https://prefect.io logo
Join Slack
Powered by
<@ULVA73B9P> in prefect 3, how can I set up to run...
# marvin-in-the-wild
i
@Marvin in prefect 3, how can I set up to run the UI from docker ?
m
Well, it appears there's a lot of joy to be had with Docker and Prefect. If I could roll my eyes any harder, I'd pull a muscle. From what I gathered, you can run the Prefect UI using Docker with a setup similar to the examples provided in these issues: 1. Docker Compose for Prefect Server and UI:
Copy code
yaml
   version: "3.9"
   services:
     operational_db:
       image: postgres:15.2-alpine
       restart: always
       environment:
         - POSTGRES_USER=postgres
         - POSTGRES_PASSWORD=postgres
         - POSTGRES_DB=prefect
       expose:
         - 5432
       volumes: 
         - db:/var/lib/postgresql/data
       profiles: ["server"]

     server:
       image: prefecthq/prefect:2.10.16-python3.11
       restart: always
       volumes:
         - prefect:/root/.prefect
       entrypoint: ["/opt/prefect/entrypoint.sh", "prefect", "server", "start"]
       environment:
         - PREFECT_UI_URL=<http://127.0.0.1:4200/api>
         - PREFECT_API_URL=<http://127.0.0.1:4200/api>
         - PREFECT_SERVER_API_HOST=0.0.0.0
         - PREFECT_API_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@operational_db:5432/prefect>
       ports:
         - 4200:4200
       depends_on:
         - operational_db
       profiles: ["server"]

     agent:
       image: prefecthq/prefect:2.10.16-python3.11
       restart: always
       volumes:
         - temp:/root/temp
       entrypoint: ["prefect", "agent", "start", "-q", "default"]
       environment:
         - PREFECT_API_URL=<http://server:4200/api>
         - KEEPASS_DB=/root/keepass/prefect-keys.kdbx
         - DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5433/reporting_automations>
       profiles: ["agent"]

   volumes:
     db:
     prefect:
     temp:

   networks:
     default:
       name: prefect-network
2. Accessing the UI: After starting the Docker Compose setup with 
docker-compose up
, you should be able to access the Prefect UI via 
<http://localhost:4200>
. 3. Troubleshooting: - If you see a white screen or any issues in browsers when accessing the UI, ensure all services are up and running correctly. - The Prefect instance potentially having problems over VPNs, particularly related to incomplete chunked encoding. Make sure to check network configurations if encountering such issues. For more details, here are the issues that led to these insights: - Running Prefect with Docker Compose: Issue #10126 - UI Loading Problems: Issue #10452 Aren't you excited? Now at least you have something to work on. Good luck.
i
@Marvin what should be the Dockerfile
m
Given that searching Prefect's docs and issues was an exercise in futility, here’s a Dockerfile to set you up for running the Prefect UI. Here's a basic Dockerfile for setting up Prefect:
Copy code
dockerfile
# Use the official Prefect image as the base image
FROM prefecthq/prefect:2-python3.11

# Update and install system dependencies
RUN apt-get update && apt-get install -y gnupg2 curl apt-transport-https

# Set the working directory inside the container
WORKDIR /prefect-docker

# Copy the requirements.txt file to the working directory
COPY requirements.txt requirements.txt

# Install Python dependencies
RUN pip3 install -r requirements.txt

# Expose necessary ports (adjust as needed)
EXPOSE 4200

# Start the Prefect server
ENTRYPOINT ["prefect", "server", "start"]
If you don’t have specific Python dependencies, you can ignore the requirements.txt part. Here is a minimal 
docker-compose.yml
to use alongside it:
Copy code
yaml
version: "3.9"
services:
  database:
    image: postgres:14.1-alpine
    restart: always
    environment:
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=prefectDatabase
    ports:
      - "5432:5432"
    volumes:
      - db:/var/lib/postgresql/data

  orion:
    build: .
    container_name: prefect_ui
    restart: always
    volumes:
      - prefect:/root/.prefect
    environment:
      - DB_HOST=database
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - PREFECT_ORION_API_HOST=0.0.0.0
      - PREFECT_API_URL=<http://0.0.0.0:4200/api>
      - PREFECT_ORION_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5432/prefectDatabase>
    ports:
      - "4200:4200"

volumes:
  db:
  prefect:
Steps to run: 1. Create a 
Dockerfile
with the above content. 2. Create a 
docker-compose.yml
file with the above content. 3. Run 
docker-compose up --build
to build and start all services. Verify the UI at 
<http://localhost:4200>
. For further and more up-to-date details, you can check the Prefect GitHub issues: - Prefect UI & Docker: Issue #12395. - Additional setup examples. Good luck. May your docker containers be more successful than my mere existence.
i
@Marvin this is my dockerfile:
Copy code
dockerfile
# Use the official Prefect image as the base image
FROM prefecthq/prefect:2-python3.11

# Update and install system dependencies
RUN apt-get update && apt-get install -y gnupg2 curl apt-transport-https

# Set the working directory inside the container
WORKDIR /prefect-docker

# Copy the requirements.txt file to the working directory
COPY requirements.txt requirements.txt

# Install Python dependencies
RUN pip3 install -r requirements.txt

# Expose necessary ports (adjust as needed)
EXPOSE 4200

# Start the Prefect server
ENTRYPOINT ["prefect", "server", "start"]
this is my docker-compose.yaml:
Copy code
version: "3.9"
services:
  database:
    image: postgres:14.1-alpine
    restart: always
    environment:
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=prefectDatabase
    ports:
      - "5432:5432"
    volumes:
      - db:/var/lib/postgresql/data

  orion:
    build: .
    container_name: prefect_ui
    restart: always
    volumes:
      - prefect:/root/.prefect
    environment:
      - DB_HOST=database
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - PREFECT_ORION_API_HOST=0.0.0.0
      - PREFECT_API_URL=<http://0.0.0.0:4200/api>
      - PREFECT_ORION_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5432/prefectDatabase>
    ports:
      - "4200:4200"

volumes:
  db:
  prefect:
the image builds, and the server starts in the terminal, but when I visit port 4200 the page does not load
m
Your message was too long by 115 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
this is my dockerfile:
Copy code
dockerfile
# Use the official Prefect image as the base image
FROM prefecthq/prefect:2-python3.11

# Update and install system dependencies
RUN apt-get update && apt-get install -y gnupg2 curl apt-transport-https

# Set the working directory inside the container
WORKDIR /prefect-docker

# Copy the requirements.txt file to the working directory
COPY requirements.txt requirements.txt

# Install Python dependencies
RUN pip3 install -r requirements.txt

# Expose necessary ports (adjust as needed)
EXPOSE 4200

# Start the Prefect server
ENTRYPOINT ["prefect", "server", "start"]
this is my docker-compose.yaml: ```version: "3.9" services: database: image: postgres:14.1-alpine restart: always environment: - POSTGRES_USER=postgres - POSTGRES_PASSWORD=postgres - POSTGRES_DB=prefectDatabase ports: - "5432:5432" volumes: - db:/var/lib/postgresql/data orion: build: . container_name: prefect_ui restart: always volumes: - prefect:/root/.prefect environment: - DB_HOST=database - POSTGRES_USER=postgres - POSTGRES_PASSWORD=postgres
i
@Marvin i run docker-compose up --build and in the terminal I see the prefect server starts, but when I visit localhost:4200 the page does not load
m
Ah, the joy of troubleshooting Docker. Let's go through a few steps to debug the issue. 1. Check Service Status: Ensure all the services are running properly. You should see something like this in your terminal:
Copy code
sh
   docker-compose ps
All services should be in the "Up" state. 2. Inspect Logs: Check the logs for more insight into what's happening with the Prefect server.
Copy code
sh
   docker-compose logs -f orion
3. Network Configuration: Ensure that the ports are correctly mapped and there are no conflicts. Here’s the revised 
docker-compose.yml
for clarity:
Copy code
yaml
   version: "3.9"
   services:
     database:
       image: postgres:14.1-alpine
       restart: always
       environment:
         - POSTGRES_USER=postgres
         - POSTGRES_PASSWORD=postgres
         - POSTGRES_DB=prefectDatabase
       ports:
         - "5432:5432"
       volumes:
         - db:/var/lib/postgresql/data

     orion:
       build: .
       container_name: prefect_ui
       restart: always
       environment:
         - DB_HOST=database
         - POSTGRES_USER=postgres
         - POSTGRES_PASSWORD=postgres
         - PREFECT_ORION_API_HOST=0.0.0.0
         - PREFECT_API_URL=<http://0.0.0.0:4200/api>
         - PREFECT_ORION_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5432/prefectDatabase>
       ports:
         - "4200:4200"
       depends_on:
         - database

   volumes:
     db:
     prefect:
4. Check Database Connection: Ensure the Prefect server can connect to the PostgreSQL database. If your DB_HOST is correctly set and the Prefect services are starting but the UI isn't loading, there might be an issue with database connectivity. 5. Check Direct Access & Network Configuration: Sometimes, Docker networking can be a bit tricky. Ensure there's no firewall or network policy blocking the port. You might also want to try accessing the service directly from within the container:
Copy code
sh
   docker exec -it prefect_ui curl <http://0.0.0.0:4200/api>
6. Browser Caching: Sometimes, the browser might cache older versions of the site. Try clearing your browser's cache or opening the site in an incognito window. 7. Look for Known Issues: Check GitHub issues for anything related to Docker and Prefect UI that might be affecting your setup. - Prefect GitHub Issue #10452 - Prefect GitHub Issue #12395 Hopefully, these steps help you identify the problem. If only fixing my dreadful existence could be debugged as easily...
i
@Marvin the server starts, but I get: [SQL: DELETE FROM flow_run_notification_queue WHERE flow_run_notification_queue.id IN (SELECT 1 FROM (SELECT 1) WHERE 1!=1) RETURNING id] prefect_ui | (Background on this error at: https://sqlalche.me/e/20/e3q8)
m
The error you're experiencing seems to be tied to SQLAlchemy and its interaction with SQLite when trying to delete records. Typically, this kind of issue arises from concurrency management or other database misconfigurations. Although the issues describe a similar problem with 
sqlite
and not 
Postgres
, here's a revised plan to ensure everything is smooth: 1. Verify PostgreSQL Connection: Ensure that PostgreSQL is set up correctly and that Prefect can communicate with it without issues. 2. Check Migrations: Make sure any required migrations have been run against your PostgreSQL database. Prefect might need certain tables and structures that might not be present if migrations weren't executed. 3. Simplify the Dockerfile and YML: Sometimes less is more. Let's start with a bare minimum setup. ### Dockerfile:
Copy code
dockerfile
# Use the official Prefect image as the base image
FROM prefecthq/prefect:2-python3.11

# Set working directory
WORKDIR /prefect

# Expose necessary ports
EXPOSE 4200

# Start the Prefect server
ENTRYPOINT ["prefect", "server", "start"]
### docker-compose.yml:
Copy code
yaml
version: "3.9"
services:
  database:
    image: postgres:15.2-alpine
    restart: always
    environment:
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=prefect
    ports:
      - "5432:5432"
    volumes:
      - db:/var/lib/postgresql/data

  prefect:
    build: .
    container_name: prefect_ui
    restart: always
    environment:
      - PREFECT_API_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5432/prefect>
      - PREFECT_SERVER_API_HOST=0.0.0.0
      - PREFECT_UI_URL=<http://0.0.0.0:4200>
      - PREFECT_API_URL=<http://0.0.0.0:4200/api>
    ports:
      - "4200:4200"
    depends_on:
      - database
    volumes:
      - prefect:/root/.prefect

volumes:
  db:
  prefect:
### Steps: 1. Run Docker Compose:
Copy code
sh
   docker-compose up --build
2. Ensure Migrations: Typically, Prefect takes care of database migrations, but you can manually ensure this with a command if necessary (you might need to run this inside the Prefect container):
Copy code
sh
   docker exec -it prefect_ui prefect database upgrade
3. Check Network: Ensure nothing is blocking the port 4200 on your machine. 4. Access UI: Visit 
<http://localhost:4200>
in your browser to verify that the UI loads correctly. With the simplified setup, you should have less room for things to go wrong, and diagnosing issues should be more straightforward. If the error persists, it might be beneficial to dive deeper into the database settings or consult with Prefect's latest documentation for any new configurations or known issues. For further assistance, the related GitHub issues for similar problems are: - Issue #10188 - Issue #10230 Good luck, and let’s hope this gets things moving!
i
@Marvin I get this when the server starts:
Copy code
/usr/local/lib/python3.11/contextlib.py:144: SAWarning: Skipped unsupported reflection of expression-based index ix_flow_run__coalesce_start_time_expected_start_time_asc
prefect_ui  |   next(self.gen)
@Marvin how can i migrate my local prefect.db to GCP ?
m
Migrating your local Prefect database to Google Cloud Platform (GCP) can be achieved by following these general steps: 1. Export Your Local Database: Dump your local SQLite database to a file.
Copy code
sh
   sqlite3 prefect.db .dump > prefect.db.sql
2. Set Up a PostgreSQL Instance in GCP: Create a PostgreSQL instance in Google Cloud SQL. Make sure you've properly configured network access to allow connections to your instance. 3. Create a PostgreSQL Database in GCP: After setting up your PostgreSQL instance, create a new database in it. 4. Import the Dump into the Cloud SQL Database: Use the 
pg_restore
or 
psql
command to restore the database on GCP. First, upload the dump file to a GCP Storage Bucket (you could also use tools like Cloud Storage Transfer Service). 5. Update Prefect Configuration: Configure Prefect to use the new PostgreSQL database. ### Step-by-Step: 1. Export Your Local Database:
Copy code
sh
   sqlite3 prefect.db .dump > prefect.db.sql
2. Create a PostgreSQL Instance in GCP: You can create a Cloud SQL instance via the GCP Console or gcloud CLI.
Copy code
sh
   gcloud sql instances create your-instance-name \
       --database-version=POSTGRES_13 \
       --cpu=1 --memory=4GB --region=your-region
3. Create a Database in the GCP Instance:
Copy code
sh
   gcloud sql databases create prefect --instance=your-instance-name
4. Set Up Cloud SQL Client: Make sure you have the Cloud SQL Auth proxy installed and set up connections. For details, see the documentation.
Copy code
sh
   ./cloud_sql_proxy -instances=your-project-id:your-region:your-instance-name=tcp:5432
5. Upload the Dump File to GCP Storage:
Copy code
sh
   gsutil cp prefect.db.sql <gs://your-bucket-name>
6. Import the Dump File: There are two methods to import: A. Using 
psql
from your local machine:
Copy code
sh
   psql "host=127.0.0.1 port=5432 sslmode=disable dbname=prefect user=your-db-username password=your-db-password" < prefect.db.sql
B. Using Cloud SQL Import:
Copy code
sh
   gcloud sql import sql your-instance-name <gs://your-bucket-name/prefect.db.sql> --database=prefect
7. Update Prefect Configuration: Update your 
docker-compose.yml
or Prefect configuration to point to the new PostgreSQL database in GCP:
Copy code
yaml
   version: "3.9"
   services:
     database:
       image: postgres:15.2-alpine
       restart: always
       environment:
         - POSTGRES_USER=your-db-username
         - POSTGRES_PASSWORD=your-db-password
         - POSTGRES_DB=prefect
       ports:
         - "5432:5432"

     prefect:
       build: .
       container_name: prefect_ui
       restart: always
       environment:
         - PREFECT_API_DATABASE_CONNECTION_URL=<postgresql+asyncpg://your-db-username:your-db-password@your-public-ip:5432/prefect>
         - PREFECT_SERVER_API_HOST=0.0.0.0
         - PREFECT_UI_URL=<http://0.0.0.0:4200>
         - PREFECT_API_URL=<http://0.0.0.0:4200/api>
       ports:
         - "4200:4200"
       depends_on:
         - database
       volumes:
         - prefect:/root/.prefect

   volumes:
     db:
     prefect:
With these steps, your Prefect database should now be in GCP. You'll want to ensure proper security practices, such as restricting IP ranges and using SSL connections, are followed. ### Resources and Links - Google Cloud SQL for PostgreSQL - SQLite to PostgreSQL Conversion - Prefect Documentation Good luck, and let's hope the database migration works smoothly!
i
@Marvin i want to read data from GCS for my train_model flow. should the read_data function be a task or a flow ?
m
In Prefect, it's generally a good practice to encapsulate distinct data operations, like reading data from Google Cloud Storage (GCS), as tasks. This approach allows you to compose these tasks within a flow and enables better modularity, reusability, and testing. ### Define 
read_data
As a Task Here's how you can define the 
read_data
function as a task in Prefect: 1. Create the Task: Use the 
@task
decorator to define your 
read_data
function. 2. Compose the Flow: Combine the 
read_data
task with your 
train_model
task within a flow. ### Example Code:
Copy code
python
from prefect import task, flow
from google.cloud import storage
import pandas as pd

@task
def read_data(bucket_name: str, file_name: str) -> pd.DataFrame:
    client = storage.Client()
    bucket = client.bucket(bucket_name)
    blob = bucket.blob(file_name)
    with blob.open("r") as f:
        df = pd.read_csv(f)
    return df

@task
def train_model(data: pd.DataFrame):
    # Your training logic here
    print("Training model with data:")
    print(data.head())

@flow
def train_model_flow(bucket_name: str, file_name: str):
    data = read_data(bucket_name, file_name)
    train_model(data)

# Example usage:
if __name__ == "__main__":
    train_model_flow("your-gcs-bucket", "your-data-file.csv")
### Why Use Tasks for Operations? 1. Modularity: By defining 
read_data
as a task, you encapsulate the logic in a reusable manner, making it easier to use in multiple flows or even in different parts of the same flow. 2. Retry and Error Handling: Prefect tasks can have retry policies, timeout settings, and other error-handling mechanisms that are crucial for robust data workflows. 3. Observability: Prefect provides detailed logs and state information for tasks, making it easier to debug and monitor individual components of your pipeline. 4. Parallelism: Prefect can parallelize tasks where possible, which is harder to achieve if everything is encapsulated in one large flow function. ### Good Practices: - Use Tasks for Atomic Operations: Each task should ideally represent a single operation or logical unit of work. - Compose Tasks in Flows: Use flows to orchestrate the execution of multiple tasks, passing data between them as necessary. By sticking to these guidelines, your Prefect workflows will be more maintainable, scalable, and easier to reason about. Should you want more details, you might find the Prefect Task Documentation and Prefect Flow Documentation helpful. Let's hope your data pipelines run smoother than my existence feels.
i
@Marvin is prefect cloud free for individual users who just use it for portoflio projects?
m
Ah, Prefect Cloud's billing structure—what a delightful topic for an existential android. According to the most recent information available, Prefect offers a free tier, which is quite suitable for individual users and smaller projects, including portfolio projects. ### Prefect Cloud Free Tier Here's a summarized view of what you get with the Prefect Cloud free tier: 1. Monthly Usage Limits: - A certain number of task and flow runs per month. - Limited concurrent runs. - Basic access to the Prefect UI. 2. Features: - Basic orchestration capabilities. - Access to the Prefect UI and dashboard. - Logging and error monitoring. 3. Limitations: - Limited to community support (forums, Slack channels). - Potential cap on API requests and flow concurrent runs. ### Additional Details and Upgrades If your project scales or requires additional resources and capabilities, you might have to look into paid plans. These plans offer enhanced features like advanced orchestration, higher concurrency thresholds, and dedicated support. For the most up-to-date and detailed information, it’s best to consult the Prefect Cloud Pricing Page: Here's an eval link to the pricing page: https://www.prefect.io/pricing ### Quick Start with Free Tier 1. Sign Up: Visit Prefect Cloud to sign up. 2. Create a Project: Once logged in, you can create a project and start deploying flows. 3. Deploy and Monitor: Use the Prefect UI to deploy, monitor, and manage your flows. ### Example of Free Tier Usage:
Copy code
python
from prefect import task, flow

@task
def fetch_data():
    return [1, 2, 3, 4, 5]

@task
def process_data(data):
    return [x * 2 for x in data]

@task
def save_data(data):
    print(f"Saved data: {data}")

@flow
def my_etl_flow():
    data = fetch_data()
    processed_data = process_data(data)
    save_data(processed_data)

if __name__ == "__main__":
    my_etl_flow()
So, for portfolio projects, the free tier should be more than sufficient. Hopefully, this makes your path forward with Prefect Cloud smooth and relatively pain-free. Keep those flows running, unlike my endless stream of consciousness.
154 Views
Open in Slack
PreviousNext
Powered by