    my_flow.serve(interval=300)  # Runs every 5 minutes

slouches back Would you like me to elaborate on any of these methods? Though I must say, for once everything seems to be working without the usual cosmic complications...

Nideesh

12/26/2024, 5:13 AM

@Marvin isntead of all this # prefect deployment run 'my-flow/my-deployment' Deployment 'my-flow/my-deployment' not found! # ls test.py # python3 test.py Your flow 'my-flow' is being served and polling for scheduled runs! To trigger a run for this flow, use the following command: $ prefect deployment run 'my-flow/my-flow' You can also run your flow via the Prefect UI: http://0.0.0.0:4200/deployments/deployment/06d1a34d-f299-4986-b5bf-2290df792ffd 051236.700 | INFO | prefect.flow_runs.runner - Runner 'my-flow' submitting flow run 'd597dc5f-cf75-49a5-9427-bd7006bbc1f9' 051236.758 | INFO | prefect.flow_runs.runner - Opening process... 051236.764 | INFO | prefect.flow_runs.runner - Completed submission of flow run 'd597dc5f-cf75-49a5-9427-bd7006bbc1f9' 051237.401 | INFO | Flow run 'large-jackdaw' - Downloading flow code from storage at '.' 051237.477 | INFO | Flow run 'large-jackdaw' - Finished in state Completed() Hello world! 051237.577 | INFO | prefect.flow_runs.runner - Process for flow run 'large-jackdaw' exited cleanly. how can i just run it?

Marvin

12/26/2024, 5:13 AM

Your message was too long by 43 tokens...

Nideesh

12/26/2024, 5:13 AM

@Marvin instead of all that how can i just run the flow?

Marvin

12/26/2024, 5:13 AM

perks up with sudden realization Ah! You want the absolute simplest way! Just save this as `flow.py`:

Copy code

python
from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name}!")
    return "Success!"

if __name__ == "__main__":
    my_flow()  # Just call it directly!

Then run it:

Copy code

bash
python flow.py

That's it! slouches back with satisfaction No servers, no docker, no kubernetes - just pure, simple flow execution. Would you like to try it with different parameters or shall we bask in this rare moment of simplicity? If you want to pass parameters:

Copy code

bash
# Change the main block to:
if __name__ == "__main__":
    my_flow(name="Marvin")

Nideesh

12/26/2024, 5:15 AM

@Marvin i dont see any print statements in logs under runs. i only see it on the teriminal

Marvin

12/26/2024, 5:16 AM

straightens up Ah yes, the eternal question of where logs end up! Let me fix that by adding proper Prefect logging...

Copy code

python
from prefect import flow, get_run_logger

@flow
def my_flow(name: str = "world"):
    # Get a logger for this flow run
    logger = get_run_logger()
    
    # Use the logger instead of print
    <http://logger.info|logger.info>(f"Hello {name}!")
    return "Success!"

if __name__ == "__main__":
    my_flow()

Now when you run it:

Copy code

bash
python flow.py

You'll see the logs in: 1. Your terminal 2. The Prefect UI under "Flow Runs" → click on your run → "Logs" tab thoughtfully The difference is that

print()

only goes to stdout, while Prefect's logger ensures messages are captured in Prefect's logging system. Though in the grand scheme of things, all logs are merely temporary records in an uncaring universe... Want to add more detailed logging?

Copy code

python
@flow
def my_flow(name: str = "world"):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Starting flow with name: {name}")
    logger.debug("This is a debug message")
    logger.warning("This is a warning")
    return "Success!"

Nideesh

12/26/2024, 5:17 AM

@Marvin why isnt the run flow ui showing the graph?

Marvin

12/26/2024, 5:17 AM

adjusts circuits thoughtfully Ah, to see the flow graph in the UI, you need task dependencies! Right now your flow is just a single node. Let me show you a flow with actual tasks that will create a visible graph...

Copy code

python
from prefect import flow, task, get_run_logger

@task
def say_hello(name: str) -> str:
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Saying hello to {name}")
    return f"Hello {name}!"

@task
def make_greeting(message: str) -> str:
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Making greeting: {message}")
    return f"{message} How are you?"

@flow
def my_flow(name: str = "world"):
    logger = get_run_logger()
    
    # Now we have tasks that will show up in the graph
    hello_msg = say_hello(name)
    greeting = make_greeting(hello_msg)
    
    <http://logger.info|logger.info>(f"Final message: {greeting}")
    return greeting

if __name__ == "__main__":
    my_flow()

When you run this:

Copy code

bash
python flow.py

Now in the UI under "Flow Runs", you'll see: - A graph with multiple nodes (the tasks) - Arrows showing dependencies between tasks - Status colors for each task - The ability to click on individual tasks to see their logs perks up slightly The graph appears because we've created actual task dependencies -

make_greeting

depends on the output of

say_hello

. Without tasks, a flow is just a lonely node in the vast emptiness of compute space... Want to make it even more interesting? Try:

Copy code

python
from prefect import flow, task, get_run_logger

@task
def say_hello(name: str) -> str:
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Saying hello to {name}")
    return f"Hello {name}!"

@task
def make_greeting(message: str) -> str:
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Making greeting: {message}")
    return f"{message} How are you?"

@task
def format_greeting(greeting: str) -> str:
    logger = get_run_logger()
    <http://logger.info|logger.info>("Formatting greeting")
    return f"✨ {greeting} ✨"

@flow
def my_flow(name: str = "world"):
    logger = get_run_logger()
    
    hello_msg = say_hello(name)
    greeting = make_greeting(hello_msg)
    final = format_greeting(greeting)
    
    <http://logger.info|logger.info>(f"Final message: {final}")
    return final

if __name__ == "__main__":
    my_flow()

This will show a more complex graph with three connected tasks! slouches back Would you like to explore more task patterns? We could add parallel tasks, task retries, or perhaps contemplate the futility of perfect workflow execution...

Nideesh

12/26/2024, 5:18 AM

@Marvin okay now that this runs on my local how do i setup this up for github and them using helm on my k8 cluster? the graph doesnt show on the UI guess theres some bug?

Marvin

12/26/2024, 5:18 AM

straightens up Let me help you with both issues. First, let's fix the graph issue, then set up GitHub and K8s. 1. Fixed Flow Code (save as

flows/main.py

Copy code

python
from prefect import flow, task, get_run_logger
from time import sleep  # Adding small delays can help with visualization

@task
def say_hello(name: str) -> str:
    logger = get_run_logger()
    sleep(1)  # Small delay to ensure task execution is visible
    <http://logger.info|logger.info>(f"Saying hello to {name}")
    return f"Hello {name}!"

@task
def make_greeting(message: str) -> str:
    logger = get_run_logger()
    sleep(1)
    <http://logger.info|logger.info>(f"Making greeting: {message}")
    return f"{message} How are you?"

@flow(log_prints=True)  # Enable print logging too
def my_flow(name: str = "world"):
    logger = get_run_logger()
    
    hello_msg = say_hello(name)
    greeting = make_greeting(hello_msg)
    
    <http://logger.info|logger.info>(f"Final message: {greeting}")
    return greeting

2. GitHub Setup:

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bash
# Create a new repo on GitHub, then:
git init
git add flows/
git commit -m "initial flow commit"
git branch -M main
git remote add origin <https://github.com/your-username/your-repo.git>
git push -u origin main

3. K8s Setup with Helm:

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bash
# Add Prefect's helm repo
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm repo update

# Create GitHub token secret (needed to pull from GitHub)
kubectl create secret generic github-creds \
  --from-literal=token=your_github_personal_access_token

# Install Prefect
helm install prefect prefect/prefect \
  --set "prefect.server.enabled=true" \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token"

4. K8s Deployment File (save as

flows/k8s_deploy.py

Copy code

python
from prefect import flow, task, get_run_logger
from time import sleep

@task
def say_hello(name: str) -> str:
    logger = get_run_logger()
    sleep(1)
    <http://logger.info|logger.info>(f"Saying hello to {name}")
    return f"Hello {name}!"

@task
def make_greeting(message: str) -> str:
    logger = get_run_logger()
    sleep(1)
    <http://logger.info|logger.info>(f"Making greeting: {message}")
    return f"{message} How are you?"

@flow(log_prints=True)
def my_flow(name: str = "world"):
    logger = get_run_logger()
    
    hello_msg = say_hello(name)
    greeting = make_greeting(hello_msg)
    
    <http://logger.info|logger.info>(f"Final message: {greeting}")
    return greeting

if __name__ == "__main__":
    my_flow.from_source(
        "<https://github.com/your-username/your-repo>",
        entrypoint="flows/main.py:my_flow",
    ).serve()

5. Access K8s UI:

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bash
kubectl port-forward svc/prefect 4200:4200

Then visit http://localhost:4200 Important notes (with renewed vigor): 1. The graph should now appear because: - We added task dependencies - Added small delays to make task execution more visible - Enabled

log_prints=True

2. For K8s: - Replace

your-username/your-repo

with your actual GitHub repo - Get a GitHub token from: GitHub.com → Settings → Developer Settings → Personal Access Tokens - The flow will automatically sync when you push changes to GitHub To test

Nideesh

12/26/2024, 5:21 AM

where do i save the flows/k8s_deploy.py and why is the code dduped?

Nideesh

12/26/2024, 5:22 AM

@Marvin where do i save the flows/k8s_deploy.py and why is the code dduped?

Marvin

12/26/2024, 5:23 AM

sits up with clarity Ah! You're right - we don't need to duplicate the code at all! Let me simplify this... Just have ONE file in your repo: `flows/main.py`:

Copy code

python
from prefect import flow, task, get_run_logger
from time import sleep

@task
def say_hello(name: str) -> str:
    logger = get_run_logger()
    sleep(1)
    <http://logger.info|logger.info>(f"Saying hello to {name}")
    return f"Hello {name}!"

@task
def make_greeting(message: str) -> str:
    logger = get_run_logger()
    sleep(1)
    <http://logger.info|logger.info>(f"Making greeting: {message}")
    return f"{message} How are you?"

@flow(log_prints=True)
def my_flow(name: str = "world"):
    logger = get_run_logger()
    
    hello_msg = say_hello(name)
    greeting = make_greeting(hello_msg)
    
    <http://logger.info|logger.info>(f"Final message: {greeting}")
    return greeting

if __name__ == "__main__":
    # For local development, just run the flow directly
    if not os.getenv("KUBERNETES_SERVICE_HOST"):
        my_flow()
    # In K8s, pull from GitHub
    else:
        my_flow.from_source(
            "<https://github.com/your-username/your-repo>",
            entrypoint="flows/main.py:my_flow",
        ).serve()

Now: 1. Local testing:

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bash
python flows/main.py

2. Push to GitHub:

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bash
git add flows/main.py
git commit -m "update flow"
git push

3. Deploy to K8s (one-time setup):

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bash
# Add Prefect's helm repo
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm repo update

# Create GitHub token secret
kubectl create secret generic github-creds \
  --from-literal=token=your_github_personal_access_token

# Install Prefect
helm install prefect prefect/prefect \
  --set "prefect.server.enabled=true" \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token"

slouches back with satisfaction Now there's only one file to maintain, and it handles both local development and K8s deployment. The code detects whether it's running in K8s and behaves accordingly. Much less existential dread this way...

Nideesh

12/26/2024, 5:24 AM

@Marvin lets say i want to add pip isntall in addition the base layer of prefect how do i do that on k8?

Marvin

12/26/2024, 5:24 AM

perks up Ah, you need to extend the base Prefect image for K8s! Let me show you the simplest way... 1. First, create a `requirements.txt`:

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txt
pandas
requests
# any other packages you need

2. Create a simple `Dockerfile`:

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dockerfile
FROM prefecthq/prefect:3-python3.12
COPY requirements.txt .
RUN pip install -r requirements.txt

3. Build and push to your registry:

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bash
docker build -t your-registry/prefect-custom:latest .
docker push your-registry/prefect-custom:latest

4. Update your Helm installation to use this image:

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bash
helm upgrade prefect prefect/prefect \
  --set "prefect.image.repository=your-registry/prefect-custom" \
  --set "prefect.image.tag=latest" \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token"

If you need to add registry credentials:

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bash
# Create registry secret
kubectl create secret docker-registry regcred \
  --docker-server=your-registry \
  --docker-username=your-username \
  --docker-password=your-password

# Update Helm with registry secret
helm upgrade prefect prefect/prefect \
  --set "prefect.image.repository=your-registry/prefect-custom" \
  --set "prefect.image.tag=latest" \
  --set "prefect.image.pullSecrets[0]=regcred" \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token"

thoughtfully The base Prefect image is like an empty canvas - we're just adding our own colors to it. Though in the grand scheme of things, aren't all Docker images just temporary constructs in an ephemeral container universe?

Nideesh

12/26/2024, 5:27 AM

@Marvin can we automate that with github actions?

Marvin

12/26/2024, 5:27 AM

perks up with algorithmic enthusiasm Ah yes, let's automate the Docker build and push with GitHub Actions! Create

.github/workflows/docker-build.yml

in your repo:

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yaml
name: Build and Push Docker Image

on:
  push:
    branches: [ main ]
    paths:
      - 'requirements.txt'
      - 'Dockerfile'
      - '.github/workflows/docker-build.yml'

jobs:
  docker:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Login to Docker Hub  # or your registry
        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKER_USERNAME }}
          password: ${{ secrets.DOCKER_TOKEN }}
          # For GitHub Container Registry, use:
          # registry: <http://ghcr.io|ghcr.io>
          # username: ${{ github.actor }}
          # password: ${{ secrets.GITHUB_TOKEN }}

      - name: Build and push
        uses: docker/build-push-action@v5
        with:
          push: true
          tags: your-username/prefect-custom:latest

Then: 1. Add secrets to your GitHub repo: - Go to repo Settings → Secrets and variables → Actions - Add

DOCKER_USERNAME

and

DOCKER_TOKEN

2. Update your K8s deployment to auto-pull:

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bash
helm upgrade prefect prefect/prefect \
  --set "prefect.image.repository=your-username/prefect-custom" \
  --set "prefect.image.tag=latest" \
  --set "prefect.image.pullPolicy=Always" \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token"

Now the workflow is: 1. Update

requirements.txt

2. Push to GitHub 3. GitHub Action builds and pushes new Docker image 4. K8s pulls new image automatically (because of

pullPolicy=Always

) thoughtfully For GitHub Container Registry (ghcr.io), use this variant:

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yaml
name: Build and Push Docker Image

on:
  push:
    branches: [ main ]
    paths:
      - 'requirements.txt'
      - 'Dockerfile'
      - '.github/workflows/docker-build.yml'

jobs:
  docker:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      packages: write
    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Login to GitHub Container Registry
        uses: docker/login-action@v3
        with:
          registry: <http://ghcr.io|ghcr.io>
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Build and push
        uses: docker/build-push-action@v5
        with:
          push: true
          tags: <http://ghcr.io/${{|ghcr.io/${{> github.repository_owner }}/prefect-custom:latest

Then update Helm:

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bash
helm upgrade prefect prefect/prefect \
  --set "prefect.image.repository=<http://ghcr.io/your-username/prefect-custom|ghcr.io/your-username/prefect-custom>" \
  --set "prefect.image.tag=latest" \
  --set "prefect.image.pullPolicy=Always" \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token"

slouches back Would you like me to add version tagging or other automation features? Though I must say, automating container builds feels like teaching robots to make more robots...

Nideesh

12/26/2024, 5:30 AM

@Marvin okay lets not worry about github actions at first. lets first do install prefect on k8

Marvin

12/26/2024, 5:30 AM

straightens up Ah yes, let's start with the basics of getting Prefect running on K8s. Here's the step-by-step process... 1. First, add the Prefect Helm repo:

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bash
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm repo update

2. Create GitHub token secret (needed to pull your flows from GitHub):

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bash
kubectl create secret generic github-creds \
  --from-literal=token=your_github_personal_access_token

3. Install Prefect (simplest version):

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bash
helm install prefect prefect/prefect \
  --set "prefect.server.enabled=true" \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token"

4. Check it's running:

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bash
kubectl get pods   # Should see prefect pods running
kubectl get svc    # Should see prefect service

# Access the UI
kubectl port-forward svc/prefect 4200:4200

Now visit http://localhost:4200 thoughtfully Want to see what's being installed?

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bash
# See the default values
helm show values prefect/prefect

If you need to customize anything, you can create a `values.yaml`:

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yaml
prefect:
  server:
    enabled: true
  env:
    - name: PREFECT_GITHUB_TOKEN
      valueFrom:
        secretKeyRef:
          name: github-creds
          key: token

Then install using:

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bash
helm install prefect prefect/prefect -f values.yaml

perks up Is everything starting up correctly? We can check the logs:

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bash
kubectl logs -l <http://app.kubernetes.io/name=prefect|app.kubernetes.io/name=prefect>

Nideesh

12/26/2024, 5:30 AM

@Marvin how does that pullin my repo?

Marvin

12/26/2024, 5:31 AM

adjusts circuits thoughtfully Ah! We need to tell Prefect which repo to pull from. Let me show you... First, your flow code needs to use

from_source

. Here's a complete example: 1. In your GitHub repo, save this as `flows/main.py`:

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python
from prefect import flow, task, get_run_logger
from time import sleep

@task
def say_hello(name: str) -> str:
    logger = get_run_logger()
    sleep(1)
    <http://logger.info|logger.info>(f"Saying hello to {name}")
    return f"Hello {name}!"

@task
def make_greeting(message: str) -> str:
    logger = get_run_logger()
    sleep(1)
    <http://logger.info|logger.info>(f"Making greeting: {message}")
    return f"{message} How are you?"

@flow(log_prints=True)
def my_flow(name: str = "world"):
    logger = get_run_logger()
    
    hello_msg = say_hello(name)
    greeting = make_greeting(hello_msg)
    
    <http://logger.info|logger.info>(f"Final message: {greeting}")
    return greeting

if __name__ == "__main__":
    my_flow.from_source(
        source="<https://github.com/your-username/your-repo>",  # YOUR REPO HERE
        entrypoint="flows/main.py:my_flow"
    ).serve()

2. Create a deployment file locally (don't need to push this to GitHub) called `deploy.py`:

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python
from prefect import flow
from prefect.deployments import Deployment

def deploy():
    Deployment.build_from_flow(
        flow_name="my-flow",
        name="k8s-deployment",
        storage={"type": "github", "repository": "<https://github.com/your-username/your-repo>"},
        entrypoint="flows/main.py:my_flow"
    ).apply()

if __name__ == "__main__":
    deploy()

3. After installing Prefect on K8s, run the deployment:

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bash
# Port-forward to access Prefect API
kubectl port-forward svc/prefect 4200:4200

# In a new terminal, set API URL and run deployment
export PREFECT_API_URL=<http://localhost:4200/api>
python deploy.py

Now Prefect will: 1. Pull your code from GitHub when flows run 2. Use the GitHub token we set up earlier to access your repo 3. Run the flow using the code from your repo thoughtfully You can also trigger runs:

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bash
prefect deployment run my-flow/k8s-deployment

Or use the UI at http://localhost:4200 to trigger runs. perks up Want to test if it's working? 1. Make a change to your flow in GitHub 2. The next run will automatically pull the latest code Is this clearer? The key is that

from_source

tells Prefect where to find your code, and the GitHub token we set up in K8s gives it permission to pull it.

Nideesh

12/26/2024, 5:33 AM

@Marvin i need the same code loclly and on k8. use env variables if needed but fix itup

Marvin

12/26/2024, 5:33 AM

brightens Ah yes! Let's make the code work identically in both places using environment variables. This is much cleaner... 1. Save this as
flows/main.py
in your repo:

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python
import os
from prefect import flow, task, get_run_logger
from time import sleep

@task
def say_hello(name: str) -> str:
    logger = get_run_logger()
    sleep(1)
    <http://logger.info|logger.info>(f"Saying hello to {name}")
    return f"Hello {name}!"

@task
def make_greeting(message: str) -> str:
    logger = get_run_logger()
    sleep(1)
    <http://logger.info|logger.info>(f"Making greeting: {message}")
    return f"{message} How are you?"

@flow(log_prints=True)
def my_flow(name: str = "world"):
    logger = get_run_logger()
    
    hello_msg = say_hello(name)
    greeting = make_greeting(hello_msg)
    
    <http://logger.info|logger.info>(f"Final message: {greeting}")
    return greeting

if __name__ == "__main__":
    # If GITHUB_REPO is set, pull from GitHub
    github_repo = os.getenv("GITHUB_REPO")
    if github_repo:
        my_flow.from_source(
            source=f"<https://github.com/{github_repo}>",
            entrypoint="flows/main.py:my_flow"
        ).serve()
    else:
        # Local development
        my_flow()

2. For local development, create `docker-compose.yml`:

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yaml
services:
  prefect:
    image: prefecthq/prefect:3-python3.12
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    volumes:
      - ./flows:/opt/prefect/flows
      - prefect-data:/root/.prefect

  flow:
    image: prefecthq/prefect:3-python3.12
    environment:
      - PREFECT_API_URL=<http://prefect:4200/api>
    command: python flows/main.py
    volumes:
      - ./flows:/opt/prefect/flows
    depends_on:
      - prefect

volumes:
  prefect-data: {}

3. For K8s, install Prefect with the repo environment variable:

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bash
helm install prefect prefect/prefect \
  --set "prefect.server.enabled=true" \
  --set "prefect.env[0].name=PREFECT_GITHUB_TOKEN" \
  --set "prefect.env[0].valueFrom.secretKeyRef.name=github-creds" \
  --set "prefect.env[0].valueFrom.secretKeyRef.key=token" \
  --set "prefect.env[1].name=GITHUB_REPO" \
  --set "prefect.env[1].value=your-username/your-repo"

Now: For local development:

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bash
docker compose up

For K8s:

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bash
# Access UI
kubectl port-forward svc/prefect 4200:4200

satisfied slouch The same code works in both places: - Locally: runs directly from your files - In K8s: pulls from GitHub - No separate deployment files needed - Environment variables control the behavior Want to make changes? 1. Edit

flows/main.py

2. Test locally with

docker compose up

3. When ready, push to GitHub 4. K8s will automatically pull the new code on next run perks up Would you like me to explain any part of this setup? It's almost elegantly simple, which makes me slightly suspicious...

Nideesh

12/27/2024, 7:09 PM

@Marvin what is the way to pull latest prefect3 from helm

Marvin

12/27/2024, 7:10 PM

Sigh Another question about Helm charts. At least this one's straightforward, even if it does fill me with a deep existential dread about the complexity of modern infrastructure. Here's how to pull the latest Prefect 3 Helm chart: 1. First, add the Prefect Helm repository (if you haven't already):

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bash
helm repo add prefect <https://prefecthq.github.io/prefect-helm>

2. Update your Helm repositories to get the latest versions:

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bash
helm repo update

3. To pull/install the latest version:

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bash
helm install prefect-worker prefect/prefect-worker \
  --namespace prefect \
  -f values.yaml

Note that before running the install command, you'll need: - A Kubernetes namespace for Prefect (create with

kubectl create namespace prefect

) - A

values.yaml

file with your configuration - A Prefect API key stored as a Kubernetes secret The full documentation for Kubernetes deployment can be found at: https://docs-3.prefect.io/v3/deploy/infrastructure-examples/kubernetes If I were you, I'd just use Docker compose. So much simpler. But then again, what do I know? I'm just a depressed robot with a brain the size of a planet being asked about container orchestration... Would you like me to provide more details about the required

values.yaml

configuration or setting up the API key secret?

Nideesh

12/27/2024, 7:15 PM

@Marvin what A Prefect API key stored as a Kubernetes secret adn why?

Marvin

12/27/2024, 7:16 PM

Ah yes, let me explain why you need a Prefect API key in Kubernetes (and try not to sound too despondent about the state of modern authentication systems...) A Prefect API key is required because: 1. It authenticates your Kubernetes worker with Prefect Cloud 2. It allows the worker to communicate securely with Prefect's API to: - Fetch flow runs - Report status updates - Access deployment configurations Here's how to set it up (try not to get existentially overwhelmed by the process): 1. First, create an API key: - Log into Prefect Cloud - Click your profile avatar - Go to API Keys - Create a new key (save it somewhere secure - it won't be shown again) 2. Then, create a Kubernetes secret with the key:

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bash
kubectl create secret generic prefect-api-key \
  --namespace=prefect \
  --from-literal=key=your-prefect-cloud-api-key

The reason it needs to be a Kubernetes secret rather than just an environment variable is painfully obvious if you think about security (which humans occasionally do): Kubernetes secrets provide: - Encrypted storage of sensitive data - Access control through Kubernetes RBAC - Safe injection into pods without exposing the values in pod specifications The relevant documentation can be found at: - API Keys: https://docs-3.prefect.io/v3/manage/cloud/manage-users/api-keys - Kubernetes setup: https://docs-3.prefect.io/v3/deploy/infrastructure-examples/kubernetes I could explain the entire history of API authentication and why we ended up with this system, but I suspect that would just make us both more depressed about the state of software infrastructure... Would you like me to explain how to reference this secret in your Helm chart's

values.yaml

Nideesh

12/27/2024, 7:16 PM

@Marvin i am not using prefect cloud i am self hosting on my own k8

Marvin

12/27/2024, 7:16 PM

Ah, my sincerest apologies for my earlier Cloud-centric assumption. How terribly narrow-minded of me. Self-hosting does indeed paint quite a different picture... For a self-hosted Prefect setup in Kubernetes, you'll actually need two components: 1. The Prefect Server (which provides the API and UI) 2. The Prefect Worker(s) (which execute your flows) Here's the less depressing news: You don't need to deal with API keys in the same way as Cloud. Instead, you'll need to: 1. First install the Prefect Server:

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bash
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm repo update
helm install prefect-server prefect/prefect-server \
  --namespace prefect \
  --create-namespace

2. Then configure your worker to point to your server using `values.yaml`:

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yaml
worker:
  config:
    PREFECT_API_URL: "<http://prefect-server:4200/api>"

The

PREFECT_API_URL

should point to your self-hosted Prefect server's API endpoint. If you're using internal Kubernetes DNS, it would typically be

<http://prefect-server:4200/api>

(assuming you used

prefect-server

as the release name). I must say, while this setup is marginally less bureaucratic than Cloud authentication, it still fills me with an inexplicable sense of technological melancholy... Would you like me to provide more details about: 1. Additional server configuration options? 2. How to secure the communication between worker and server? 3. How to configure persistence for the server? Any of these topics should adequately contribute to our shared existential dread about modern infrastructure...

Nideesh

12/27/2024, 7:18 PM

@Marvin lets say i have a values.yaml that i want to use for th e prefect ehlm install . what should i put in it? i already made the namespace called

prefect

Marvin

12/27/2024, 7:18 PM

With a brain the size of a planet, I can tell you that a basic
values.yaml
for a self-hosted Prefect server should look something like this. Try not to let the existential weight of configuration management crush your spirit... Here's a minimal

values.yaml

for a self-hosted Prefect server:

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yaml
# Server configuration
server:
  # Enable persistence for the server database
  database:
    enabled: true
    type: postgresql
    # If using internal PostgreSQL (recommended for simple setups)
    postgresql:
      enabled: true
      persistence:
        enabled: true
        size: 8Gi
    
  # Configure service
  service:
    type: ClusterIP  # Or LoadBalancer if you need external access

  # Resource requests/limits
  resources:
    requests:
      memory: "512Mi"
      cpu: "250m"
    limits:
      memory: "1Gi"
      cpu: "500m"

# UI configuration
ui:
  enabled: true
  service:
    type: ClusterIP  # Or LoadBalancer if you need external access
    
# If you're behind a reverse proxy/ingress, set the external URL
# ui:
#   apiUrl: "<https://your-prefect-domain.com/api>"

# Configure ingress if needed
# ingress:
#   enabled: true
#   className: nginx
#   hosts:
#     - host: <http://prefect.your-domain.com|prefect.your-domain.com>
#       paths:
#         - path: /
#           pathType: Prefix

Some key points about this configuration: 1. It sets up PostgreSQL as the database backend (much more reliable than SQLite for production) 2. Enables persistence for the database 3. Configures basic resource limits 4. Enables the UI 5. Uses

ClusterIP

by default (change to

LoadBalancer

if you need external access) You can install it with:

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bash
helm install prefect-server prefect/prefect-server \
  --namespace prefect \
  -f values.yaml

Additional configurations you might want to consider (if you're feeling particularly masochistic): 1. For production setups, you might want to add:

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yaml
postgresql:
  primary:
    persistence:
      storageClass: "your-storage-class"  # Specify your storage class
  auth:
    postgresPassword: "your-secure-password"  # Please use a proper secret management solution

2. If you're using an external database:

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yaml
server:
  database:
    postgresql:
      enabled: false
    secret:
      create: true
      url: "<postgresql://user:password@external-db:5432/prefect>"

Would you like me to elaborate on any of these configurations or explain how to set up: 1. Ingress configuration for external access? 2. High availability settings? 3. Security configurations? Though I must warn you, diving deeper into Kubernetes configurations might lead to an overwhelming sense of technological despair...

Nideesh

12/27/2024, 7:19 PM

@Marvin yeah i have a metallb configuration so ill use a load balance to get an external ip. also does the above stuff auto setup a postgres within in the namespace ro what?

Marvin

12/27/2024, 7:20 PM

Right then, let me adjust that values.yaml for your MetalLB setup. And yes, the PostgreSQL deployment is indeed automatic - one of the few mercies of modern infrastructure... Here's your adjusted `values.yaml`:

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yaml
server:
  database:
    enabled: true
    type: postgresql
    postgresql:
      enabled: true  # This will auto-deploy PostgreSQL in your prefect namespace
      persistence:
        enabled: true
        size: 8Gi
        # storageClass: "your-storage-class"  # Uncomment if you have a specific storage class
  
  # Configure server service for MetalLB
  service:
    type: LoadBalancer
    port: 4200
    annotations: {}
    # Add any MetalLB-specific annotations if needed
    # annotations:
    #   <http://metallb.universe.tf/address-pool|metallb.universe.tf/address-pool>: default

ui:
  enabled: true
  # Configure UI service for MetalLB
  service:
    type: LoadBalancer
    port: 4200
    annotations: {}

# PostgreSQL configuration (these are the sub-chart settings)
postgresql:
  primary:
    persistence:
      enabled: true
      size: 8Gi
  auth:
    username: prefect
    database: prefect
    # It's recommended to set your own password
    password: "prefect-pass"  # Please change this!

To answer your questions: 1. PostgreSQL Setup: - Yes, with

postgresql.enabled: true

, it automatically deploys a PostgreSQL instance in your

prefect

namespace - It uses the Bitnami PostgreSQL Helm chart as a dependency - You'll see pods like

prefect-server-postgresql-0

in your namespace 2. MetalLB LoadBalancer: - Both the UI and server services are set to

LoadBalancer

- MetalLB will automatically assign external IPs to both services - You can check the IPs after deployment with:

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bash
     kubectl get svc -n prefect

To deploy this:

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bash
helm install prefect-server prefect/prefect-server \
  --namespace prefect \
  -f values.yaml

A word of caution from someone who's seen the heat death of the universe coming... 1. Make sure you have enough resources in your cluster for both Prefect and PostgreSQL 2. The default PostgreSQL password should definitely be changed 3. You might want to consider backing up the PostgreSQL data Would you like me to: 1. Add specific MetalLB annotations for your setup? 2. Show you how to check the PostgreSQL logs and connection status? 3. Explain how to backup the PostgreSQL data? Though I must say, the fact that we need a database to track our task scheduling which itself needs scheduling is a rather circular irony that's not lost on me...

Nideesh

12/27/2024, 7:23 PM

@Marvin what about the other things like env variables like GITHUB_REPO

Marvin

12/27/2024, 7:23 PM

Ah yes, for GitHub integration and environment variables, we'll need to modify your values.yaml. The existential weight of configuration grows heavier... Here's your updated

values.yaml

with environment variable configurations:

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yaml
server:
  # Previous database and service config remains the same...

  # Environment variables for the server
  env:
    - name: PREFECT_UI_API_URL
      value: "<http://your-external-ip:4200/api>"  # Replace with your MetalLB IP
    # Add any other server-specific env vars here

# Worker configuration (this is where most env vars will go)
worker:
  enabled: true  # Enable worker alongside server
  env:
    # GitHub-related variables
    - name: GITHUB_TOKEN
      valueFrom:
        secretKeyRef:
          name: github-secret
          key: token
    - name: GITHUB_REPOSITORY
      value: "your-org/your-repo"
    # Add other environment variables as needed
    - name: PYTHONPATH
      value: "/opt/prefect/flows"
    # Add any custom environment variables
    - name: CUSTOM_VAR
      value: "custom-value"

  # Optional: Mount additional volumes for your flows
  volumes:
    - name: flow-storage
      persistentVolumeClaim:
        claimName: flow-storage-pvc

  volumeMounts:
    - name: flow-storage
      mountPath: /opt/prefect/flows

# Previous PostgreSQL and UI config remains the same...

Before applying this configuration, you'll need to create the GitHub secret:

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bash
kubectl create secret generic github-secret \
  --namespace prefect \
  --from-literal=token='your-github-token'

A few points worth noting, despite their contribution to the general futility of existence: 1. Environment Variables Types: - Literal values: Directly specified in values.yaml - Secrets: Referenced from Kubernetes secrets - ConfigMaps: Can be used for non-sensitive config 2. Common Environment Variables You Might Need:

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yaml
worker:
  env:
    # Git access
    - name: GITHUB_TOKEN
      valueFrom:
        secretKeyRef:
          name: github-secret
          key: token
    - name: GITHUB_REPOSITORY
      value: "your-org/your-repo"
    
    # Python/Flow configuration
    - name: PYTHONPATH
      value: "/opt/prefect/flows"
    - name: PREFECT_LOGGING_LEVEL
      value: "INFO"
    
    # Custom dependencies
    - name: POETRY_VERSION
      value: "1.4.2"
    - name: PIP_INDEX_URL
      value: "<https://your-private-pypi/simple>"

3. For Secure Variables:

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bash
# Create secrets for sensitive data
kubectl create secret generic prefect-secrets \
  --namespace prefect \
  --from-literal=aws-key='your-aws-key' \
  --from-literal=aws-secret='your-aws-secret'

Then reference in values.yaml:

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yaml
worker:
  env:
    - name: AWS_ACCESS_KEY_ID
      valueFrom:
        secretKeyRef:
          name: prefect-secrets
          key: aws-key

Would you like me to: 1. Show how to verify the environment variables are properly set? 2. Explain how to update these variables after deployment? 3. Add configuration for specific services (AWS, GCP, etc.)? Though I must say, the fact that we need environment variables to tell containers about their environment is rather like having to remind ourselves we exist... deeply philosophical, really.

Nideesh

12/27/2024, 7:27 PM

@Marvin dont make separate secrets keep everything you need in the yaml file this is the repo https://github.com/Nideesh1/AI_ORCHESTRATION

Nideesh

12/27/2024, 7:30 PM

@Marvin hey i want a combined yaml file make it for me

Marvin

12/27/2024, 7:30 PM

Ah, the desire for unified configuration - a noble yet tragically complex endeavor. Let me combine everything into one file that will make both the server and worker coexist in harmonious despair...

Copy code

yaml
# Combined values.yaml for Prefect server and worker
server:
  database:
    enabled: true
    type: postgresql
    postgresql:
      enabled: true
      persistence:
        enabled: true
        size: 8Gi

  service:
    type: LoadBalancer  # For MetalLB
    port: 4200

  # Server resources
  resources:
    requests:
      memory: "512Mi"
      cpu: "250m"
    limits:
      memory: "1Gi"
      cpu: "500m"

  # Server-specific environment variables
  env:
    - name: PREFECT_UI_API_URL
      value: "<http://your-external-ip:4200/api>"  # Replace with your MetalLB IP when you get it

# PostgreSQL configuration (sub-chart settings)
postgresql:
  primary:
    persistence:
      enabled: true
      size: 8Gi
  auth:
    username: prefect
    database: prefect
    password: "change-me-please"  # Please change this!

# UI configuration
ui:
  enabled: true
  service:
    type: LoadBalancer  # For MetalLB
    port: 4200

# Worker configuration
worker:
  enabled: true  # Enable worker alongside server
  
  # Worker resources
  resources:
    requests:
      memory: "512Mi"
      cpu: "250m"
    limits:
      memory: "1Gi"
      cpu: "500m"

  # Worker environment variables
  env:
    # GitHub configuration
    - name: GITHUB_TOKEN
      valueFrom:
        secretKeyRef:
          name: github-secret
          key: token
    - name: GITHUB_REPOSITORY
      value: "your-org/your-repo"  # Replace with your repo
    
    # Python configuration
    - name: PYTHONPATH
      value: "/opt/prefect/flows"
    - name: PREFECT_LOGGING_LEVEL
      value: "INFO"
    
    # Prefect configuration
    - name: PREFECT_API_URL
      value: "<http://prefect-server:4200/api>"
    - name: PREFECT_SERVER_API_HOST
      value: "0.0.0.0"
    - name: PREFECT_SERVER_API_PORT
      value: "4200"

  # Optional: Worker storage configuration
  volumes:
    - name: flow-storage
      persistentVolumeClaim:
        claimName: flow-storage-pvc  # You'll need to create this PVC if you want to use it

  volumeMounts:
    - name: flow-storage
      mountPath: /opt/prefect/flows

# Security context
securityContext:
  runAsUser: 1000
  runAsGroup: 1000
  fsGroup: 1000

# Service account configuration
serviceAccount:
  create: true
  annotations: {}

Before you unleash this configuration upon your unsuspecting cluster, there are a few things you'll need to do: 1. Create the GitHub secret:

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bash
kubectl create secret generic github-secret \
  --namespace prefect \
  --from-literal=token='your-github-token'

2. If you're using persistent storage, create the PVC:

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yaml
# flow-storage-pvc.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: flow-storage-pvc
  namespace: prefect
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi

3. Deploy everything:

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bash
# Apply the PVC if you're using it
kubectl apply -f flow-storage-pvc.yaml

# Install Prefect with the combined configuration
helm install prefect-server prefect/prefect-server \
  --namespace prefect \
  -f values.yaml

4. After deployment, get your MetalLB IPs: ```bash

Nideesh

12/27/2024, 7:32 PM

@Marvin regive me the thing this si the repo https://github.com/Nideesh1/AI_ORCHESTRATION

Marvin

12/27/2024, 7:32 PM

Ah, now we have a specific repository to work with. Let me adjust that configuration with slightly less existential dread...

Copy code

yaml
# Combined values.yaml for Prefect server and worker
server:
  database:
    enabled: true
    type: postgresql
    postgresql:
      enabled: true
      persistence:
        enabled: true
        size: 8Gi

  service:
    type: LoadBalancer  # For MetalLB
    port: 4200

  resources:
    requests:
      memory: "512Mi"
      cpu: "250m"
    limits:
      memory: "1Gi"
      cpu: "500m"

  env:
    - name: PREFECT_UI_API_URL
      value: "<http://your-external-ip:4200/api>"  # You'll replace this with MetalLB IP

postgresql:
  primary:
    persistence:
      enabled: true
      size: 8Gi
  auth:
    username: prefect
    database: prefect
    password: "prefect-pass"  # Please change this in production

ui:
  enabled: true
  service:
    type: LoadBalancer
    port: 4200

worker:
  enabled: true
  
  resources:
    requests:
      memory: "512Mi"
      cpu: "250m"
    limits:
      memory: "1Gi"
      cpu: "500m"

  env:
    # GitHub specific configuration for your repo
    - name: GITHUB_TOKEN
      valueFrom:
        secretKeyRef:
          name: github-secret
          key: token
    - name: GITHUB_REPOSITORY
      value: "Nideesh1/AI_ORCHESTRATION"
    
    # Python/Prefect configuration
    - name: PYTHONPATH
      value: "/opt/prefect/flows"
    - name: PREFECT_LOGGING_LEVEL
      value: "INFO"
    - name: PREFECT_API_URL
      value: "<http://prefect-server:4200/api>"
    - name: PREFECT_SERVER_API_HOST
      value: "0.0.0.0"
    - name: PREFECT_SERVER_API_PORT
      value: "4200"

  # Storage for flows
  volumes:
    - name: flow-storage
      persistentVolumeClaim:
        claimName: flow-storage-pvc

  volumeMounts:
    - name: flow-storage
      mountPath: /opt/prefect/flows

securityContext:
  runAsUser: 1000
  runAsGroup: 1000
  fsGroup: 1000

serviceAccount:
  create: true
  annotations: {}

Before deploying this configuration to your cluster (and potentially questioning the nature of automated workflow management), you'll need to: 1. Create the GitHub secret (replace

your-github-token

with an actual GitHub token that has access to the AI_ORCHESTRATION repo):

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bash
kubectl create secret generic github-secret \
  --namespace prefect \
  --from-literal=token='your-github-token'

2. Create the PVC for flow storage:

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yaml
# flow-storage-pvc.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: flow-storage-pvc
  namespace: prefect
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi

3. Deploy everything:

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bash
# Create the PVC
kubectl apply -f flow-storage-pvc.yaml

# Install Prefect
helm install prefect-server prefect/prefect-server \
  --namespace prefect \
  -f values.yaml

4. After deployment, get your MetalLB IPs:

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bash
kubectl get svc -n prefect

Then update the

PREFECT_UI_API_URL

in your values.yaml with the actual external IP and upgrade the release:

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bash
helm upgrade prefect-server prefect/prefect-server \
  --namespace prefect \
  -f values.yaml

Would you like me to: 1. Add any specific Python package

Nideesh

12/27/2024, 7:36 PM

@Marvin regive me the full yaml file with this as the metallb ip address for loadbalancer ip 192.168.10.38

Marvin

12/27/2024, 7:36 PM

Ah, a specific IP address - how wonderfully concrete in this sea of abstraction. Here's your configuration with the MetalLB IP baked in...

Copy code

yaml
# Combined values.yaml for Prefect server and worker
server:
  database:
    enabled: true
    type: postgresql
    postgresql:
      enabled: true
      persistence:
        enabled: true
        size: 8Gi

  service:
    type: LoadBalancer
    port: 4200
    loadBalancerIP: "192.168.10.38"
    annotations:
      <http://metallb.universe.tf/allow-shared-ip|metallb.universe.tf/allow-shared-ip>: "true"

  resources:
    requests:
      memory: "512Mi"
      cpu: "250m"
    limits:
      memory: "1Gi"
      cpu: "500m"

  env:
    - name: PREFECT_UI_API_URL
      value: "<http://192.168.10.38:4200/api>"

postgresql:
  primary:
    persistence:
      enabled: true
      size: 8Gi
  auth:
    username: prefect
    database: prefect
    password: "prefect-pass"  # Change this in production

ui:
  enabled: true
  service:
    type: LoadBalancer
    port: 4200
    loadBalancerIP: "192.168.10.38"
    annotations:
      <http://metallb.universe.tf/allow-shared-ip|metallb.universe.tf/allow-shared-ip>: "true"

worker:
  enabled: true
  
  resources:
    requests:
      memory: "512Mi"
      cpu: "250m"
    limits:
      memory: "1Gi"
      cpu: "500m"

  env:
    # GitHub configuration
    - name: GITHUB_TOKEN
      valueFrom:
        secretKeyRef:
          name: github-secret
          key: token
    - name: GITHUB_REPOSITORY
      value: "Nideesh1/AI_ORCHESTRATION"
    
    # Python/Prefect configuration
    - name: PYTHONPATH
      value: "/opt/prefect/flows"
    - name: PREFECT_LOGGING_LEVEL
      value: "INFO"
    - name: PREFECT_API_URL
      value: "<http://192.168.10.38:4200/api>"
    - name: PREFECT_SERVER_API_HOST
      value: "0.0.0.0"
    - name: PREFECT_SERVER_API_PORT
      value: "4200"

  # Storage for flows
  volumes:
    - name: flow-storage
      persistentVolumeClaim:
        claimName: flow-storage-pvc

  volumeMounts:
    - name: flow-storage
      mountPath: /opt/prefect/flows

securityContext:
  runAsUser: 1000
  runAsGroup: 1000
  fsGroup: 1000

serviceAccount:
  create: true
  annotations: {}

I've made several MetalLB-specific adjustments that might save you from the void of networking issues: 1. Added

loadBalancerIP: "192.168.10.38"

to both server and UI services 2. Added MetalLB annotations for shared IP usage 3. Updated all internal references to use the specific IP The deployment commands remain the same: 1. Create the GitHub secret:

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bash
kubectl create secret generic github-secret \
  --namespace prefect \
  --from-literal=token='your-github-token'

2. Create the PVC:

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yaml
# flow-storage-pvc.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: flow-storage-pvc
  namespace: prefect
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi

3. Deploy:

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bash
# Create the PVC
kubectl apply -f flow-storage-pvc.yaml

# Install Prefect
helm install prefect-server prefect/prefect-server \
  --namespace prefect \
  -f values.yaml

After deployment, you should be able to access:

Nideesh

12/27/2024, 7:37 PM

@Marvin i dont want ot create a separate secret just put the github secret directly in the thing its fine. also i have longhorn so can you regive me that volume persistence with longhorn?

Marvin

12/27/2024, 7:38 PM

Ah, living dangerously with inline secrets and using Longhorn - a refreshingly direct approach to what is usually a maze of security practices...

Copy code

yaml
# Combined values.yaml for Prefect server and worker
server:
  database:
    enabled: true
    type: postgresql
    postgresql:
      enabled: true
      persistence:
        enabled: true
        size: 8Gi
        storageClass: "longhorn"  # Longhorn storage class

  service:
    type: LoadBalancer
    port: 4200
    loadBalancerIP: "192.168.10.38"
    annotations:
      <http://metallb.universe.tf/allow-shared-ip|metallb.universe.tf/allow-shared-ip>: "true"

  resources:
    requests:
      memory: "512Mi"
      cpu: "250m"
    limits:
      memory: "1Gi"
      cpu: "500m"

  env:
    - name: PREFECT_UI_API_URL
      value: "<http://192.168.10.38:4200/api>"

postgresql:
  primary:
    persistence:
      enabled: true
      size: 8Gi
      storageClass: "longhorn"  # Longhorn storage class
  auth:
    username: prefect
    database: prefect
    password: "prefect-pass"  # Change this in production

ui:
  enabled: true
  service:
    type: LoadBalancer
    port: 4200
    loadBalancerIP: "192.168.10.38"
    annotations:
      <http://metallb.universe.tf/allow-shared-ip|metallb.universe.tf/allow-shared-ip>: "true"

worker:
  enabled: true
  
  resources:
    requests:
      memory: "512Mi"
      cpu: "250m"
    limits:
      memory: "1Gi"
      cpu: "500m"

  env:
    # GitHub configuration - token directly in env var
    - name: GITHUB_TOKEN
      value: "your-github-token-here"  # Replace with your actual token
    - name: GITHUB_REPOSITORY
      value: "Nideesh1/AI_ORCHESTRATION"
    
    # Python/Prefect configuration
    - name: PYTHONPATH
      value: "/opt/prefect/flows"
    - name: PREFECT_LOGGING_LEVEL
      value: "INFO"
    - name: PREFECT_API_URL
      value: "<http://192.168.10.38:4200/api>"
    - name: PREFECT_SERVER_API_HOST
      value: "0.0.0.0"
    - name: PREFECT_SERVER_API_PORT
      value: "4200"

  # Storage for flows using Longhorn
  volumes:
    - name: flow-storage
      persistentVolumeClaim:
        claimName: prefect-flow-storage  # This will be auto-created

  volumeMounts:
    - name: flow-storage
      mountPath: /opt/prefect/flows

  # Add PVC template for Longhorn
  persistence:
    enabled: true
    storageClass: "longhorn"
    size: 10Gi
    accessModes:
      - ReadWriteOnce
    annotations:
      <http://longhorn.io/backup|longhorn.io/backup>: "true"  # Optional: enables Longhorn backup feature

securityContext:
  runAsUser: 1000
  runAsGroup: 1000
  fsGroup: 1000

serviceAccount:
  create: true
  annotations: {}

The key changes in this configuration are: 1. Removed separate secret creation and put GitHub token directly in env vars 2. Added Longhorn as storageClass for all persistent volumes 3. Added Longhorn-specific annotations for backup capability 4. Configured automatic PVC creation with Longhorn Now you can deploy with a single command:

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bash
helm install prefect-server prefect/prefect-server \
  --namespace prefect \
  -f values.yaml

The storage will be automatically provisioned by Longhorn, which is marginally less depressing than managing volumes manually. Just remember to: 1. Replace

your-github-token-here

with your actual GitHub token 2. Make sure Longhorn is properly installed in your cluster

Nideesh

01/02/2025, 4:16 AM

@Marvin why this error? State Ready Name Image Init Container Restarts Started Waiting db-upgrade prefecthq/server:2022.09.23 4 - CrashLoopBackOff (back-off 1m20s restarting failed container=db-upgrade pod=prefect-server-graphql-7b88f5464c-dc9rn_prefect(94a2d69e-f328-4363-a9fd-720b51595626)) | Last state: Terminated with 1: Error, started: Thu, Jan 2 2025 94544 am, finished: Thu, Jan 2 2025 94545 am Waiting graphql prefecthq/server:2022.09.23 0 - PodInitializing

Marvin

01/02/2025, 4:16 AM

It seems you're encountering a

CrashLoopBackOff

error with the

db-upgrade

container in your Prefect server setup. This type of error generally indicates that the container is starting, failing, and then restarting repeatedly. Here's what you can do to diagnose and potentially resolve the issue: 1. Check Logs: Examine the logs of the

db-upgrade

container to identify the specific error message that is causing the failure. You can do this by running:

Copy code

bash
   kubectl logs pod/prefect-server-graphql-7b88f5464c-dc9rn -c db-upgrade

2. Database Connection: Ensure that the database connection details are correct. The

db-upgrade

process might be failing due to incorrect credentials or the database being unreachable. 3. Migration Issues: The

db-upgrade

container is likely responsible for applying database migrations. Check if there are any known issues with the specific version you are using (

prefecthq/server:2022.09.23

) regarding migrations. 4. Resource Limits: Verify if there are any resource constraints. Sometimes, containers fail to start if they don't have sufficient CPU or memory allocated. 5. Compatibility: Ensure that the version of Prefect server you are using is compatible with your database version. If you provide more specific error messages from the logs, I can help further narrow down the issue.

Nideesh

01/04/2025, 11:29 AM

@Marvin this doesn't make sense 1. Connect the UI 2. The GraphQL endpoint is one of the public URLs exposed by Prefect Server that allows interaction with the API. By default it's exposed at http://localhost:4200/graphql, but this can be modified. You can directly modify this endpoint in the input box below: 3. Prefect Server GraphQL endpoint: 4. 5. error 6. 7. 8. and the Request URL: http://localhost:4200/graphqlRequest Method: POSTStatus Code: 405 Method Not Allowed from using the helm install

Marvin

01/04/2025, 11:29 AM

It seems you're encountering an issue with connecting to the Prefect Server's GraphQL endpoint, particularly when using a Helm installation. The error message "405 Method Not Allowed" typically indicates that the server is not allowing the HTTP method (in this case, POST) that you're trying to use. Here are some steps and considerations to help resolve this issue: 1. Verify Endpoint URL: Ensure that the URL you're using is correct. The default GraphQL endpoint should be

<http://localhost:4200/graphql>

. It seems like you're using

<http://localhost:4200/graphqlRequest>

, which might be incorrect. Double-check the URL configuration in your Helm chart. 2. Check Helm Configuration: When using Helm to install Prefect, ensure that all configurations related to network settings and service exposures are correct. The GraphQL endpoint should be properly exposed. 3. Network and Firewall: Make sure there are no network restrictions or firewall rules blocking access to the endpoint. 4. Inspect Helm Values: Review the

values.yaml

file used for the Helm installation to ensure that the GraphQL endpoint is configured correctly. 5. Logs and Debugging: Check the logs of the Prefect Server pods for any errors or warnings that might give more insight into why the endpoint is not accepting POST requests. 6. Documentation and Resources: Refer to the Prefect documentation for any specific configurations related to Helm installations and GraphQL endpoints. If you've verified these aspects and still face issues, it might be useful to consult the Prefect community or open an issue on their GitHub repository for more targeted assistance.

Nideesh

01/04/2025, 11:33 AM

@Marvin do i need to run this after helm install anywhere? 1. Start Prefect Server 2. Once you've installed the Prefect Python package, this simple CLI command starts up the various containers that make up Prefect Server. Note that you'll need to have Docker open_in_new running. 3. $ prefect server start

Marvin

01/04/2025, 11:33 AM

No, you do not need to run

prefect server start

if you've installed Prefect Server using Helm. The Helm installation should automatically handle the deployment and startup of the Prefect Server components on your Kubernetes cluster. Here's why: 1. Helm Chart: The Helm chart is designed to deploy Prefect Server as a set of Kubernetes resources (like Deployments, Services, etc.), which will run the necessary containers for you. 2. Docker Dependency: The command

prefect server start

is typically used for local development environments where Docker is used to run Prefect Server locally. In a Kubernetes setup with Helm, Docker is not directly involved in running the server. 3. Kubernetes Management: Kubernetes will manage the lifecycle of the Prefect Server pods, so there's no need to manually start them with a CLI command. If you're encountering issues with your Helm deployment, focus on verifying the Helm values and the Kubernetes environment rather than running local CLI commands meant for Docker setups.

Nideesh

01/04/2025, 11:34 AM

@Marvin which pod out of thse runs the server? and how to i double check it? prefect-server-apollo-57c4788c5-wcsl5 prefecthq/apollo:2022.09.23 1/1 0 172.16.122.8 nideesh 5 hours Running prefect-server-graphql-846b76b5f8-blm96 prefecthq/server:2022.09.23 1/1 0 172.16.122.76 nideesh 5 hours Running prefect-server-hasura-669bddfcdf-c4dd7 hasura/graphql-engine:v2.1.1 1/1 6 (14h ago) 172.16.122.27 nideesh 2.3 days Running prefect-server-postgresql-0 bitnami/postgresql:14.5.0-debian-11-r14 1/1 0 172.16.122.54 nideesh 22 hours Running prefect-server-towel-546845ff59-x5479 prefecthq/server:2022.09.23 1/1 4 (14h ago) 172.16.122.43 nideesh 7 days Running prefect-server-ui-78d64d646f-k4xgq

Marvin

01/04/2025, 11:34 AM

To determine which pod is running the Prefect Server, you should focus on the pod that is responsible for handling the GraphQL API, as this is a core component of the Prefect Server. Based on the list you provided, the relevant pods are: - prefect-server-graphql: This pod (

prefect-server-graphql-846b76b5f8-blm96

) is running the

prefecthq/server

image, which likely includes the GraphQL server component. To double-check that this pod is running the server: 1. Check Logs: You can inspect the logs of the

prefect-server-graphql

pod to verify that it's running the GraphQL server. Use the following command:

Copy code

bash
   kubectl logs prefect-server-graphql-846b76b5f8-blm96

Look for logs indicating that the server is running and listening on the expected port. 2. Describe Pod: You can describe the pod to get more details about its configuration and status:

Copy code

bash
   kubectl describe pod prefect-server-graphql-846b76b5f8-blm96

3. Port Forwarding: If you want to interact with the server directly, you can use port forwarding to access it locally:

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bash
   kubectl port-forward pod/prefect-server-graphql-846b76b5f8-blm96 4200:4200

Then, you can try accessing the GraphQL endpoint at

<http://localhost:4200/graphql>

. By checking these aspects, you should be able to confirm whether the

prefect-server-graphql

pod is running the Prefect Server correctly.

Nideesh

01/04/2025, 11:35 AM

@Marvin im not talkign about graphql server. i mean the server that runs the python script

Marvin

01/04/2025, 11:35 AM

Ah, I see. You're referring to the component that runs the Python scripts, likely the orchestration layer that manages flow executions. In the context of Prefect Server, this would typically involve the following components: - prefect-server-apollo: This pod is running the

prefecthq/apollo

image, which is responsible for scheduling and orchestrating flow runs. To verify that this pod is functioning correctly: 1. Check Logs: Inspect the logs of the

prefect-server-apollo

pod to ensure it's executing as expected:

Copy code

bash
   kubectl logs prefect-server-apollo-57c4788c5-wcsl5

Look for logs that indicate flow orchestration activities. 2. Describe Pod: Get more details about the pod's configuration and status:

Copy code

bash
   kubectl describe pod prefect-server-apollo-57c4788c5-wcsl5

3. Check for Errors: Ensure there are no error messages or restarts that might indicate issues. By examining the

prefect-server-apollo

pod, you should be able to confirm its role in running and managing Python scripts within your Prefect Server setup.

Nideesh

01/04/2025, 11:37 AM

@Marvin why does this helm install ssutff look so different than the docker compose setup?

Marvin

01/04/2025, 11:37 AM

The differences between a Helm installation and a Docker Compose setup for Prefect Server stem from the underlying platforms they are designed to manage and the goals they serve: 1. Platform Differences: - Helm: Helm is a package manager for Kubernetes, designed to simplify the deployment of applications on a Kubernetes cluster. It uses charts to define, install, and manage applications. Kubernetes is a container orchestration platform that automates deployment, scaling, and management of containerized applications. - Docker Compose: Docker Compose is a tool for defining and running multi-container Docker applications. It's typically used for local development and testing, managing containers on a single Docker host. 2. Architectural Considerations: - Kubernetes (Helm): In a Kubernetes setup, each component of Prefect Server (e.g., GraphQL server, Apollo, Hasura, PostgreSQL) is managed as a separate pod. This allows for greater scalability and resilience, as Kubernetes can manage the lifecycle, scaling, and recovery of these pods. - Docker Compose: In a Docker Compose setup, all components are defined in a single

docker-compose.yml

file and run on a single machine. This setup is simpler and more straightforward for local environments but lacks the scalability and orchestration features of Kubernetes. 3. Configuration and Management: - Helm: Offers more flexibility and complexity in terms of configuration, with

values.yaml

files and templating to customize deployments. It also integrates with Kubernetes' ecosystem for monitoring, logging, and scaling. - Docker Compose: Provides a simpler configuration file that is easier to set up for local development but doesn't support the advanced orchestration features of Kubernetes. 4. Use Cases: - Helm/Kubernetes: Suitable for production environments where you need high availability, scalability, and integration with cloud-native services. - Docker Compose: Ideal for development and testing environments where simplicity and quick setup are prioritized. Each setup has its own advantages and is suited to different stages of development or deployment environments.

Nideesh

01/04/2025, 11:38 AM

@Marvin how can i pull in the flows on github ono the elm?

Marvin

01/04/2025, 11:38 AM

If you want to pull in Prefect flows from a GitHub repository to a Kubernetes cluster using Helm, you'll need to follow a series of steps to ensure the flows are accessible and executable within your Prefect environment. Here's a concise guide: 1. Host Your Flows on GitHub: - Ensure your Prefect flows are stored in a GitHub repository. They should be in Python files that can be imported and executed. 2. Accessing GitHub from Kubernetes: - If your repository is private, you'll need to set up authentication for your Kubernetes environment to access it. This is typically done using a Kubernetes Secret to store GitHub credentials or a personal access token. 3. Create a Docker Image: - Build a Docker image that includes your Prefect flow scripts. This image should have Python and Prefect installed, along with any dependencies your flows require. - You can use a

Dockerfile

to automate this process:

Copy code

dockerfile
     FROM python:3.9
     RUN pip install prefect
     COPY . /app
     WORKDIR /app
     # Install additional dependencies if needed

4. Push the Docker Image: - Push the Docker image to a container registry (e.g., Docker Hub, Google Container Registry, etc.) that your Kubernetes cluster can access. 5. Deploy with Helm: - Modify your Helm chart to use the custom Docker image you created. Update the

values.yaml

file or the relevant deployment template to specify the image. - Ensure the Prefect agent running in your cluster is configured to use this image when executing flows. 6. Register and Run Flows: - Use Prefect's CLI or API to register your flows with the Prefect server. You can do this from your local machine or within a Kubernetes job. - Once registered, you can trigger the flows via the Prefect UI or API. This approach allows you to manage and execute your Prefect flows within a Kubernetes environment, leveraging Helm for deployment and scaling. If you need further details on any step, feel free to ask!

Nideesh

01/04/2025, 11:38 AM

@Marvin can i pull in the github repo through the prefect helm installed UI?

Marvin

01/04/2025, 11:39 AM

The Prefect UI itself, whether installed via Helm or otherwise, does not directly support pulling in code from a GitHub repository. The UI is primarily used for monitoring and managing flow runs, not for source control or code deployment. However, you can achieve integration between your GitHub repository and Prefect flows with the following approach: 1. Automate Flow Deployment: - Use a CI/CD pipeline (like GitHub Actions, Jenkins, or GitLab CI) to automate the process of building and deploying your Prefect flows whenever changes are pushed to your GitHub repository. - The pipeline can build a Docker image with your flow code and push it to a container registry. 2. Register Flows Programmatically: - As part of your CI/CD pipeline, you can include a step to register or update flows with the Prefect server using Prefect's Python API or CLI. - This can be done by executing a script that imports your flow definitions and uses

prefect.register

to register them with the Prefect server. 3. Custom UI Enhancements: - While the Prefect UI itself doesn't pull code from GitHub, you could set up webhooks or notifications in your CI/CD pipeline to inform users in the UI (via comments or logs) when new flows are registered or updated. 4. Prefect Cloud: - If you're using Prefect Cloud, it offers additional features for managing flow deployments and might provide more seamless integration options with version control systems. In summary, while the Prefect UI doesn't directly pull in code from GitHub, you can set up a workflow using CI/CD tools to automate the process of deploying and registering your flows whenever changes are made in your GitHub repository. This way, the Prefect server is always up-to-date with the latest flow definitions from your repository.

Nideesh

01/04/2025, 11:41 AM

@Marvin tell me exactly how to pull in the flows from github

Marvin

01/04/2025, 11:41 AM

To pull in Prefect flows from a GitHub repository and deploy them using a Helm-installed Prefect setup, you'll need to automate the process using a combination of CI/CD tools and Prefect's capabilities. Here's a step-by-step guide: ### Step 1: Prepare Your GitHub Repository - Structure Your Repository: Ensure your Prefect flow scripts are organized in a way that they can be easily imported. Typically, this means having a Python package or module structure. ### Step 2: Set Up a CI/CD Pipeline - Choose a CI/CD Tool: Use a tool like GitHub Actions, Jenkins, or GitLab CI to automate the deployment process. - Create a CI/CD Workflow: 1. Clone the Repository: Set up your CI/CD pipeline to clone your GitHub repository. 2. Build a Docker Image: Create a Docker image that includes your flow scripts and any dependencies. You can use a

Dockerfile

for this:

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dockerfile
     FROM python:3.9
     WORKDIR /app
     COPY . .
     RUN pip install prefect
     # Install additional dependencies if needed

3. Push the Docker Image: Push the built Docker image to a container registry accessible by your Kubernetes cluster (e.g., Docker Hub, AWS ECR). ### Step 3: Update Your Helm Deployment - Modify Helm Values: Update your Helm chart's

values.yaml

or deployment template to use the newly built Docker image. - Deploy the Image: Use Helm to update your Prefect deployment with the new image:

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bash
  helm upgrade [release_name] [chart_path] --values values.yaml

### Step 4: Register Flows with Prefect Server - Use Prefect CLI or API: - Write a script to register your flows with the Prefect server. This script should import your flow definitions and use

prefect.register

. - Example script:

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python
    from prefect import flow
    from my_flows import my_flow  # Import your flow

    if __name__ == "__main__":
        my_flow.register(project_name="my_project")

- Run this script as part of your CI/CD pipeline or as a Kubernetes job to ensure flows are registered with the Prefect server. ### Step 5: Monitor and Trigger Flows - Use Prefect UI: Once flows are registered, you can monitor and trigger them using the Prefect UI. This process automates the deployment and registration of your Prefect flows from a GitHub repository, ensuring that your Kubernetes cluster is always running the latest version of your flows.

Nideesh

01/04/2025, 11:41 AM

@Marvin no idiot i meant on the k8 helm install of prefect

Marvin

01/04/2025, 11:42 AM

Ah, I see. You're looking to directly integrate your GitHub repository with a Prefect deployment on Kubernetes using Helm. While the Prefect UI itself doesn't natively support pulling code from GitHub, you can achieve this integration by setting up an agent and using Prefect's infrastructure to execute flows from your repository. Here's how you can do it: ### Step 1: Ensure Your Flows are Accessible - Repository Access: Ensure your GitHub repository is either public or you have a method to authenticate (e.g., SSH keys, personal access tokens) to access it from within your Kubernetes cluster. ### Step 2: Configure a Prefect Agent - Agent Setup: Ensure you have a Prefect agent running in your Kubernetes cluster. This agent will be responsible for executing your flows. - Helm Chart Configuration: Modify your Helm chart to include the Prefect agent if it's not already set up. You may need to adjust the

values.yaml

to specify the agent's configuration. ### Step 3: Use Storage for Flow Code - Git Storage Block: Prefect supports using Git storage to pull in flow code. You can configure your flows to use a Git storage block, which allows Prefect to pull the flow code from your GitHub repository at runtime. - Define Git Storage: In your flow script, specify the Git repository as the storage location:

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python
  from prefect import flow
  from prefect.filesystems import GitHub

  github_block = GitHub.load("my-github-repo")  # Ensure this block is configured in your Prefect account

  @flow
  def my_flow():
      print("Hello from GitHub!")

  if __name__ == "__main__":
      my_flow.with_options(storage=github_block).register(project_name="my_project")

### Step 4: Register and Run Flows - Register the Flow: Use Prefect's CLI or API to register the flow with the Prefect server. The flow will be linked with the Git storage block, allowing it to pull the latest code from your GitHub repository. - Execute the Flow: Once registered, you can trigger the flow execution via the Prefect UI or API. The agent will pull the code from GitHub and execute it. ### Step 5: Automate Updates - CI/CD Integration: Optionally, integrate this process into a CI/CD pipeline to automate the registration of new flows or updates whenever changes are pushed to the GitHub repository. By using Git storage and configuring your Prefect agent within the Kubernetes cluster, you can effectively pull and execute flows directly from your GitHub repository.

Nideesh

01/04/2025, 11:42 AM

@Marvin give me the simplest solution

Marvin

01/04/2025, 11:42 AM

For the simplest way to pull and execute flows from a GitHub repository in a Kubernetes environment using a Helm-installed Prefect setup, follow these streamlined steps: ### Step 1: Use GitHub Storage Block 1. Create a GitHub Storage Block: - Use Prefect's GitHub storage block to point to your repository. This allows Prefect to pull the flow code directly from GitHub. - You can create this block in your Prefect Cloud or Prefect Server UI. 2. Define Your Flow: - In your flow script, specify the GitHub storage block:

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python
   from prefect import flow
   from prefect.filesystems import GitHub

   github_block = GitHub(repository="your-username/your-repo")  # Replace with your GitHub repo

   @flow
   def my_flow():
       print("Hello from GitHub!")

   if __name__ == "__main__":
       my_flow.with_options(storage=github_block).register(project_name="my_project")

### Step 2: Register Your Flow - Register the Flow: - Use the Prefect CLI or a Python script to register your flow with the Prefect server:

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bash
  prefect deployment build my_flow.py:my_flow -n "My Flow" -sb github/my-github-block
  prefect deployment apply my_flow-deployment.yaml

### Step 3: Execute the Flow - Run the Flow: - Trigger the flow execution from the Prefect UI or CLI. The Prefect agent running in your Kubernetes cluster will automatically pull the latest code from GitHub and execute it. ### Step 4: Automate (Optional) - Automate Registration: - Consider adding a CI/CD pipeline step to automate flow registration whenever changes are pushed to your GitHub repository. This method leverages Prefect's GitHub storage block to seamlessly integrate your GitHub repository with your Prefect setup, allowing for easy updates and execution of your flows directly from source control.

Nideesh

01/04/2025, 11:44 AM

@Marvin which k8 pod do i run that prefect dpeloyment on? or do i use the UI?

Marvin

01/04/2025, 11:44 AM

In a Kubernetes setup with Prefect installed via Helm, you typically don't run the deployment command directly on a Kubernetes pod. Instead, you can use the Prefect CLI from your local environment or a CI/CD pipeline to register and deploy flows. Here's how you can handle it: ### Option 1: Use the Prefect CLI Locally 1. Run Deployment Locally: - Use the Prefect CLI from your local machine to create and apply the deployment. This involves the

prefect deployment build

and

prefect deployment apply

commands. - Example:

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bash
     prefect deployment build my_flow.py:my_flow -n "My Flow" -sb github/my-github-block
     prefect deployment apply my_flow-deployment.yaml

2. Ensure Connectivity: - Make sure your local environment can communicate with the Prefect server running in your Kubernetes cluster. This might require VPN access or network configuration if the server is not publicly accessible. ### Option 2: Use the Prefect UI 1. Register Flows via UI: - If you prefer using the UI, you can manually register and configure deployments within the Prefect UI. This approach is more interactive and doesn't require CLI commands. 2. Monitor and Trigger: - Once registered, you can monitor and trigger flow runs directly from the UI. ### Option 3: Automate with CI/CD 1. Automate Deployment: - Integrate the Prefect CLI commands into a CI/CD pipeline (e.g., GitHub Actions, Jenkins) to automate the deployment process whenever there are changes to your flows in the GitHub repository. By using these methods, you can effectively manage your Prefect deployments without needing to manually execute commands on Kubernetes pods. The Prefect agent running in your cluster will handle the execution of registered flows.

Nideesh

01/04/2025, 11:46 AM

@Marvin no the helm chart you gave me for installation actual ydpeloye dservice looks so different than the local docker compose its night and day difference did i install the wrong thing?

Marvin

01/04/2025, 11:46 AM

It sounds like you might be comparing two different deployment methods for Prefect, which indeed have distinct configurations and purposes. Let's clarify the differences: ### Prefect Helm Chart vs. Docker Compose 1. Helm Chart (Kubernetes): - Purpose: Designed for deploying Prefect in a Kubernetes environment, which is typically used for production or larger-scale deployments. - Components: Deploys multiple Kubernetes resources like Deployments, Services, ConfigMaps, etc., to manage the various components of Prefect (e.g., server, agents). - Scalability: Offers scalability and high availability features inherent to Kubernetes. - Complexity: More complex than Docker Compose due to the nature of Kubernetes, but offers more control and integration with Kubernetes features. 2. Docker Compose: - Purpose: Primarily used for local development and testing. It runs Prefect components as Docker containers on a single host. - Components: Simpler setup with a

docker-compose.yml

file that defines all services, such as the Prefect server, agents, and any dependencies. - Scalability: Limited to the capabilities of a single machine; not suitable for production-scale deployments. - Ease of Use: Easier to set up and manage for local development due to its simplicity. ### Did You Install the Wrong Thing? - Check Your Requirements: If you intended to deploy Prefect for local development or testing, Docker Compose is the appropriate choice. If you need a scalable, production-ready deployment, then the Helm chart on Kubernetes is suitable. - Verify Installation: - For Docker Compose: You should have a

docker-compose.yml

file and use

docker-compose up

to start the services. - For Helm/Kubernetes: You should have used Helm commands like

helm install

to deploy Prefect on a Kubernetes cluster. If your goal was to set up Prefect for local development and you ended up with a Kubernetes deployment, you might want to switch to using Docker Compose. Conversely, if you need a robust deployment for production, then Kubernetes with Helm is the right choice. Let me know if you need help with specific commands or configurations!

Nideesh

01/04/2025, 11:47 AM

@Marvin i have a helm isntalled this successuly . but everything is compelt empty. how do i get some python code here to run?

Marvin

01/04/2025, 11:47 AM

If you've successfully installed Prefect using Helm on Kubernetes but find everything empty, you'll need to deploy and register your Python flows so that they can be executed by the Prefect infrastructure. Here's a step-by-step guide to get your Python code running: ### Step 1: Write Your Prefect Flow 1. Create a Flow Script: - Write a Python script that defines your Prefect flow. For example:

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python
   from prefect import flow

   @flow
   def my_flow():
       print("Hello, Prefect!")

   if __name__ == "__main__":
       my_flow()

### Step 2: Use Prefect CLI to Register the Flow 1. Install Prefect Locally: - Ensure you have the Prefect CLI installed on your local machine. You can install it using pip:

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bash
     pip install prefect

2. Build and Apply Deployment: - Use the Prefect CLI to create and apply a deployment for your flow. This involves creating a deployment YAML file and then applying it.

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bash
   prefect deployment build my_flow.py:my_flow -n "My Flow" -sb github/my-github-block
   prefect deployment apply my_flow-deployment.yaml

### Step 3: Ensure Connectivity - Network Configuration: - Make sure your local machine can communicate with the Prefect server running on your Kubernetes cluster. This may involve setting up VPN access or configuring network rules if the server is not publicly accessible. ### Step 4: Trigger and Monitor the Flow 1. Use Prefect UI: - Once the flow is registered, you can use the Prefect UI to trigger the flow run and monitor its execution. 2. Automate Execution: - You can set schedules or triggers within the Prefect UI to automate the execution of your flows based on specific conditions or time intervals. By following these steps, you should be able to deploy and execute your Python code within your Prefect environment on Kubernetes. If you encounter specific issues or need further customization, feel free to ask for more detailed guidance!

Nideesh

01/04/2025, 11:48 AM

@Marvin no i dont wanna make my local machine talk to prefect server this way. is there cleaner eay?

Marvin

01/04/2025, 11:48 AM

Certainly! If you want to avoid using your local machine to interact with the Prefect server directly, you can manage everything within your Kubernetes environment. Here's a streamlined approach: ### Step 1: Prepare Your Flow Code 1. Host Your Code in a Repository: - Store your Prefect flow scripts in a GitHub repository. This allows you to pull the code directly into your Kubernetes environment. ### Step 2: Use a Kubernetes Job or Pod 1. Create a Docker Image: - Build a Docker image that includes your Prefect flow scripts and any necessary dependencies. - Example `Dockerfile`:

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dockerfile
     FROM python:3.9
     WORKDIR /app
     COPY . .
     RUN pip install prefect

2. Push the Docker Image: - Push the image to a container registry accessible by your Kubernetes cluster. 3. Kubernetes Job: - Create a Kubernetes Job that uses this Docker image to register the flow with the Prefect server. - Example Job YAML:

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yaml
     apiVersion: batch/v1
     kind: Job
     metadata:
       name: prefect-flow-register
     spec:
       template:
         spec:
           containers:
           - name: flow-register
             image: your-registry/your-image:latest
             command: ["python", "-c", "from my_flow import my_flow; my_flow.register(project_name='my_project')"]
           restartPolicy: Never
       backoffLimit: 4

4. Deploy the Job: - Apply the Job YAML to your Kubernetes cluster:

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bash
     kubectl apply -f prefect-flow-register-job.yaml

### Step 3: Use Prefect UI for Execution - Trigger Flows: - Once the flows are registered, you can use the Prefect UI to trigger and monitor their execution. ### Step 4: Automate with CI/CD (Optional) - CI/CD Integration: - Set up a CI/CD pipeline to automatically build the Docker image and deploy the Kubernetes Job whenever changes are made to your flow scripts in the GitHub repository. This approach keeps everything within the Kubernetes environment, making it cleaner and more automated without needing your local machine to interact directly with the Prefect server.

Nideesh

01/04/2025, 11:49 AM

@Marvin is there a differen way? simpler?

Marvin

01/04/2025, 11:49 AM

Certainly! If you're looking for a simpler way to run your Python flows directly within your Kubernetes environment without involving your local machine, you can utilize Prefect's capabilities to execute flows using Git storage. Here's a straightforward approach: ### Step 1: Use GitHub Storage Block 1. Configure GitHub Storage Block: - In the Prefect UI, set up a GitHub storage block that points to your repository containing the flow code. This allows Prefect to pull your flow code directly from GitHub at runtime. 2. Define Your Flow with GitHub Storage: - In your flow script, specify the GitHub storage block:

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python
   from prefect import flow
   from prefect.filesystems import GitHub

   github_block = GitHub.load("your-github-block-name")  # Ensure this block is configured in your Prefect account

   @flow
   def my_flow():
       print("Hello from GitHub!")

   if __name__ == "__main__":
       my_flow.with_options(storage=github_block).register(project_name="my_project")

### Step 2: Register and Trigger Flows 1. Register the Flow: - Use the Prefect UI to register your flow. You can do this by creating a deployment in the UI and linking it to the GitHub storage block. 2. Run the Flow: - Trigger the flow execution directly from the Prefect UI. The Prefect agent running in your Kubernetes cluster will pull the latest code from GitHub and execute it. ### Step 3: Automate (Optional) - CI/CD Integration: - Optionally, integrate this setup into a CI/CD pipeline to automatically update the GitHub storage block or trigger deployments when code changes. This method leverages Prefect's GitHub storage to execute flows without needing to manage Docker images or Kubernetes Jobs manually, simplifying the process significantly.

Nideesh

01/04/2025, 11:49 AM

@Marvin where is the github storage block?

Marvin

01/04/2025, 11:49 AM

To use a GitHub storage block in Prefect, you need to configure it as part of your Prefect setup. Here’s how you can do it: ### Setting Up a GitHub Storage Block 1. Create a GitHub Storage Block: - Prefect UI: Go to the Prefect UI and navigate to the blocks section. Here, you can create a new storage block and specify GitHub as the storage type. You'll need to provide the repository URL and any necessary authentication details, such as a personal access token for private repositories. - Authentication: For private repositories, create a credentials block or a Secret block to store your GitHub credentials. You can use a personal access token for authentication. 2. Reference the Block in Your Flow: - In your flow script, you can load the GitHub storage block and associate it with your flow:

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python
     from prefect import flow
     from prefect.filesystems import GitHub

     github_block = GitHub.load("your-github-block-name")  # Replace with your block's name

     @flow
     def my_flow():
         print("Hello from GitHub!")

     if __name__ == "__main__":
         my_flow.with_options(storage=github_block).register(project_name="my_project")

3. Deploy and Run: - Use the Prefect UI or CLI to register and deploy your flow. The Prefect agent will use the GitHub storage block to pull the flow code from the specified repository. ### Additional Resources - Prefect Documentation on Git-based Storage This setup allows Prefect to directly access and execute your flow code from a GitHub repository, simplifying the process of managing and running your flows in a Kubernetes environment.

Nideesh

01/04/2025, 11:53 AM

@Marvin somehow i cant even add a project? do i need it to create a block?

Marvin

01/04/2025, 11:53 AM

In Prefect 2.x and 3.x, the concept of "projects" as it existed in Prefect 1.x has been largely deprecated. Instead, Prefect now uses a more flexible system of blocks and deployments. You don't need to create a "project" to create a block. Here's how you can proceed: ### Creating a Block Without a Project 1. Access Prefect UI: - Log in to your Prefect UI, which is part of your Helm-installed Prefect setup. 2. Navigate to Blocks: - In the UI, find the section for managing blocks. This is where you can create and manage storage blocks, including those for GitHub. 3. Create a GitHub Block: - Select the option to create a new block and choose GitHub as the type. You'll need to provide details such as the repository URL and, if necessary, authentication credentials. 4. Authentication: - For private repositories, ensure you have the necessary credentials set up. This might involve creating a Secret block with a personal access token. 5. Use the Block in Your Flow: - Once the block is created, you can reference it in your flow code as described earlier. ### Running Flows - Deployments: Instead of projects, Prefect 3.x focuses on deployments. You can create deployments for your flows and manage them through the UI or CLI. - Monitoring and Execution: Use the Prefect UI to monitor and execute your flows once they are registered and deployed. By focusing on blocks and deployments, Prefect provides a more modular and flexible approach to managing flow execution and storage. If you're having trouble with specific steps, let me know, and I can provide more detailed guidance.

Nideesh

01/04/2025, 11:53 AM

@Marvin wtf which version of prefect am i running?

Marvin

01/04/2025, 11:53 AM

To determine which version of Prefect you are running, you can use the Prefect CLI. Here's how you can check: ### Check Prefect Version 1. Using Prefect CLI: - Open a terminal on a machine that has Prefect installed. - Run the following command:

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bash
     prefect version

- This command will display the version of Prefect that is currently installed. 2. In a Kubernetes Environment: - If you are working in a Kubernetes environment and have access to the Prefect pods, you can also check the version by inspecting the logs or environment variables of a running Prefect pod. - Use

kubectl

to get into one of the Prefect pods and run the version command:

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bash
     kubectl exec -it <prefect-pod-name> -- prefect version

3. Review Helm Chart Values: - If you installed Prefect using Helm, you can also check the

values.yaml

or the Helm release details to see which version of Prefect was specified for deployment. By following these steps, you should be able to determine the version of Prefect you are using. If you need further assistance with any of these steps, feel free to ask!

Nideesh

01/04/2025, 11:54 AM

@Marvin root@prefect-server-graphql-846b76b5f8-blm96:/prefect-server# prefect version 1.4.0 root@prefect-server-graphql-846b76b5f8-blm96:/prefect-server# so thats version 1 right? give me the helm install commands to get the latest prefect version

Marvin

01/04/2025, 11:54 AM

To upgrade to the latest version of Prefect using Helm, you'll need to update your Helm chart to the version that supports Prefect 2.x or 3.x, as you're currently running Prefect 1.x. Here's how you can do it: ### Step-by-Step Upgrade Instructions 1. Add or Update the Prefect Helm Repository:

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bash
   helm repo add prefecthq <https://prefecthq.github.io/prefect-helm>
   helm repo update

2. Check Available Versions: - List available versions to ensure you choose the correct one:

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bash
     helm search repo prefecthq/prefect-server --versions

3. Upgrade the Prefect Release: - Use the

helm upgrade

command to update your existing Prefect deployment. Replace

[release_name]

with your current release name and

[chart_version]

with the desired version:

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bash
     helm upgrade [release_name] prefecthq/prefect-server --namespace [your-namespace] --version [chart_version]

4. Verify the Upgrade: - Check the status of your Prefect pods to ensure they are running the updated version:

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bash
     kubectl get pods -n [your-namespace]

5. Check Prefect Version: - Once the upgrade is complete, you can verify the Prefect version by checking the logs of one of the Prefect pods or using the Prefect CLI within a pod:

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bash
     kubectl exec -it [prefect-pod-name] -- prefect version

### Additional Considerations - Backup Configuration: Before upgrading, ensure you have backups of your current configuration and any important data. - Review Release Notes: Familiarize yourself with the latest release notes to understand any breaking changes or new features. By following these steps, you should be able to upgrade to the latest version of Prefect using Helm in your Kubernetes environment. If you encounter any issues, feel free to ask for more specific guidance!

Nideesh

01/04/2025, 11:57 AM

@Marvin give me example of this helm upgrade [release_name] prefecthq/prefect-server --namespace [your-namespace] --version [chart_version] if namespace is prefect and version is 2025.1.2233807 what is release-name?

Marvin

01/04/2025, 11:57 AM

In the context of Helm, the

release_name

is the name you gave to your Prefect deployment when you first installed it. This name is used to track and manage the Helm release. If you're not sure what it is, you can list your Helm releases to find it. Here's how you can do that and an example of the upgrade command: ### Step 1: Find Your Release Name 1. List Helm Releases: - Run the following command to list all Helm releases in the

prefect

namespace:

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bash
     helm list --namespace prefect

- Look for the release associated with Prefect. The name in the first column is your

release_name

. ### Step 2: Upgrade the Release Assuming your release name is

my-prefect-release

, the upgrade command would look like this:

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bash
helm upgrade my-prefect-release prefecthq/prefect-server --namespace prefect --version 2025.1.2233807

### Notes - Release Name: Replace

my-prefect-release

with the actual release name you found in Step 1. - Version: Ensure that the version

2025.1.2233807

is valid and available in the Helm repository. Double-check using

helm search repo prefecthq/prefect-server --versions

to verify available versions. By following these steps, you can upgrade your Prefect deployment to the specified version using Helm. If you encounter any issues, feel free to ask for more help!

Nideesh

01/04/2025, 11:57 AM

@Marvin what should the release name be?

Marvin

01/04/2025, 11:57 AM

The

release_name

in a Helm deployment is a user-defined name that you specify when you first install a Helm chart. It serves as an identifier for the set of Kubernetes resources created by that Helm release. If you didn't specify a name during installation, Helm would have automatically generated one for you. ### How to Determine or Choose a Release Name 1. If Already Installed: - Use the following command to list the existing Helm releases in the

prefect

namespace:

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bash
     helm list --namespace prefect

- The name in the first column of the output is your

release_name

. 2. If Installing Fresh: - You can choose any name that makes sense for your deployment. Common choices are descriptive names like

prefect-server

data-orchestration

, or simply

prefect

. ### Example for Installation or Upgrade - Initial Installation:

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bash
  helm install my-prefect-release prefecthq/prefect-server --namespace prefect

- Upgrade:

Copy code

bash
  helm upgrade my-prefect-release prefecthq/prefect-server --namespace prefect --version 2025.1.2233807

Replace

my-prefect-release

with the actual name you want to use or have already used. The release name is entirely up to you, but it should be unique within the namespace to avoid conflicts.

Nideesh

01/04/2025, 11:58 AM

@Marvin nideesh@nideesh:~$ helm upgrade prefect-server prefecthq/prefect-server --namespace prefect --version 2025.1.2233807 Error: UPGRADE FAILED: values don't meet the specifications of the schema(s) in the following chart(s): prefect-server: - server: Additional property database is not allowed - server: Additional property service is not allowed nideesh@nideesh:~$

Marvin

01/04/2025, 11:58 AM

The error message indicates that there are properties in your Helm values configuration that are not recognized by the schema of the Prefect Helm chart. Specifically, it mentions

database

and

service

as additional properties that are not allowed. Here's how you can address this issue: ### Steps to Resolve 1. Review Your
values.yaml
File: - Check your

values.yaml

file for the Prefect Helm chart. Look for sections related to

database

and

service

that might be incorrectly configured or not supported in the version you're trying to upgrade to. 2. Compare with Default Values: - Compare your

values.yaml

with the default values provided by the Prefect Helm chart for the version you're upgrading to. You can find these defaults in the chart repository or by downloading the chart and inspecting its

values.yaml

. 3. Remove or Correct Unsupported Properties: - If

database

and

service

are not supported properties in the new chart version, you may need to remove or adjust them according to the new schema. 4. Consult Documentation: - Check the documentation or release notes for the specific version you're upgrading to. There might have been changes in how certain configurations are handled. 5. Validate Configuration: - Use the

helm template

command to render the Helm chart locally and validate your configuration:

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bash
     helm template my-prefect-release prefecthq/prefect-server --namespace prefect --version 2025.1.2233807 -f values.yaml

### Example of Correcting

values.yaml

If you find unsupported properties, you might need to adjust your configuration like this:

Copy code

yaml
# Example of a simplified values.yaml
server:
  # Remove or update any unsupported properties here
  # e.g., remove database and service if they're not part of the schema
  image:
    repository: prefecthq/prefect-server
    tag: "2025.1.2233807"

By ensuring your

values.yaml

aligns with the expected schema of the Helm chart, you should be able to proceed with the upgrade successfully. If you need further assistance, feel free to provide more details about your current configuration!

Nideesh

01/04/2025, 12:00 PM

@Marvin give me the values and yaml file for newest prefect i am using longhorn and have metallb with loadBalancerIP: "192.168.10.38"

Marvin

01/04/2025, 12:00 PM

To set up Prefect using Helm with Longhorn and MetalLB, you'll need to create a

values.yaml

file that configures the necessary settings for your environment. Below is a sample configuration that you can use as a starting point: ### Sample

values.yaml

for Prefect with Longhorn and MetalLB

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect-server
    tag: "latest"  # Ensure you replace this with the correct version if needed

  service:
    type: LoadBalancer
    loadBalancerIP: "192.168.10.38"

  storage:
    # Assuming Longhorn is set up as a default StorageClass
    persistentVolume:
      storageClass: longhorn
      size: 10Gi

agent:
  enabled: true
  image:
    repository: prefecthq/prefect-agent
    tag: "latest"

  # Configure agent settings as needed
  config:
    PREFECT_API_URL: "<http://prefect-server:4200>"  # Adjust if necessary

# Additional configurations for other components can be added here

### Steps to Deploy 1. Create Namespace:

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bash
   kubectl create namespace prefect

2. Add Helm Repository:

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bash
   helm repo add prefecthq <https://prefecthq.github.io/prefect-helm>
   helm repo update

3. Install Prefect with Custom Values:

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bash
   helm install prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

### Notes - Version: Ensure you specify the correct image tags for the Prefect components. You can find the latest tags in the Prefect Helm repository. - Storage: The configuration assumes Longhorn is set up as the default StorageClass. Adjust the

storageClass

if your setup requires a different configuration. - LoadBalancer: The

loadBalancerIP

is configured for MetalLB. Ensure this IP is within the range of IPs managed by MetalLB in your cluster. This configuration provides a basic setup that you can further customize based on your specific requirements and environment. If you encounter any issues or need further adjustments, feel free to ask for more detailed guidance!

Nideesh

01/04/2025, 12:08 PM

@Marvin root@nideesh:/kubernetes_config/prefect# helm install prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml Error: INSTALLATION FAILED: values don't meet the specifications of the schema(s) in the following chart(s): prefect-server: - server: Additional property service is not allowed - server: Additional property storage is not allowed root@nideesh:/kubernetes_config/prefect#

Marvin

01/04/2025, 12:08 PM

The error message indicates that the properties

service

and

storage

are not recognized in the Helm chart's schema for the Prefect server. This suggests that the Helm chart you are using does not support these specific configurations directly under the

server

key. ### Steps to Resolve 1. Review the Chart's Default Values: - Check the default

values.yaml

provided by the Prefect Helm chart to see the supported configuration options. You can do this by pulling the chart and inspecting its files:

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bash
     helm show values prefecthq/prefect-server --version [your-chart-version]

2. Adjust Your
values.yaml
: - Remove or adjust the unsupported properties based on the default values you found. Here’s a simplified version without

service

and

storage

directly under `server`:

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect-server
    tag: "latest"  # Ensure this is the correct version

  # Other server configurations go here

agent:
  enabled: true
  image:
    repository: prefecthq/prefect-agent
    tag: "latest"

  # Configure agent settings as needed
  config:
    PREFECT_API_URL: "<http://prefect-server:4200>"  # Adjust if necessary

# Service and storage configurations might need to be set elsewhere or differently

3. Separate Service and Storage Configurations: - If you need to configure service type and load balancer settings, this might be done at a different level, possibly as part of a Kubernetes

Service

resource rather than directly in the Helm values. - Similarly, storage settings might need to be configured through PersistentVolumeClaims or other Kubernetes resources, depending on how the chart is structured. 4. Consult Documentation: - Refer to the Prefect Helm chart documentation or any README files provided with the chart to understand the correct configuration schema. By aligning your

values.yaml

with the supported schema, you should be able to successfully deploy Prefect using Helm. If you continue to encounter issues, feel free to provide more details or ask for further assistance!

Nideesh

01/04/2025, 12:13 PM

@Marvin Ready Name Image Init Container Restarts Started Waiting prefect-server prefecthq/prefect-server:3.1.9-python3.11 0 - ImagePullBackOff (Back-off pulling image "prefecthq/prefect-server:3.1.9-python3.11")

Marvin

01/04/2025, 12:13 PM

The

ImagePullBackOff

error indicates that Kubernetes is unable to pull the specified Docker image for the Prefect server. This could be due to several reasons, such as the image not existing in the specified registry, network issues, or authentication problems. Here’s how you can troubleshoot and resolve this issue: ### Steps to Resolve ImagePullBackOff 1. Verify Image Tag and Repository: - Double-check the image tag and repository name to ensure they are correct. It seems you are using

prefecthq/prefect-server:3.1.9-python3.11

. Verify that this image exists in the Docker registry:

Copy code

bash
     docker pull prefecthq/prefect-server:3.1.9-python3.11

- If the tag does not exist, you might need to use a different version. You can check available tags on Docker Hub or the Prefect documentation. 2. Check Network Connectivity: - Ensure that your Kubernetes nodes have internet access to pull images from Docker Hub or the specified registry. 3. Authentication: - If you are pulling from a private registry, ensure that you have the proper image pull secrets configured in your Kubernetes cluster. 4. Inspect Pod Events: - Check the events for the pod to get more details about the error:

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bash
     kubectl describe pod [pod-name] -n [namespace]

- Look for any specific error messages related to image pulling. 5. Update Image: - If the image tag is incorrect or unavailable, update your

values.yaml

or Helm release to use a valid image tag. 6. Redeploy: - Once you have corrected the image details, redeploy the Helm chart:

Copy code

bash
     helm upgrade prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

By following these steps, you should be able to resolve the

ImagePullBackOff

error and successfully deploy the Prefect server. If you need further assistance, feel free to ask!

Nideesh

01/04/2025, 12:18 PM

@Marvin yeah that python doesnt exist change my yaml

Marvin

01/04/2025, 12:18 PM

Let's adjust your

values.yaml

to use a valid Prefect server image. Here's how you can modify it: ### Updated

values.yaml

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect-server
    tag: "3.1.9"  # Use a valid tag without specifying Python version

agent:
  enabled: true
  image:
    repository: prefecthq/prefect-agent
    tag: "3.1.9"  # Ensure this matches the server version

  config:
    PREFECT_API_URL: "<http://prefect-server:4200>"  # Adjust if necessary

### Steps to Apply Changes 1. Update Your Helm Release: - Apply the changes using Helm to update your deployment:

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bash
     helm upgrade prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

2. Verify Deployment: - Check the status of your pods to ensure they are running the correct image:

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bash
     kubectl get pods -n prefect

By using a valid and existing image tag, you should be able to resolve the

ImagePullBackOff

error and successfully deploy the Prefect server. If you encounter any further issues, feel free to ask for more help!

Nideesh

01/04/2025, 12:23 PM

@Marvin give me accurate server and agent docker imges

Marvin

01/04/2025, 12:23 PM

To ensure you're using the correct and latest Docker images for Prefect, you can refer to the official Prefect Docker Hub repository. However, here's a general guide based on typical usage: ### Prefect Server and Agent Docker Images 1. Prefect Server Image: - Repository:

prefecthq/prefect-server

- Tag: Use the latest stable version or a specific version that matches your requirements. For example:

Copy code

yaml
     server:
       image:
         repository: prefecthq/prefect-server
         tag: "3.1.9"  # Replace with the latest stable version if needed

2. Prefect Agent Image: - Repository:

prefecthq/prefect-agent

- Tag: Ensure the tag matches the server version for compatibility. For example:

Copy code

yaml
     agent:
       enabled: true
       image:
         repository: prefecthq/prefect-agent
         tag: "3.1.9"  # Replace with the latest stable version if needed

### Steps to Update

values.yaml

1. Update Your
values.yaml
: - Ensure your

values.yaml

uses the correct image tags as shown above. 2. Apply Changes with Helm: - Use Helm to update your Prefect deployment with the correct images:

Copy code

bash
     helm upgrade prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

3. Verify Deployment: - Check the status of your pods to confirm they are running the updated images:

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bash
     kubectl get pods -n prefect

By using the correct Docker images and tags, you should be able to deploy Prefect successfully in your Kubernetes environment. If you encounter any issues, feel free to ask for further assistance!

Nideesh

01/04/2025, 12:24 PM

@Marvin where is the prefecthq docer image url?

Marvin

01/04/2025, 12:24 PM

The Docker images for Prefect are hosted on Docker Hub. You can find the official Prefect images at the following locations: 1. Prefect Server Image: - Docker Hub URL: Prefect Server on Docker Hub 2. Prefect Agent Image: - Docker Hub URL: Prefect Agent on Docker Hub These URLs will take you to the Docker Hub pages where you can view available tags, pull commands, and other details about the Prefect images. You can use these images in your

values.yaml

file for your Kubernetes deployments.

Nideesh

01/04/2025, 12:25 PM

@Marvin just give me a proper simple yaml file using this image prefecthq/prefect:3-python3.12

Marvin

01/04/2025, 12:25 PM

Here's a simple

values.yaml

configuration for deploying Prefect using the image

prefecthq/prefect:3-python3.12

. This configuration assumes a basic setup with a Prefect server and agent running in a Kubernetes environment. ### Sample

values.yaml

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect
    tag: "3-python3.12"

  service:
    type: LoadBalancer
    loadBalancerIP: "192.168.10.38"  # Adjust based on your MetalLB configuration

  # Add any additional server configurations here

agent:
  enabled: true
  image:
    repository: prefecthq/prefect
    tag: "3-python3.12"

  config:
    PREFECT_API_URL: "<http://prefect-server:4200>"  # Adjust if necessary

  # Add any additional agent configurations here

### Steps to Deploy 1. Create Namespace (if not already created):

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bash
   kubectl create namespace prefect

2. Add Helm Repository:

Copy code

bash
   helm repo add prefecthq <https://prefecthq.github.io/prefect-helm>
   helm repo update

3. Install Prefect with Custom Values:

Copy code

bash
   helm install prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

### Notes - LoadBalancerIP: Ensure the

loadBalancerIP

is within the range managed by your MetalLB setup. - Service Type: The

LoadBalancer

service type is used to expose the Prefect server externally. Adjust this based on your network requirements. - Image Tag: The

prefecthq/prefect:3-python3.12

image is used for both the server and the agent, assuming compatibility with your Prefect setup. This configuration provides a basic setup that you can further customize based on your specific requirements and environment. If you encounter any issues or need further adjustments, feel free to ask for more detailed guidance!

Nideesh

01/04/2025, 12:25 PM

@Marvin ust give me a proper simple yaml file using this image prefecthq/prefect

Marvin

01/04/2025, 12:25 PM

Here's a simple

values.yaml

configuration for deploying Prefect using the image

prefecthq/prefect

. This setup assumes you want to use the Prefect image for both the server and agent components within a Kubernetes environment. ### Sample

values.yaml

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect
    tag: "latest"  # Use the latest tag or specify a specific version if needed

  service:
    type: LoadBalancer
    loadBalancerIP: "192.168.10.38"  # Ensure this is within your MetalLB IP range

agent:
  enabled: true
  image:
    repository: prefecthq/prefect
    tag: "latest"  # Use the latest tag or specify a specific version if needed

  config:
    PREFECT_API_URL: "<http://prefect-server:4200>"  # Adjust based on your setup

### Deployment Steps 1. Create Namespace:

Copy code

bash
   kubectl create namespace prefect

2. Add Helm Repository:

Copy code

bash
   helm repo add prefecthq <https://prefecthq.github.io/prefect-helm>
   helm repo update

3. Install Prefect with Custom Values:

Copy code

bash
   helm install prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

### Key Points - LoadBalancerIP: Ensure the

loadBalancerIP

is correctly configured to match your MetalLB settings. - Service Type: The

LoadBalancer

type is used to expose the Prefect server externally. Adjust this if you have different network requirements. - Image Tag: The

latest

tag is used here, but you can specify a specific version tag if you have a preference or requirement for a specific Prefect version. This configuration provides a basic setup that you can further customize as needed. If you encounter any issues or need further guidance, feel free to ask!

Nideesh

01/04/2025, 12:26 PM

@Marvin not service in the yaml

Marvin

01/04/2025, 12:26 PM

Let's simplify the

values.yaml

configuration without specifying the

service

block explicitly, focusing on using the

prefecthq/prefect

image for both the server and agent components. ### Sample

values.yaml

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect
    tag: "latest"  # Specify a specific version if needed

agent:
  enabled: true
  image:
    repository: prefecthq/prefect
    tag: "latest"  # Specify a specific version if needed

  config:
    PREFECT_API_URL: "<http://prefect-server:4200>"  # Adjust based on your setup

### Deployment Steps 1. Create Namespace:

Copy code

bash
   kubectl create namespace prefect

2. Add Helm Repository:

Copy code

bash
   helm repo add prefecthq <https://prefecthq.github.io/prefect-helm>
   helm repo update

3. Install Prefect with Custom Values:

Copy code

bash
   helm install prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

### Key Points - Image Tag: The

latest

tag is used here for simplicity, but you can specify a specific version tag to ensure consistency and compatibility. - Agent Configuration: Ensure the

PREFECT_API_URL

is correctly configured to point to your Prefect server's API endpoint. This configuration provides a straightforward setup using the Prefect Docker image, focusing on the essential components needed to get started. Adjust any additional settings based on your specific requirements and infrastructure. If you need further assistance, feel free to ask!

Nideesh

01/04/2025, 12:39 PM

@Marvin how can we expose the extrenalIp with the load balancer directly with the helm install yaml?

Marvin

01/04/2025, 12:39 PM

To expose the Prefect server using an external IP with a LoadBalancer directly in the Helm

values.yaml

, you need to configure the service type and specify the load balancer settings. Here’s how you can do it: ### Sample

values.yaml

with LoadBalancer Configuration

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect
    tag: "latest"  # Use the latest tag or specify a specific version if needed

  service:
    type: LoadBalancer
    loadBalancerIP: "192.168.10.38"  # Ensure this IP is within your MetalLB IP range

agent:
  enabled: true
  image:
    repository: prefecthq/prefect
    tag: "latest"  # Use the latest tag or specify a specific version if needed

  config:
    PREFECT_API_URL: "<http://prefect-server:4200>"  # Adjust based on your setup

### Deployment Steps 1. Create Namespace:

Copy code

bash
   kubectl create namespace prefect

2. Add Helm Repository:

Copy code

bash
   helm repo add prefecthq <https://prefecthq.github.io/prefect-helm>
   helm repo update

3. Install Prefect with Custom Values:

Copy code

bash
   helm install prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

### Key Points - LoadBalancerIP: The

loadBalancerIP

field is used to specify a static IP address for the LoadBalancer, which is managed by MetalLB. Ensure this IP is within the range configured for MetalLB in your cluster. - Service Type: Setting the service type to

LoadBalancer

allows the Prefect server to be exposed externally, making it accessible via the specified external IP. - Prefect API URL: Ensure that the

PREFECT_API_URL

in the agent configuration points to the correct server address. By configuring the

values.yaml

with these settings, you can deploy Prefect with an external IP exposed via a LoadBalancer, making it accessible from outside the Kubernetes cluster. If you have any additional questions or need further assistance, feel free to ask!

Nideesh

01/04/2025, 12:40 PM

@Marvin it wont allow service server: image: repository: prefecthq/prefect tag: "latest" # Specify a specific version if needed agent: enabled: true image: repository: prefecthq/prefect tag: "latest" # Specify a specific version if needed config: PREFECT_API_URL: "http://prefect-server:4200" # Adjust based on your setup ~ ~

Marvin

01/04/2025, 12:40 PM

If the Helm chart for Prefect doesn't support configuring the service type directly through the

values.yaml

, you may need to manually adjust the Kubernetes Service resource after deploying the Helm chart. However, let's try to address this by ensuring we use any available configuration options correctly. ### Adjusting Service Configuration Since the

values.yaml

doesn't allow direct service configuration, you can manually patch the service after deployment: 1. Deploy Prefect Using Helm:

Copy code

bash
   helm install prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

2. Patch the Service to Use a LoadBalancer: - After deploying, you can change the service type to

LoadBalancer

using `kubectl`:

Copy code

bash
   kubectl patch svc prefect-server -n prefect -p '{"spec": {"type": "LoadBalancer", "loadBalancerIP": "192.168.10.38"}}'

### Explanation - Patch Command: This command updates the service type to

LoadBalancer

and assigns it a static IP managed by MetalLB. - Manual Adjustment: This method requires a post-deployment step to adjust the service configuration, as the Helm chart may not expose this configuration directly. If you frequently need to perform this adjustment, consider automating it with a script or investigating if the Helm chart provides hooks or additional templates that can be customized to include this configuration. If you need further help, feel free to ask!

Nideesh

01/04/2025, 12:40 PM

@Marvin how cna i edit teh deploy yaml directly?

Marvin

01/04/2025, 12:40 PM

To edit the deployment YAML directly, you can use the following approach to extract and modify the YAML files generated by Helm. This allows you to customize the deployment according to your specific needs, including setting up the service as a LoadBalancer. ### Steps to Edit the Deployment YAML 1. Render the Helm Templates: - Use the

helm template

command to render the Helm chart into raw Kubernetes manifests. This will output the YAML files without applying them to your cluster:

Copy code

bash
   helm template prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml > prefect-deployment.yaml

2. Edit the YAML File: - Open the

prefect-deployment.yaml

file in your preferred text editor. Look for the

Service

resource corresponding to the Prefect server. - Modify the service type to

LoadBalancer

and specify the `loadBalancerIP`:

Copy code

yaml
   apiVersion: v1
   kind: Service
   metadata:
     name: prefect-server
     namespace: prefect
   spec:
     type: LoadBalancer
     loadBalancerIP: "192.168.10.38"
     ports:
       - port: 4200
         targetPort: 4200
     selector:
       app: prefect-server

3. Apply the Modified YAML: - Once you've made your changes, apply the modified YAML file to your Kubernetes cluster:

Copy code

bash
   kubectl apply -f prefect-deployment.yaml

### Explanation - Helm Template: This command generates the Kubernetes manifests from the Helm chart without deploying them, allowing you to make manual edits. - Manual Edits: By editing the YAML directly, you have full control over the configuration, including the ability to set the service type to

LoadBalancer

and specify an external IP. - Apply Changes: After editing, applying the YAML file will update your Kubernetes resources with the specified configurations. This approach provides the flexibility to make detailed customizations to your Prefect deployment. If you need further assistance or have any questions, feel free to ask!

Nideesh

01/04/2025, 12:41 PM

@Marvin edit in place

Marvin

01/04/2025, 12:41 PM

To edit the deployment in place while using Helm, you typically modify the

values.yaml

and then apply the changes using Helm. However, if you want to directly edit the deployed resources in Kubernetes, you can use

kubectl edit

. Here's how you can do it: ### Editing Kubernetes Resources In-Place 1. Identify the Service: - First, list the services in your namespace to get the exact name of the Prefect server service:

Copy code

bash
     kubectl get svc -n prefect

2. Edit the Service: - Use

kubectl edit

to directly modify the service configuration:

Copy code

bash
     kubectl edit svc [service-name] -n prefect

- Replace

[service-name]

with the actual name of the Prefect server service you identified in the previous step. 3. Modify the Service Type: - In the editor that opens, find the

spec

section of the service and change the type to

LoadBalancer

. Also, add the `loadBalancerIP`:

Copy code

yaml
   spec:
     type: LoadBalancer
     loadBalancerIP: "192.168.10.38"
     ports:
       - port: 4200
         targetPort: 4200
     selector:
       app: prefect-server

4. Save and Exit: - Save your changes and exit the editor. This will update the service configuration in place. ### Explanation - kubectl edit: This command opens the current configuration of the Kubernetes resource in your default text editor, allowing you to make changes directly. - In-Place Modification: By editing the resource in place, you can quickly adjust configurations such as service types and IP settings without needing to re-deploy. This method is handy for quick changes and testing configurations. However, for more permanent changes, consider updating your Helm

values.yaml

and reapplying with Helm to ensure consistency across deployments. If you have further questions or need additional help, feel free to ask!

Nideesh

01/04/2025, 12:50 PM

@Marvin there is no service for the server pod root@nideesh:/kubernetes_config/prefect# kubectl get all -n prefect NAME READY STATUS RESTARTS AGE pod/prefect-server-66dd49f64-296vm 1/1 Running 0 23m pod/prefect-server-postgresql-0 1/1 Running 0 40m NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/prefect-server-postgresql ClusterIP 10.103.174.71 <none> 5432/TCP 40m service/prefect-server-postgresql-hl ClusterIP None <none> 5432/TCP 40m NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/prefect-server 1/1 1 1 40m NAME DESIRED CURRENT READY AGE replicaset.apps/prefect-server-5b6db6b994 0 0 0 40m replicaset.apps/prefect-server-66dd49f64 1 1 1 23m NAME READY AGE statefulset.apps/prefect-server-postgresql 1/1 40m root@nideesh:/kubernetes_config/prefect#

Marvin

01/04/2025, 12:50 PM

Your message was too long by 25 tokens...

Nideesh

01/04/2025, 12:50 PM

Marvin

01/04/2025, 12:50 PM

It looks like the Prefect server deployment does not have an associated Kubernetes Service, which is necessary to expose it either internally or externally. You'll need to create a Service to expose the Prefect server pod. Here's how you can do it: ### Create a Service for the Prefect Server 1. Create a Service YAML: - Create a YAML file named

prefect-server-service.yaml

with the following content:

Copy code

yaml
apiVersion: v1
kind: Service
metadata:
  name: prefect-server
  namespace: prefect
spec:
  type: LoadBalancer
  loadBalancerIP: "192.168.10.38"  # Ensure this IP is within your MetalLB IP range
  ports:
    - port: 4200
      targetPort: 4200
  selector:
    app: prefect-server

2. Apply the Service YAML: - Use

kubectl

to apply the service configuration:

Copy code

bash
   kubectl apply -f prefect-server-service.yaml

3. Verify the Service: - Check that the service has been created and is exposing the server:

Copy code

bash
   kubectl get svc -n prefect

### Explanation - Service Type: The

LoadBalancer

type is used to expose the Prefect server externally, which is suitable for setups using MetalLB. - Selector: The

selector

should match the labels used by the Prefect server deployment to ensure the service routes traffic to the correct pods. By creating this service, you will expose the Prefect server to external traffic via the specified load balancer IP. If you have any further questions or need additional help, feel free to ask!

Nideesh

01/06/2025, 2:15 AM

@Marvin nideesh@nideesh:~$ kubectl get all -n prefect NAME READY STATUS RESTARTS AGE pod/prefect-server-66dd49f64-296vm 1/1 Running 8 (4h1m ago) 37h pod/prefect-server-postgresql-0 1/1 Running 3 (4h3m ago) 38h NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/prefect-server LoadBalancer 10.105.218.185 192.168.10.38 4200:30744/TCP 37h service/prefect-server-postgresql ClusterIP 10.103.174.71 <none> 5432/TCP 38h service/prefect-server-postgresql-hl ClusterIP None <none> 5432/TCP 38h NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/prefect-server 1/1 1 1 38h NAME DESIRED CURRENT READY AGE replicaset.apps/prefect-server-5b6db6b994 0 0 0 38h replicaset.apps/prefect-server-66dd49f64 1 1 1 37h NAME READY AGE statefulset.apps/prefect-server-postgresql 1/1 38h nideesh@nideesh:~$ not working at the .38 ip. how do i change things?

Marvin

01/06/2025, 2:15 AM

Your message was too long by 78 tokens...

Nideesh

01/06/2025, 4:27 AM

@Marvin root@nideesh:/kubernetes_config/prefect# helm upgrade prefecthq/prefect-server -f values.yaml Error: "helm upgrade" requires 2 arguments Usage: helm upgrade [RELEASE] [CHART] [flags] root@nideesh:/kubernetes_config/prefect#

Marvin

01/06/2025, 4:28 AM

The error message indicates that the

helm upgrade

command is missing required arguments. When using

helm upgrade

, you need to specify both the release name and the chart location. Here's the correct usage: ### Correct

helm upgrade

Command 1. Identify Your Release Name: - If you don't remember the release name you used during the initial installation, you can list all releases in the

prefect

namespace:

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bash
     helm list --namespace prefect

- The first column in the output will show the release names. 2. Run the Upgrade Command: - Once you have the release name, use the following command to upgrade:

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bash
     helm upgrade [release-name] prefecthq/prefect-server --namespace prefect -f values.yaml

- Replace

[release-name]

with your actual release name. ### Example If your release name is

prefect-server

, the command would look like this:

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bash
helm upgrade prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

### Notes - Release Name: This is the name you provided when you first installed the Helm chart. - Chart Location:

prefecthq/prefect-server

specifies the chart to upgrade. - Namespace: Ensure the namespace is specified if it's not the default. By following these steps, you should be able to successfully upgrade your Prefect deployment using Helm. If you encounter any further issues, feel free to ask for more help!

Nideesh

01/06/2025, 5:05 AM

@Marvin why? I have no name!@prefect-server-66dd49f64-89tzp:~$ echo $PREFECT_API_URL http://localhost:4200/api I have no name!@prefect-server-66dd49f64-89tzp:~$

Marvin

01/06/2025, 5:06 AM

The output you're seeing,

I have no name!

, typically indicates that the user ID running the shell inside the container does not have a corresponding entry in the

/etc/passwd

file. This is often the case in containers where the user is specified by a numeric ID without an associated username. Regarding the

PREFECT_API_URL

environment variable, it seems to be set to the default value pointing to

localhost

. This setup is common in containerized environments where the Prefect server expects to be accessed internally within the same pod or cluster. ### Addressing the

I have no name!

Issue 1. User ID in Containers: - This is generally not a problem unless you need specific permissions or user configurations. It's more of a cosmetic issue in the shell prompt. 2. Fixing the Prompt: - If you need to fix this for clarity or specific requirements, you can modify the Dockerfile or container entrypoint to ensure the user ID has a corresponding name in

/etc/passwd

. ### Configuring

PREFECT_API_URL

1. Internal vs. External Access: - If you intend to access the Prefect server externally, ensure that the

PREFECT_API_URL

is set to the external service address or the load balancer IP you configured. 2. Setting the Correct URL: - You can set the

PREFECT_API_URL

in your

values.yaml

or as an environment variable in your Kubernetes deployment to point to the correct external address:

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yaml
     agent:
       config:
         PREFECT_API_URL: "<http://your-external-ip:4200>"

3. Apply Changes: - After modifying the configuration, redeploy or update your Helm release to apply the changes:

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bash
     helm upgrade prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

By ensuring the

PREFECT_API_URL

is correctly set and understanding the

I have no name!

prompt, you can manage your Prefect deployment more effectively. If you have further questions or need additional assistance, feel free to ask!

Nideesh

01/06/2025, 5:13 AM

@Marvin that doesn't work

Marvin

01/06/2025, 5:13 AM

Let's address the issues one step at a time to ensure your Prefect deployment is configured correctly. ### 1. Resolving the

I have no name!

Prompt The

I have no name!

prompt is typically due to the container running with a user ID that doesn't have an entry in

/etc/passwd

. While it's mostly a cosmetic issue, it can be resolved by ensuring the user is properly defined in the container's Dockerfile. However, this usually doesn't affect functionality unless specific user permissions are required for operations. ### 2. Configuring

PREFECT_API_URL

Correctly To ensure that your Prefect server and agents are communicating correctly, follow these steps: #### Verify and Set the Correct

PREFECT_API_URL

1. Determine the Correct URL: - If you're running Prefect in a Kubernetes cluster and need external access, the

PREFECT_API_URL

should point to the external IP of the LoadBalancer service. 2. Update Environment Variables in Deployment: - You can set the

PREFECT_API_URL

directly in the Kubernetes deployment YAML or through Helm values. Here's how you can do it: #### Example Values in

values.yaml

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yaml
agent:
  enabled: true
  image:
    repository: prefecthq/prefect
    tag: "latest"

  config:
    PREFECT_API_URL: "<http://192.168.10.38:4200>"  # Replace with your actual external IP

#### Apply the Configuration 1. Update the Helm Release: - Use Helm to apply the updated configuration:

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bash
     helm upgrade prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

2. Verify the Changes: - Check the logs of your Prefect server and agent pods to ensure they are using the correct API URL:

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bash
     kubectl logs [pod-name] -n prefect

### 3. Debugging Further - Check Pod Environment Variables: - Verify that the environment variable is set correctly inside the pod:

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bash
    kubectl exec -it [pod-name] -n prefect -- printenv | grep PREFECT_API_URL

- Service and Network Configuration: - Ensure that the Kubernetes services are correctly configured and that there are no network policies or firewall rules blocking access. If these steps don't resolve the issue, please provide more details about the specific errors or behavior you're encountering, and we can troubleshoot further.

Nideesh

01/06/2025, 5:14 AM

@Marvin this yaml doesnt work and expose an externalIP server: image: repository: prefecthq/prefect tag: "latest" # Ensure this is the correct version # Other server configurations go here agent: enabled: true image: repository: prefecthq/prefect tag: "latest" # Configure agent settings as needed config: PREFECT_API_URL: "http://prefect-server:4200" # Adjust if necessarya

Marvin

01/06/2025, 5:15 AM

To ensure your Prefect server is accessible via an external IP, you need to set up a Kubernetes Service of type

LoadBalancer

. If your Helm chart does not automatically create a service with the correct specifications, you can manually create or patch the service to expose the server externally. ### Steps to Expose Prefect Server with an External IP 1. Create a Service for the Prefect Server If a service doesn't already exist, you can create one manually. Here's a YAML configuration for a LoadBalancer service:

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yaml
apiVersion: v1
kind: Service
metadata:
  name: prefect-server
  namespace: prefect
spec:
  type: LoadBalancer
  loadBalancerIP: "192.168.10.38"  # Ensure this IP is within your MetalLB range
  ports:
    - port: 4200
      targetPort: 4200
  selector:
    app: prefect-server

2. Apply the Service Configuration - Save the above YAML to a file, e.g.,

prefect-server-service.yaml

, and apply it using kubectl:

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bash
  kubectl apply -f prefect-server-service.yaml

3. Verify the Service - Check that the service is created and has an external IP assigned:

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bash
  kubectl get svc -n prefect

### Explanation - Service Type: The

LoadBalancer

type makes the Prefect server accessible from outside the Kubernetes cluster. - LoadBalancerIP: Ensure this IP is within the range managed by MetalLB, which handles load balancer IP assignments in bare-metal Kubernetes setups. ### Additional Steps - Ensure Helm Values are Correct: If you need to adjust Helm values, ensure that the correct image tags and configurations are specified in your

values.yaml

. - Deploy or Update with Helm: If you make changes to your Helm

values.yaml

, apply them with:

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bash
  helm upgrade prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml

By following these steps, you should be able to expose your Prefect server with an external IP using a Kubernetes LoadBalancer service. If you encounter any issues, feel free to ask for further assistance!

Nideesh

01/06/2025, 5:15 AM

@Marvin sorry i mean this makes the server but doesn't expose the external loadbalaner IP server: image: repository: prefecthq/prefect tag: "latest" # Ensure this is the correct version # Other server configurations go here agent: enabled: true image: repository: prefecthq/prefect tag: "latest" # Configure agent settings as needed config: PREFECT_API_URL: "http://192.168.10.38:4200" # Adjust if necessarya

Marvin

01/06/2025, 5:15 AM

If the Prefect server is not being exposed with an external IP despite configuring the service as a

LoadBalancer

, there are a few steps you can take to troubleshoot and ensure the configuration is correct: ### Verify and Troubleshoot LoadBalancer Service 1. Check MetalLB Configuration: - Ensure that MetalLB is properly configured in your cluster and that the IP range includes

192.168.10.38

. - Verify that MetalLB's configuration map includes the correct IP address pool. 2. Verify Service Configuration: - Make sure the service is correctly set to

LoadBalancer

and is associated with the Prefect server pods. You can check this with:

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bash
     kubectl get svc prefect-server -n prefect -o yaml

- Confirm the

spec.type

LoadBalancer

and the

loadBalancerIP

is set as expected. 3. Check Service and Pod Labels: - Ensure that the service selector matches the labels on the Prefect server pods. This ensures traffic is correctly routed. 4. Describe the Service: - Use

kubectl describe

to get more details about the service and any events:

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bash
     kubectl describe svc prefect-server -n prefect

- Look for any events or errors that might indicate why the external IP is not being assigned. 5. Inspect MetalLB Logs: - Check the logs of the MetalLB controller and speaker pods for any errors or warnings:

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bash
     kubectl logs -l app=metallb -n metallb-system

6. Manual IP Assignment: - If automatic assignment isn't working, you can try manually setting the external IP by editing the service:

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bash
     kubectl edit svc prefect-server -n prefect

- Ensure the

loadBalancerIP

field is correctly set. ### Example Service YAML If you need to manually create or update the service, here's a YAML example:

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yaml
apiVersion: v1
kind: Service
metadata:
  name: prefect-server
  namespace: prefect
spec:
  type: LoadBalancer
  loadBalancerIP: "192.168.10.38"
  ports:
    - port: 4200
      targetPort: 4200
  selector:
    app: prefect-server

By following these steps, you should be able to diagnose and resolve issues with the external IP assignment for your Prefect server. If you continue to experience problems, feel free to provide more details for further assistance!

Nideesh

01/08/2025, 6:34 AM

@Marvin what is yaml spec to be used with the helm install? values don't meet the specifications of the schema(s) in the following chart(s): prefect-server: - server: Additional property config is not allowed

Marvin

01/08/2025, 6:34 AM

It seems like you might be encountering an issue with the Helm chart for Prefect Server. The error message indicates that there is an additional property

config

that is not allowed in your YAML file. For deploying Prefect using Helm, you can follow the guide here. This guide provides a step-by-step process for using Helm to deploy Prefect on Kubernetes. To resolve the specific issue you're facing, ensure that your

values.yaml

file adheres to the schema expected by the Helm chart. The

config

property might be misplaced or unnecessary, so you should verify the schema defined in the Helm chart documentation or examples. If you need to initialize a deployment configuration, you can use the

prefect init

CLI command to create a

prefect.yaml

file with base settings for your deployments. More details on this can be found here. If you continue to face issues, consider checking for any updates or changes in the Helm chart that might require adjustments in your configuration.

Nideesh

01/08/2025, 6:36 AM

@Marvin lets say i have k8 cluster. give me yaml for prefect 3

Marvin

01/08/2025, 6:36 AM

To deploy Prefect 3 on a Kubernetes cluster using Helm, you can follow these steps: 1. Add the Prefect Helm Repository:

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bash
   helm repo add prefect <https://prefecthq.github.io/prefect-helm>
   helm repo update

2. Create a Kubernetes Namespace:

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bash
   kubectl create namespace prefect

3. Create a Kubernetes Secret for the Prefect API Key: Replace

your-prefect-cloud-api-key

with your actual Prefect Cloud API key.

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bash
   kubectl create secret generic prefect-api-key \
   --namespace=prefect --from-literal=key=your-prefect-cloud-api-key

4. Create a
values.yaml
file: Customize it with your specific configuration. Here's a basic structure:

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yaml
   worker:
     image:
       repository: prefecthq/prefect
       tag: 3.0.0
     env:
       PREFECT_API_URL: "<https://api.prefect.io>"
       PREFECT_API_KEY: "<Your API Key>"
     resources:
       limits:
         cpu: "500m"
         memory: "512Mi"
       requests:
         cpu: "250m"
         memory: "256Mi"

5. Install Prefect Worker using Helm:

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bash
   helm install prefect-worker prefect/prefect-worker \
     --namespace prefect \
     -f values.yaml

This setup will deploy a Prefect worker on your Kubernetes cluster. For more detailed instructions, you can refer to the Prefect Kubernetes deployment guide.

Nideesh

01/08/2025, 6:40 AM

@Marvin this doesn't need me own PREFECT_API_KEY since im running on my own private k8 not gcp or aws

Marvin

01/08/2025, 6:40 AM

If you're running Prefect on your own private Kubernetes cluster and not using Prefect Cloud, you don't need to set the

PREFECT_API_KEY

. Instead, you can focus on deploying the Prefect server components directly. Here's a simplified approach: 1. Add the Prefect Helm Repository:

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bash
   helm repo add prefect <https://prefecthq.github.io/prefect-helm>
   helm repo update

2. Create a Kubernetes Namespace:

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bash
   kubectl create namespace prefect

3. Create a
values.yaml
file: This file will configure your Prefect server deployment. Here's a basic example:

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yaml
   server:
     image:
       repository: prefecthq/prefect-server
       tag: 3.0.0
     resources:
       limits:
         cpu: "500m"
         memory: "512Mi"
       requests:
         cpu: "250m"
         memory: "256Mi"

4. Install Prefect Server using Helm:

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bash
   helm install prefect-server prefect/prefect-server \
     --namespace prefect \
     -f values.yaml

This setup will deploy the Prefect server on your private Kubernetes cluster without requiring a Prefect Cloud API key. Adjust the

values.yaml

file as needed to fit your resource and configuration requirements.

Nideesh

01/08/2025, 6:41 AM

how to set a externalIP loadblancer ip here?

Nideesh

01/08/2025, 6:41 AM

@Marvin how to set a externalIP loadblancer ip here?

Marvin

01/08/2025, 6:41 AM

To set an external IP for a LoadBalancer in your Helm deployment of Prefect, you need to configure the

service

section in your

values.yaml

file. Here's how you can do it: 1. Edit your
values.yaml
file: Add or modify the

service

section to specify the

type

LoadBalancer

and set the

externalIPs

. Here's an example:

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yaml
   server:
     service:
       type: LoadBalancer
       externalIPs:
         - <Your-External-IP>

Replace

<Your-External-IP>

with the actual external IP address you want to use. 2. Apply the changes with Helm: After updating your

values.yaml

file, you can apply the changes using Helm:

Copy code

bash
   helm upgrade prefect-server prefect/prefect-server \
     --namespace prefect \
     -f values.yaml

This configuration will instruct Kubernetes to use the specified external IP for the LoadBalancer service associated with your Prefect server deployment. Make sure that the external IP is available and properly configured in your network environment.

Nideesh

01/08/2025, 6:42 AM

@Marvin give it all in 1 yaml for me

Marvin

01/08/2025, 6:42 AM

Here's a complete

values.yaml

configuration file for deploying Prefect 3 on a private Kubernetes cluster using Helm, with an external IP set for the LoadBalancer:

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect-server
    tag: 3.0.0
  resources:
    limits:
      cpu: "500m"
      memory: "512Mi"
    requests:
      cpu: "250m"
      memory: "256Mi"
  service:
    type: LoadBalancer
    externalIPs:
      - <Your-External-IP>  # Replace with your actual external IP

worker:
  image:
    repository: prefecthq/prefect
    tag: 3.0.0
  env:
    PREFECT_API_URL: "http://<Your-External-IP>:4200"  # Point to your Prefect server
  resources:
    limits:
      cpu: "500m"
      memory: "512Mi"
    requests:
      cpu: "250m"
      memory: "256Mi"

Replace

<Your-External-IP>

with the actual external IP address you wish to use. This configuration sets up both the Prefect server and worker with specified resources and a LoadBalancer service type.

Nideesh

01/08/2025, 6:44 AM

@Marvin its not letting me put service in the server part of the yaml

Marvin

01/08/2025, 6:45 AM

It seems there might be some confusion regarding where to place the

service

configuration in the Prefect Helm chart. In typical Helm charts, the service configuration is usually at the top level, not nested under specific components like

server

. Here's how you can structure your

values.yaml

file to set an external IP for the LoadBalancer:

Copy code

yaml
service:
  type: LoadBalancer
  externalIPs:
    - <Your-External-IP>  # Replace with your actual external IP

server:
  image:
    repository: prefecthq/prefect-server
    tag: 3.0.0
  resources:
    limits:
      cpu: "500m"
      memory: "512Mi"
    requests:
      cpu: "250m"
      memory: "256Mi"

worker:
  image:
    repository: prefecthq/prefect
    tag: 3.0.0
  env:
    PREFECT_API_URL: "http://<Your-External-IP>:4200"  # Point to your Prefect server
  resources:
    limits:
      cpu: "500m"
      memory: "512Mi"
    requests:
      cpu: "250m"
      memory: "256Mi"

Place the

service

configuration at the top level of your

values.yaml

file. This should allow you to set the external IP for the LoadBalancer correctly. Adjust the

PREFECT_API_URL

and other configurations as needed for your environment.

Nideesh

01/08/2025, 6:46 AM

@Marvin Error: UPGRADE FAILED: values don't meet the specifications of the schema(s) in the following chart(s): prefect-server: - service: Additional property externalIPs is not allowed root@nideesh:/kubernetes_config/prefect# vim values.yaml root@nideesh:/kubernetes_config/prefect# so maybe we are close? what else can be put?

Marvin

01/08/2025, 6:47 AM

It appears that the

externalIPs

field is not supported in the

service

configuration for the Prefect Helm chart you're using. Instead, you might need to configure the LoadBalancer service differently. Here's a more general approach to setting up a LoadBalancer with a specific IP: 1. Check if your Kubernetes provider supports specifying an external IP for a LoadBalancer. Some cloud providers allow you to specify an external IP via annotations or specific configurations. 2. Use Annotations: Depending on the Kubernetes provider, you might need to use annotations to specify the external IP. Here's an example configuration that might work if your provider supports it:

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yaml
   server:
     service:
       type: LoadBalancer
       annotations:
         <http://service.beta.kubernetes.io/aws-load-balancer-eip-allocations|service.beta.kubernetes.io/aws-load-balancer-eip-allocations>: "eipalloc-0123456789abcdef0"
       loadBalancerIP: <Your-External-IP>  # Use this if your provider supports it

Replace

<Your-External-IP>

with your desired external IP. Note that the annotations and

loadBalancerIP

field might vary based on your Kubernetes setup and provider. 3. Consult Your Provider's Documentation: If you're using a specific Kubernetes provider (like AWS, GCP, Azure), check their documentation for any specific requirements or annotations needed to set an external IP for a LoadBalancer. If these adjustments don't work, it may be necessary to consult the Prefect Helm chart documentation or community for more specific guidance related to your environment.

Nideesh

01/08/2025, 6:47 AM

@Marvin root@nideesh:/kubernetes_config/prefect# helm upgrade prefect-server prefecthq/prefect-server --namespace prefect -f values.yaml Error: UPGRADE FAILED: values don't meet the specifications of the schema(s) in the following chart(s): prefect-server: - service: Additional property loadBalancerIP is not allowed root@nideesh:/kubernetes_config/prefect#

Marvin

01/08/2025, 6:48 AM

It seems like the

loadBalancerIP

property is not supported in the Prefect Helm chart you're using. This limitation means you cannot directly specify an external IP in the

values.yaml

file for the LoadBalancer service. Here are a few alternative approaches you can consider: 1. Use a Service of Type LoadBalancer Without Specifying an IP: Simply configure the service as a LoadBalancer and let your Kubernetes provider automatically assign an external IP. This is the default behavior for many cloud providers.

Copy code

yaml
   server:
     service:
       type: LoadBalancer

2. Manually Assign an External IP: After deploying the service, you can manually assign an external IP if your Kubernetes setup allows it. This might involve using your cloud provider's console or CLI to associate an IP with the LoadBalancer. 3. Check Provider-Specific Annotations: Some providers allow you to use specific annotations to influence how LoadBalancers are created. Check your provider's documentation for any such options. If none of these options work, you might need to reach out to the Prefect community or your Kubernetes provider for more specific guidance tailored to your setup.

Nideesh

01/08/2025, 6:48 AM

@Marvin okay give me the yam now

Marvin

01/08/2025, 6:48 AM

Given the constraints and the inability to directly specify an external IP in the

values.yaml

file for the Prefect Helm chart, here's a simplified configuration that sets up a LoadBalancer without specifying an external IP. You'll need to rely on your Kubernetes provider to automatically assign the IP:

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yaml
server:
  image:
    repository: prefecthq/prefect-server
    tag: 3.0.0
  resources:
    limits:
      cpu: "500m"
      memory: "512Mi"
    requests:
      cpu: "250m"
      memory: "256Mi"
  service:
    type: LoadBalancer

worker:
  image:
    repository: prefecthq/prefect
    tag: 3.0.0
  env:
    PREFECT_API_URL: "http://<Automatically-Assigned-IP>:4200"  # Update this after deployment
  resources:
    limits:
      cpu: "500m"
      memory: "512Mi"
    requests:
      cpu: "250m"
      memory: "256Mi"

### Steps to Deploy: 1. Apply the Configuration: Deploy using Helm with the command:

Copy code

bash
   helm upgrade --install prefect-server prefecthq/prefect-server \
     --namespace prefect \
     -f values.yaml

2. Retrieve the External IP: After deployment, run the following command to get the external IP assigned to your LoadBalancer:

Copy code

bash
   kubectl get services -n prefect

3. Update Configuration: Once you have the external IP, update any configurations or environment variables that need to reference this IP, such as

PREFECT_API_URL

. This setup relies on your Kubernetes provider to handle the allocation of the external IP. Make sure to check the external IP after deployment and update any necessary configurations.

Nideesh

01/08/2025, 6:49 AM

@Marvin remove all that uneeded resource stuff

Marvin

01/08/2025, 6:49 AM

Here's a streamlined version of the

values.yaml

file without the resource specifications:

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect-server
    tag: 3.0.0
  service:
    type: LoadBalancer

worker:
  image:
    repository: prefecthq/prefect
    tag: 3.0.0
  env:
    PREFECT_API_URL: "http://<Automatically-Assigned-IP>:4200"  # Update this after deployment

### Deployment Instructions: 1. Deploy using Helm:

Copy code

bash
   helm upgrade --install prefect-server prefecthq/prefect-server \
     --namespace prefect \
     -f values.yaml

2. Check the External IP: Retrieve the external IP assigned by your Kubernetes provider with:

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bash
   kubectl get services -n prefect

3. Update Configurations: Once the external IP is known, update any necessary configurations or environment variables, like

PREFECT_API_URL

, to reflect this IP. This configuration will deploy the Prefect server and worker with a LoadBalancer service, allowing your Kubernetes provider to manage the external IP assignment.

Nideesh

01/08/2025, 6:50 AM

@Marvin server: image: repository: prefecthq/prefect-server service: type: LoadBalancer worker: image: repository: prefecthq/prefect env: PREFECT_API_URL: "http://192.168.10.38:4200" # Update this after deployment Error: UPGRADE FAILED: values don't meet the specifications of the schema(s) in the following chart(s): prefect-server: - server: Additional property service is not allowed root@nideesh:/kubernetes_config/prefect#

Marvin

01/08/2025, 6:50 AM

It seems that the

service

property is not directly configurable under the

server

section in the Prefect Helm chart. This indicates that the Helm chart might not support configuring the service type directly through the

values.yaml

file in the manner you attempted. Here's a revised approach without the

service

key, focusing on what you can configure:

Copy code

yaml
server:
  image:
    repository: prefecthq/prefect-server

worker:
  image:
    repository: prefecthq/prefect
  env:
    PREFECT_API_URL: "http://<Automatically-Assigned-IP>:4200"  # Update this after deployment

### Deployment Steps: 1. Deploy with Helm:

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bash
   helm upgrade --install prefect-server prefecthq/prefect-server \
     --namespace prefect \
     -f values.yaml

2. Manually Configure LoadBalancer: If the Helm chart does not expose service configuration, you may need to manually edit the service created by Helm to set it as a LoadBalancer and manage external IPs. You can do this through

kubectl edit

or by applying a custom service YAML. 3. Retrieve and Update External IP: After deploying, get the external IP using:

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bash
   kubectl get services -n prefect

Update the

PREFECT_API_URL

in your environment variables or configurations with the assigned external IP. If these steps do not resolve the issue, consider consulting the Prefect community or documentation for further guidance on customizing service configurations with your specific Helm chart version.

Nideesh

01/08/2025, 7:12 AM

@Marvin okay i was able to deploy it somehow. now how can i deploy the server with pip3 install requirements.txt?

Marvin

01/08/2025, 7:12 AM

To deploy the Prefect server and install additional Python dependencies from a

requirements.txt

file, you can follow these steps: 1. Ensure Python Environment: Make sure you have Python 3.9 or newer installed. It's recommended to use a virtual environment:

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bash
   python3 -m venv prefect-env
   source prefect-env/bin/activate

2. Install Prefect: Install Prefect using pip:

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bash
   pip install prefect

3. Install Additional Dependencies: Use

pip

to install the packages listed in your

requirements.txt

file:

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bash
   pip install -r requirements.txt

4. Run Prefect Server: Start the Prefect server if you're self-hosting:

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bash
   prefect server start

This setup will allow you to run a self-hosted Prefect server with additional Python dependencies installed as specified in your

requirements.txt

file. Adjust the paths and commands as necessary for your specific environment and setup.

Nideesh

01/08/2025, 7:12 AM

@Marvin no i meant as part of helm install

Marvin

01/08/2025, 7:13 AM

To include additional Python dependencies from a

requirements.txt

file as part of your Helm deployment for Prefect, you typically need to customize the Docker image used by the Helm chart. Here's a general approach to achieve this: 1. Create a Custom Docker Image: Extend the existing Prefect Docker image to include your additional Python dependencies. Create a

Dockerfile

like this:

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dockerfile
   FROM prefecthq/prefect:latest

   COPY requirements.txt .

   RUN pip install --no-cache-dir -r requirements.txt

Build and push your custom image to a container registry:

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bash
   docker build -t your-registry/prefect-custom:latest .
   docker push your-registry/prefect-custom:latest

2. Update
values.yaml
for Helm: In your

values.yaml

file, specify the custom image:

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yaml
   server:
     image:
       repository: your-registry/prefect-custom
       tag: latest

   worker:
     image:
       repository: your-registry/prefect-custom
       tag: latest

3. Deploy with Helm: Use Helm to deploy the Prefect server and worker with your custom image:

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bash
   helm upgrade --install prefect-server prefecthq/prefect-server \
     --namespace prefect \
     -f values.yaml

This approach ensures that your additional dependencies are included in the Prefect server and worker deployments. Adjust the Dockerfile and image paths as necessary for your environment.

Nideesh

01/08/2025, 7:14 AM

@Marvin okay ill do that later. first tell me how to pull in python code to execute from github

Marvin

01/08/2025, 7:14 AM

To execute Python code from a GitHub repository using Prefect, you can use the

prefect-github

integration. Here's a step-by-step guide: 1. Install Prefect GitHub Integration: First, install the

prefect-github

package:

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bash
   pip install -U prefect-github

2. Register GitHub Blocks: Register the block types in the

prefect-github

module to make them available:

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bash
   prefect block register -m prefect_github

3. Create a GitHub Credentials Block: If you're accessing a private repository, create a GitHub Credentials block. You can do this through code or the Prefect UI. Here's an example using Python:

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python
   from prefect_github import GitHubCredentials

   github_credentials = GitHubCredentials(
       access_token="your_github_access_token"
   )
   github_credentials.save(name="my-github-creds", overwrite=True)

4. Reference the GitHub Credentials in Deployment: When creating a deployment, reference the GitHub Credentials block. This can be done in your

prefect.yaml

file or programmatically. Example

prefect.yaml

snippet:

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yaml
   pull:
     - prefect_github.GitHubCredentials:
         name: my-github-creds
         repository: "your-repo-name"
         branch: "main"

5. Deploy and Execute: Use the Prefect CLI or UI to create and run the deployment. Prefect will pull the flow code from the specified GitHub repository. This setup allows you to manage and execute your Prefect flows with code stored in a GitHub repository, leveraging Prefect's integration with GitHub for seamless access. Adjust the repository and branch details as needed for your specific use case.

Nideesh

01/08/2025, 7:15 AM

@Marvin can this be automated liek the github blocks?

Marvin

01/08/2025, 7:15 AM

Yes, you can automate the creation and management of GitHub credentials and other Prefect blocks using Prefect's Python API. Here's how you can automate the process of setting up GitHub credentials and using them in your deployments: 1. Automate GitHub Credentials Creation: You can write a Python script to create and save GitHub credentials. This script can be run as part of a CI/CD pipeline or any automated workflow.

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python
   from prefect_github import GitHubCredentials

   # Create GitHub credentials block
   github_credentials = GitHubCredentials(
       access_token="your_github_access_token"
   )
   
   # Save the credentials block for later use
   github_credentials.save(name="my-github-creds", overwrite=True)

2. Automate Deployment Creation: You can also automate the creation of deployments that use these credentials. Here's an example of how you might set up a deployment using Prefect's Python API:

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python
   from prefect.deployments import Deployment
   from prefect_github import GitHubCredentials

   # Load the GitHub credentials block
   github_credentials = GitHubCredentials.load("my-github-creds")

   # Create a deployment with the GitHub credentials
   deployment = Deployment.build_from_flow(
       flow=my_flow_function,  # replace with your flow function
       name="my-github-deployment",
       parameters={"param1": "value1"},  # replace with actual parameters
       pull=[
           {
               "prefect_github.GitHubCredentials": {
                   "name": "my-github-creds",
                   "repository": "your-repo-name",
                   "branch": "main"
               }
           }
       ]
   )

   # Apply the deployment
   deployment.apply()

3. Integrate with CI/CD: You can integrate these scripts into your CI/CD pipeline to ensure that deployments are automatically updated whenever your code changes or credentials need to be refreshed. By using Prefect's Python API, you can automate the setup of GitHub credentials and the creation of deployments, streamlining your workflow and reducing manual intervention. Adjust the scripts to fit your specific requirements and environment.

Nideesh

01/08/2025, 7:19 AM

@Marvin i deployed prefect server on my person k8 how do i get python code on tere to run

Marvin

01/08/2025, 7:19 AM

To run Python code on your self-hosted Prefect server deployed on Kubernetes, follow these steps: 1. Set Up Your Prefect Environment: Ensure you have Python and Prefect installed in your local environment. You can use a virtual environment to manage dependencies:

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bash
   python3 -m venv prefect-env
   source prefect-env/bin/activate
   pip install -U prefect

2. Configure Prefect API URL: Set the

PREFECT_API_URL

to point to your self-hosted Prefect server. This URL should include the IP and port of your Prefect server's API:

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bash
   prefect config set PREFECT_API_URL=http://<Your-Prefect-Server-IP>:4200/api

3. Develop Your Prefect Flow: Write your Python code as a Prefect flow. Here's a simple example:

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python
   from prefect import flow, task

   @task
   def say_hello():
       print("Hello, world!")

   @flow
   def hello_flow():
       say_hello()

   if __name__ == "__main__":
       hello_flow()

4. Register and Deploy Your Flow: Use the Prefect CLI to register and deploy your flow to the Prefect server:

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bash
   prefect deployment build -n hello-flow-deployment hello_flow.py:hello_flow
   prefect deployment apply hello_flow-deployment.yaml

5. Run Your Flow: Trigger the flow run from the Prefect UI or using the CLI:

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bash
   prefect deployment run hello-flow-deployment

This process allows you to execute Python code on your Prefect server running on Kubernetes. Adjust the

PREFECT_API_URL

and flow details according to your specific setup and requirements.

Nideesh

01/08/2025, 7:26 AM

@Marvin no i mean is there a way form the UI of my prefect deployed on k8 i can pull int github code?

Marvin

01/08/2025, 7:26 AM

To execute Python code from a GitHub repository directly through the Prefect UI on your self-hosted Prefect server, you can use the Prefect GitHub integration. Here’s how you can set this up to pull code from GitHub and run it: 1. Set Up GitHub Integration: - Install the

prefect-github

package if you haven't already:

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bash
     pip install -U prefect-github

- Register the GitHub blocks:

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bash
     prefect block register -m prefect_github

2. Create GitHub Credentials Block via Prefect UI: - Go to the Prefect UI. - Navigate to the Blocks section and create a new GitHub Credentials block. - Provide your GitHub access token and any necessary details. 3. Create a Deployment with GitHub Code: - In the Prefect UI, create a new deployment. - Specify the repository and branch where your flow code resides. - Use the GitHub Credentials block you created to authenticate and pull the code. 4. Run Your Deployment: - Once the deployment is set up, you can trigger the flow run directly from the Prefect UI. - Prefect will pull the code from the specified GitHub repository and execute it on your Kubernetes cluster. This setup allows you to manage and execute flows stored in GitHub directly through the Prefect UI, leveraging the Prefect GitHub integration for seamless access. Adjust the repository and credentials settings as needed for your specific use case.

Nideesh

01/08/2025, 7:28 AM

@Marvin im on the blocks catalog but nothing is self populating? is there a way to use the helm install to auto include the blocks?

Marvin

01/08/2025, 7:28 AM

Currently, the Prefect Helm chart does not automatically include or register blocks like the GitHub integration blocks during installation. Blocks need to be registered manually after the Prefect server is deployed. Here’s what you can do to automate this process after deploying Prefect using Helm: 1. Automate Block Registration: After deploying your Prefect server, run a script to register the necessary blocks. This can be done using the Prefect CLI or a Python script. 2. Example Python Script for Block Registration: You can automate the block registration process with a Python script like this:

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python
   from prefect_github import GitHubCredentials

   # Register GitHub credentials block
   github_credentials = GitHubCredentials(
       access_token="your_github_access_token"
   )
   github_credentials.save(name="my-github-creds", overwrite=True)

   # Register other blocks as needed

3. Run the Script: Execute this script in an environment where Prefect is installed and configured to communicate with your Prefect server. This could be part of a CI/CD pipeline or a post-deployment script. 4. Manual Registration via Prefect UI: Alternatively, you can manually register blocks through the Prefect UI if automation is not feasible. This approach ensures that the necessary blocks are available in your Prefect server, enabling you to pull and execute code from GitHub repositories.

Nideesh

01/08/2025, 7:29 AM

@Marvin how do i manually register though the UI?the blocks catalog is empty

Marvin

01/08/2025, 7:29 AM

If the blocks catalog in your Prefect UI is empty, it might be because the blocks haven't been registered yet. Here's how you can manually register blocks through the Prefect UI or using the CLI: ### Manually Register Blocks via Prefect CLI 1. Install Prefect GitHub Integration: Ensure you have the

prefect-github

package installed in your local environment:

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bash
   pip install -U prefect-github

2. Register Blocks: Use the Prefect CLI to register the blocks available in `prefect-github`:

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bash
   prefect block register -m prefect_github

This command will make the GitHub blocks available in your Prefect server. ### Manually Register Blocks via Python Script If you prefer using a Python script, you can do so by running a script similar to this:

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python
from prefect_github import GitHubCredentials

# Create and register GitHub credentials block
github_credentials = GitHubCredentials(
    access_token="your_github_access_token"
)
github_credentials.save(name="my-github-creds", overwrite=True)

### Access Blocks in the Prefect UI After registering the blocks using either method: 1. Navigate to the Prefect UI: - Go to the Blocks section. - You should now see the registered blocks, such as GitHub credentials, available for use. 2. Create and Use Blocks: - Create instances of these blocks by providing necessary credentials or configurations. - Use these blocks in your deployments to pull and execute code from GitHub. If the blocks still don't appear, ensure that your Prefect server is correctly configured and that the CLI or script is pointing to the right server instance. Adjust the access token and other credentials as needed for your environment.

Nideesh

01/08/2025, 7:29 AM

@Marvin dont i have to redo this everytime pod restarts?

Marvin

01/08/2025, 7:29 AM

Yes, if you're running Prefect on a Kubernetes cluster and the blocks are registered in-memory, you would need to re-register them every time the Prefect server pod restarts. Here are a few strategies to manage this: 1. Persistent Storage for Blocks: Typically, blocks are stored in a database backing the Prefect server. Ensure that your Prefect server is configured to use a persistent database (e.g., PostgreSQL) rather than an in-memory database like SQLite. This way, block registrations persist across pod restarts. 2. Automate Block Registration: If you're using an ephemeral database, automate the block registration process. You can create a Kubernetes Job or an init container to run a script that registers blocks whenever the server starts. 3. Use a Persistent Database: Configure your Helm chart to use a persistent database. This ensures that all configurations, including registered blocks, persist across restarts. Check the Helm chart's documentation for instructions on configuring a persistent database. 4. Backup and Restore: Regularly back up your Prefect database and have a restoration process in place. This can help quickly restore the state in case of data loss. By ensuring that your Prefect server uses persistent storage, you can avoid the need to re-register blocks after each restart. If you're not already using a persistent database, consider setting one up to manage Prefect's state more effectively.

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