Here's how to set up and run workflows with a self-hosted Prefect server: 1. Point Your Client to the Server There are two ways to configure your client to connect to your self-hosted server: a. Using environment variables:

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

b. Using the Prefect CLI:

bash
   prefect config set PREFECT_API_URL="<http://your-server-address:4200/api>"

2. Where to Run Workflows You can run your workflows from any machine that can reach your Prefect server. Here's the typical setup: a. Development Machine: You can run flows directly during development using:

python
   if __name__ == "__main__":
       my_flow()

b. Production Environment: For production, you'll want to: 1. Create a deployment using

flow.from_source()

and

.deploy()

2. Set up a work pool in your server UI or via CLI 3. Run a worker that connects to your work pool to execute flows Example deployment setup:

python
   from prefect import flow
   
   @flow
   def my_flow():
       # your flow code here
       pass

   if __name__ == "__main__":
       my_flow.from_source(
           source="local",  # or your git repository
           entrypoint="path/to/flow.py:my_flow"
       ).deploy(
           name="my-deployment",
           work_pool_name="my-work-pool"
       )

3. Running Workers After setting up your deployment, start a worker:

bash
   prefect worker start -p "my-work-pool"

The worker will poll your self-hosted server for new flow runs and execute them when scheduled. Key points to remember: - Your server, client, and workers all need network connectivity to each other - Workers need access to any resources your flows require (databases, APIs, etc.) - You can have multiple workers across different machines all connecting to the same server - Make sure your

PREFECT_API_URL

is set correctly on all machines that need to communicate with the server For more details, you can check out the documentation on: - Self-hosted server setup - Server concepts

For an ad hoc workflow run, here's why you might choose deployments and work pools over direct script execution: Advantages of Using Deployments & Work Pools: 1. Infrastructure Isolation: Your flow runs in a clean, isolated environment managed by the work pool, rather than whatever environment you happen to be in when you run the script 2. Resource Management: Work pools can automatically provision and tear down infrastructure (like containers or serverless functions) as needed 3. Observability: You get better visibility into runs through the Prefect UI, including: - Full run history - Detailed logs - Runtime metrics - State transitions 4. Reproducibility: The exact same code and dependencies will be used each time, regardless of who triggers the run 5. Failure Handling: Built-in retry mechanisms and the ability to restart failed runs from the UI 6. Parameter Management: You can easily modify parameters for each run through the UI without changing the code Advantages of Direct Script Execution: 1. Simplicity: Just run

python script.py

and you're done 2. Quick Iteration: Faster for development and testing 3. Local Debugging: Easier to use debuggers and step through code 4. No Infrastructure Overhead: Don't need to wait for worker provisioning My recommendation: - Use direct script execution during development and debugging - Use deployments/work pools when: - Others need to run your flows - You need consistent execution environments - You want better observability and management features - The flow needs specific infrastructure or resource requirements Even for ad hoc runs, deployments can be worth the small extra setup time if you value the additional control and visibility they provide. You can still trigger them manually through the UI or CLI when needed, while maintaining all the benefits of proper infrastructure management and observability.