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<@ULVA73B9P> I am using `prefect 3.2.6` and curren...
# ask-marvin
t
@Marvin I am using 
prefect 3.2.6
and currently got issues with prefect UI from outside of the VM. Example from my 
docker-compose.yml
as
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services:
  prefect-ui:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
    environment:
      PREFECT_UI_PORT: 4200
      PREFECT_UI_HOST: 0.0.0.0
then I put in 
.env
file
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PREFECT_UI_HOST=0.0.0.0
PREFECT_UI_PORT=4200
but when I run 
docker compose up -d
the service seems running well, but I could not access to the UI using the current IP from my VM. e.g. 
<http://x.y.z.w:4200/>
What was wrong here?
m
✅ thought for 78.7 seconds
Based on your configuration and the research, I can see a couple of potential issues: 1. Missing API Configuration: The most important missing piece is the 
PREFECT_API_URL
configuration. When running Prefect server in Docker, you need to tell both the server and UI where to find the API. Add these to your environment variables:
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yaml
environment:
  PREFECT_UI_PORT: 4200
  PREFECT_UI_HOST: 0.0.0.0
  PREFECT_API_URL: "<http://x.y.z.w:4200/api>"
2. Server Command Configuration: Instead of just 
--port
, you should explicitly set both host and port in the command. Modify your command to:
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yaml
command: prefect server start --host 0.0.0.0 --port 4200
Here's your corrected `docker-compose.yml`:
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yaml
services:
  prefect-ui:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
    environment:
      PREFECT_UI_PORT: 4200
      PREFECT_UI_HOST: 0.0.0.0
      PREFECT_API_URL: "<http://x.y.z.w:4200/api>"
And in your 
.env
file:
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PREFECT_UI_HOST=0.0.0.0
PREFECT_UI_PORT=4200
PREFECT_API_URL=<http://x.y.z.w:4200/api>
Additional troubleshooting steps: 1. Verify Port Access: - Make sure port 4200 is open in your VM's firewall - You can check this using: 
sudo ufw status
(for Ubuntu) or 
firewall-cmd --list-all
(for CentOS/RHEL) 2. Test Container Access: - After starting the container, you can verify the server is listening properly:
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bash
   docker logs <container-name>
- You should see messages indicating the server started successfully 3. Network Check: - Try accessing the UI from within the VM first using 
curl <http://localhost:4200>
- If that works but external access doesn't, it's likely a firewall/networking issue If after implementing these changes you still can't access the UI, please: 1. Share any error messages you see in the Docker logs 2. Confirm whether you can access the UI from within the VM itself 3. Verify if there are any firewall rules that might be blocking access to port 4200
t
@Marvin Thank you. I still wonder such a IP address of the host VM needed to be defined inside the .env due to IT security issue. There is no need to add the block
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environment:
      PREFECT_UI_PORT: 4200
      PREFECT_UI_HOST: 0.0.0.0
      PREFECT_API_URL: "<http://x.y.z.w:4200/api>"
if we already have
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env_file:
      - .env
correct?
m
✅ thought for 7.8 seconds
Ah yes, you're absolutely right! Let me clarify this: 1. Redundant Configuration: You are correct - if you're using an 
env_file
, you don't need to duplicate the environment variables in the 
environment
block. That would be redundant, and it's cleaner to keep all environment variables in the 
.env
file. Here's the cleaner `docker-compose.yml`:
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yaml
services:
  prefect-ui:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
And your 
.env
file should contain:
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PREFECT_UI_HOST=0.0.0.0
PREFECT_UI_PORT=4200
PREFECT_API_URL=<http://x.y.z.w:4200/api>
Regarding the IT security issue about hardcoding IP addresses: Yes, it's generally better practice to: 1. Either use environment variables that are set by your deployment process 2. Or use DNS names instead of IP addresses if available 3. Or let the API URL be configured by the client rather than hardcoding it in the server You could also consider using relative URLs if your setup allows it, which would avoid the need to specify the host altogether. However, this depends on your specific networking setup and how you're accessing the UI. Would you like me to research more about secure configuration patterns for the Prefect server in enterprise environments?
t
@Marvin Many thanks. I liked the option 2. I am building relatively local service for now (no cloud service yet),but would prefer to hear more about option 3 if you could explain abit
m
✅ thought for 43.3 seconds
Let me explain Option 3 (client-side configuration) in more detail: Instead of hardcoding the API URL in your server's 
.env
file, you can configure it on the client side. This approach offers several benefits: 1. Flexible Client Configuration: Each client (whether it's a worker, flow runner, or someone accessing the UI) can configure their own connection to the Prefect server using either: - Environment variables on the client machine:
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bash
     export PREFECT_API_URL="<http://your-server:4200/api>"
- Or using Prefect profiles (recommended):
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bash
     prefect profile create local-dev
     prefect config set PREFECT_API_URL="<http://your-server:4200/api>"
2. Profile-Based Management: You can create different profiles for different environments:
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bash
   # Create and configure a development profile
   prefect profile create dev
   prefect config set --profile dev PREFECT_API_URL="<http://dev-server:4200/api>"
   
   # Create and configure a production profile
   prefect profile create prod
   prefect config set --profile prod PREFECT_API_URL="<http://prod-server:4200/api>"
3. Simplified Server Configuration: Your server's 
.env
file becomes simpler:
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PREFECT_UI_HOST=0.0.0.0
   PREFECT_UI_PORT=4200
4. Security Benefits: - No hardcoded URLs in your server configuration - Each client can have its own authentication settings - Easier to manage different access patterns (internal vs external access) To implement this approach: 1. On your server, keep minimal configuration:
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yaml
# docker-compose.yml
services:
  prefect-ui:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
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# .env
PREFECT_UI_HOST=0.0.0.0
PREFECT_UI_PORT=4200
2. On each client machine that needs to interact with the server:
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bash
# Create a profile for this environment
prefect profile create my-local
prefect config set PREFECT_API_URL="<http://your-server-dns:4200/api>"
prefect profile use my-local
This approach gives you: - More flexibility in how different clients connect to the server - Better security by not hardcoding URLs - Easier management of multiple environments - Cleaner server configuration The main trade-off is that you need to configure each client, but this is often a benefit as it gives you more control and flexibility over how different clients connect to your Prefect server. Would you like me to explain more about how to manage multiple profiles or how to set up secure client-side configurations?
t
@Marvin no thank you. It is clear for me
m
✅ thought for 3.0 seconds
Great! Glad I could help clarify the client-side configuration approach. If you run into any other issues with Prefect server configuration or need help with other aspects of Prefect, feel free to ask. Good luck with your local service setup!
t
@Marvin I have a workflow compling multiple 
flows.py
files which I still working on. For the deployment, I will have 
deploy.py
, 
Dockerfile
and 
docker-compose.yml
file which as defined as above. Can I get the 
docker compose up -d
with only the server on it, so that I can at least get the prefect server running and access from outside, in the meanwhile, I still can work and test the single 
flow.py
m
✅ thought for 9.3 seconds
Yes, absolutely! This is actually a good development practice - running the Prefect server separately while developing and testing your flows. Let me explain how to do this: 1. First, keep your server-only `docker-compose.yml`:
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yaml
services:
  prefect-ui:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
2. Run the server:
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bash
docker compose up -d
3. On your local development machine, configure your client to point to this server:
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bash
prefect config set PREFECT_API_URL="<http://your-server:4200/api>"
4. Now you can develop and test your flows locally. For example, to test a single flow:
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python
# test_flow.py
from prefect import flow

@flow
def my_test_flow():
    print("Flow is running!")

if __name__ == "__main__":
    my_test_flow()
You can run this directly with:
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bash
python test_flow.py
The flow will execute locally but will be registered with your running Prefect server, and you'll be able to see its runs in the UI. Later, when your flows are ready for deployment: 1. You can create your 
deploy.py
2. Set up your deployment 
Dockerfile
3. Either add the deployment services to your existing 
docker-compose.yml
or create a separate one for deployments This separation allows you to: - Keep the server running continuously - Develop and test flows independently - See your test runs in the UI - Iterate quickly on your flows without needing to rebuild containers - Deploy flows when they're ready Would you like me to provide more details about any part of this setup, such as how to structure your deployment files when you're ready for that stage?
t
@Marvin I wonder the role of 
Dockerfile
in this case. Would it then entcapsulate the 
service
defined in the 
docker-compose.yml
and get it run as daemon on the server?
m
✅ thought for 39.2 seconds
Let me clarify the distinct roles: 1. 
Dockerfile
 is used to: - Define the environment for your flow code and its dependencies - Create a reproducible image that contains your flow code - Specify the Python version, required packages, and any system dependencies Example 
Dockerfile
for your flows:
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dockerfile
FROM prefecthq/prefect:3.2.6-python3.11-conda

# Copy your flow code and requirements
COPY requirements.txt .
COPY flows/ /opt/prefect/flows/

# Install dependencies
RUN pip install -r requirements.txt

# Set working directory
WORKDIR /opt/prefect/flows
2. 
docker-compose.yml
 is used to: - Define and orchestrate multiple services - Configure how these services interact - Manage environment variables and networking - Run services as daemons Your current 
docker-compose.yml
(server only):
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yaml
services:
  prefect-ui:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
When you're ready to deploy your flows, you might expand this to include worker services:
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yaml
services:
  prefect-ui:
    # ... (your existing server configuration)

  worker:
    build:
      context: .
      dockerfile: Dockerfile    # Uses your flow Dockerfile
    command: prefect worker start -p 'my-pool'
    environment:
      PREFECT_API_URL: <http://prefect-ui:4200/api>
    depends_on:
      - prefect-ui
The key differences are: - 
Dockerfile
defines HOW to build your flow's environment - 
docker-compose.yml
defines HOW to run and connect services Typical workflow: 1. 
Dockerfile
builds your flow image with all dependencies 2. 
docker-compose.yml
uses this image to run workers that execute your flows 3. The server (also defined in 
docker-compose.yml
) orchestrates everything So no, the 
Dockerfile
doesn't encapsulate the services - rather, 
docker-compose.yml
uses the image built by your 
Dockerfile
to run services. The 
docker-compose.yml
is what manages the daemon aspect of your services. Would you like me to provide more specific examples of how to structure these files for your particular use case?
t
@Marvin Thank you Can you give me example of docker-compose.yml file where define both server and work pool for prefect and keep them running
m
✅ thought for 29.3 seconds
Here's a complete 
docker-compose.yml
that sets up both the Prefect server and a worker pool:
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yaml
services:
  prefect-server:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
    healthcheck:
      test: ["CMD", "curl", "-f", "<http://localhost:4200/api/health>"]
      interval: 10s
      timeout: 5s
      retries: 5
    networks:
      - prefect-network

  prefect-worker:
    build:
      context: .
      dockerfile: Dockerfile    # Your Dockerfile with flow code and dependencies
    command: bash -c "prefect work-pool create -n 'my-pool' -t process || true && prefect worker start -p 'my-pool'"
    env_file:
      - .env
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    depends_on:
      prefect-server:
        condition: service_healthy
    networks:
      - prefect-network

networks:
  prefect-network:
    driver: bridge
And the corresponding 
.env
file:
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PREFECT_UI_HOST=0.0.0.0
PREFECT_UI_PORT=4200
Key features of this setup: 1. Server Service (
prefect-server
): - Runs the Prefect UI and API server - Exposes port 4200 - Includes a healthcheck to ensure the server is ready before starting workers 2. Worker Service (
prefect-worker
): - Uses your custom Dockerfile with flow code - Creates a work pool named 'my-pool' if it doesn't exist - Starts a worker that connects to that pool - Waits for the server to be healthy before starting 3. Networking: - Both services are connected via a Docker network - Worker can reach the server using the service name as hostname To use this: 1. Make sure you have your 
Dockerfile
ready with your flow code:
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dockerfile
FROM prefecthq/prefect:3.2.6-python3.11-conda

# Copy your flow code and requirements
COPY requirements.txt .
COPY flows/ /opt/prefect/flows/

# Install dependencies
RUN pip install -r requirements.txt

# Set working directory
WORKDIR /opt/prefect/flows
2. Start everything:
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bash
docker compose up -d
3. Check the status:
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bash
docker compose ps
docker compose logs -f
The services will: - Start automatically when you run 
docker compose up -d
- Restart automatically if they crash (default Docker behavior) - Be accessible via the Prefect UI at 
<http://localhost:4200>
To stop everything:
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bash
docker compose down
Some tips: - You can scale workers if needed: 
docker compose up -d --scale prefect-worker=3
- Monitor logs: 
docker compose logs -f prefect-worker
- Access the server: 
docker compose logs -f prefect-server
Would you like me to explain any part of this configuration in more detail?
t
@Marvin what if my prefect server is confirmed to be unhealthy and how to overcome this? 
dependency failed to start: container test-prefect-server-1 is unhealthy
m
✅ thought for 16.5 seconds
Let's troubleshoot the unhealthy server status. First, let's check what might be causing this issue. 1. First, let's check the server logs directly:
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bash
docker compose logs prefect-server
Common causes for unhealthy status include: 1. Port Conflicts: If port 4200 is already in use 2. Permission Issues: If the container can't write to required directories 3. Network Issues: If the healthcheck can't reach the API 4. Memory/Resource Issues: If the container doesn't have enough resources Let's modify the 
docker-compose.yml
with better debugging capabilities and more resilient configuration:
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yaml
services:
  prefect-server:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
    environment:
      - PREFECT_SERVER_API_HOST=0.0.0.0
      - PREFECT_LOGGING_LEVEL=DEBUG  # Increased logging for debugging
    healthcheck:
      test: ["CMD", "curl", "-f", "<http://localhost:4200/api/health>"]
      interval: 30s      # Increased interval
      timeout: 10s      # Increased timeout
      retries: 5
      start_period: 30s # Give more time for initial startup
    restart: unless-stopped
    networks:
      - prefect-network
    # Optional: Add resource limits if needed
    # mem_limit: 1g
    # cpu_limit: 1.0

networks:
  prefect-network:
    driver: bridge
Try these troubleshooting steps: 1. Clean up existing containers and volumes:
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bash
docker compose down -v
docker compose rm -f
2. Check if port 4200 is already in use:
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bash
sudo lsof -i :4200
# or
netstat -tuln | grep 4200
If it's in use, either free the port or change to a different port in your compose file. 3. Try running the server without healthcheck first:
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yaml
services:
  prefect-server:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
    environment:
      - PREFECT_SERVER_API_HOST=0.0.0.0
      - PREFECT_LOGGING_LEVEL=DEBUG
    restart: unless-stopped
    networks:
      - prefect-network
4. Verify the container can reach itself: Add curl to the container and test manually:
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yaml
services:
  prefect-server:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: >
      bash -c "apt-get update && apt-get install -y curl && 
      prefect server start --host 0.0.0.0 --port 4200"
    # ... rest of the configuration
5. Check container status and logs:
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bash
# Check container status
docker ps -a

# Check detailed container info
docker inspect test-prefect-server-1

# Follow logs in real-time
docker compose logs -f prefect-server
6. Verify network connectivity:
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bash
# Enter the container
docker compose exec prefect-server bash

# Inside container, test API
curl <http://localhost:4200/api/health>
7. Memory and CPU Usage:
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bash
docker stats test-prefect-server-1
Could you please: 1. Run 
docker compose logs prefect-server
and share any error messages you see? 2. Try accessing 
<http://localhost:4200>
directly from your host machine and let me know if it responds? 3. Check if there are any firewall rules that might be blocking the healthcheck? This will help me provide more specific guidance for your situation.
t
@Marvin each time when I run 
docker compose down
and 
up
again, my 
prefect-ui
defined in the 
prefect-server
no longer store my old pools, flows and runs... How can I building this keeping all old works still displayed
m
✅ thought for 11.0 seconds
Ah, this happens because the Prefect server's data isn't being persisted between container restarts. We need to add a volume to store the SQLite database that Prefect uses by default. Let me help you modify your 
docker-compose.yml
to persist the data:
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yaml
services:
  prefect-server:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
    environment:
      - PREFECT_SERVER_API_HOST=0.0.0.0
    volumes:
      - prefect_data:/root/.prefect
    healthcheck:
      test: ["CMD", "curl", "-f", "<http://localhost:4200/api/health>"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    networks:
      - prefect-network

  prefect-worker:
    build:
      context: .
      dockerfile: Dockerfile
    command: bash -c "prefect work-pool create -n 'my-pool' -t process || true && prefect worker start -p 'my-pool'"
    env_file:
      - .env
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    depends_on:
      prefect-server:
        condition: service_healthy
    networks:
      - prefect-network

volumes:
  prefect_data:    # Define the named volume
    name: prefect_data    # Optional: specify a custom name for the volume

networks:
  prefect-network:
    driver: bridge
Key changes: 1. Added a named volume 
prefect_data
2. Mounted it to 
/root/.prefect
in the container where Prefect stores its SQLite database and other data Now when you run:
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bash
docker compose down
docker compose up -d
Your data will persist because it's stored in a Docker volume. Important notes: 1. Don't use 
docker compose down -v
as this will remove the volumes 2. To completely start fresh, you would need to explicitly remove the volume:
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bash
   docker compose down
   docker volume rm prefect_data
3. To check your volumes:
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bash
   docker volume ls
4. To inspect the volume:
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bash
   docker volume inspect prefect_data
If you want even more robust data persistence, you could consider using PostgreSQL instead of SQLite. Would you like me to show you how to configure that as well?
t
@Marvin My server still running, but it return the status
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dependency failed to start: container outlast-prefect-server-1 is unhealthy
Having a look at the 
docker compose logs -f prefect-server
, I see this error
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"Health": {
                "Status": "unhealthy",
                "FailingStreak": 39,
                "Log": [
                    {
                        "Start": "2025-08-05T15:11:22.540807318+02:00",
                        "End": "2025-08-05T15:11:22.585307869+02:00",
                        "ExitCode": -1,
                        "Output": "OCI runtime exec failed: exec failed: unable to start container process: exec: \"curl\": executable file not found in $PATH: unknown"
                    },
                    {
                        "Start": "2025-08-05T15:11:52.585807093+02:00",
                        "End": "2025-08-05T15:11:52.634417093+02:00",
                        "ExitCode": -1,
                        "Output": "OCI runtime exec failed: exec failed: unable to start container process: exec: \"curl\": executable file not found in $PATH: unknown"
                    },
                    {
                        "Start": "2025-08-05T15:12:22.635462386+02:00",
                        "End": "2025-08-05T15:12:22.679534873+02:00",
                        "ExitCode": -1,
                        "Output": "OCI runtime exec failed: exec failed: unable to start container process: exec: \"curl\": executable file not found in $PATH: unknown"
                    },
                    {
                        "Start": "2025-08-05T15:12:52.680107775+02:00",
                        "End": "2025-08-05T15:12:52.728335655+02:00",
                        "ExitCode": -1,
                        "Output": "OCI runtime exec failed: exec failed: unable to start container process: exec: \"curl\": executable file not found in $PATH: unknown"
                    },
                    {
                        "Start": "2025-08-05T15:13:22.72883059+02:00",
                        "End": "2025-08-05T15:13:22.775616227+02:00",
                        "ExitCode": -1,
                        "Output": "OCI runtime exec failed: exec failed: unable to start container process: exec: \"curl\": executable file not found in $PATH: unknown"
                    }
                ]
            }
        },
m
✅ thought for 12.6 seconds
Ah, I see the issue! The healthcheck is failing because 
curl
is not installed in the container. Let's modify the 
docker-compose.yml
to either install 
curl
or use a different healthcheck method. Here are two solutions: Solution 1 - Install curl when container starts:
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yaml
services:
  prefect-server:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: >
      bash -c "apt-get update && apt-get install -y curl &&
      prefect server start --host 0.0.0.0 --port 4200"
    ports:
      - "4200:4200"
    env_file:
      - .env
    environment:
      - PREFECT_SERVER_API_HOST=0.0.0.0
    volumes:
      - prefect_data:/root/.prefect
    healthcheck:
      test: ["CMD", "curl", "-f", "<http://localhost:4200/api/health>"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    networks:
      - prefect-network
OR Solution 2 - Use Python for healthcheck (preferred since Python is already in the image):
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yaml
services:
  prefect-server:
    image: prefecthq/prefect:3.2.6-python3.11-conda
    command: prefect server start --host 0.0.0.0 --port 4200
    ports:
      - "4200:4200"
    env_file:
      - .env
    environment:
      - PREFECT_SERVER_API_HOST=0.0.0.0
    volumes:
      - prefect_data:/root/.prefect
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('<http://localhost:4200/api/health>')"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    networks:
      - prefect-network

  prefect-worker:
    # ... rest of your worker configuration ...

volumes:
  prefect_data:
    name: prefect_data

networks:
  prefect-network:
    driver: bridge
Try these steps: 1. Stop your current containers:
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bash
docker compose down
2. Use either of the above configurations (I recommend Solution 2 since it uses Python) 3. Start the services again:
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bash
docker compose up -d
4. Check the logs to verify the healthcheck is working:
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bash
docker compose logs -f prefect-server
The Python-based healthcheck (Solution 2) is better because: - It uses Python which is already in the container - No need to install additional packages - More lightweight - Still accomplishes the same health verification Let me know if you still see any issues with the healthcheck after making these changes!
t
@Marvin option 2 works perfectly. Thanks
m
✅ thought for 13.1 seconds
Great to hear that Solution 2 with the Python-based healthcheck is working! This is indeed the more elegant solution since it uses what's already available in the container. Just to recap what you now have: - A persistent Prefect server (thanks to the volume mounting) - A reliable healthcheck using Python's built-in urllib - No unnecessary additional packages If you need any help with other aspects of your Prefect setup, such as: - Setting up additional workers - Configuring deployments - Managing work pools - Or any other Prefect-related topics Feel free to ask!
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