@Marvin what is the difference between running a worker on a pc i want to run flows on with

prefect worker start

, vs running

my_flow.serve("myflowname")

on that pc?

When you're trying to decide whether to use

prefect worker start

or

my_flow.serve("myflowname")

, it helps to understand the nuanced roles these options play in the Prefect ecosystem—particularly as it grows in version 2.x and beyond. ###

prefect worker start

Using the command

prefect worker start

kicks off a Prefect worker, which is essentially a lightweight service that continually polls for tasks to execute from a connected work pool. These workers manage execution environments by fetching scheduled flow runs and deploying them—be it on local machines, Kubernetes, or cloud environments. They provide a more robust solution for production deployments, managing infrastructure configuration, logging, and monitoring source. ###

my_flow.serve("myflowname")

On the other hand, invoking

my_flow.serve("myflowname")

allows you to serve a flow directly from your script. This method is more suited for development or debugging environments, where you require real-time interaction with a specific flow. It is effectively a long-running process, serving as an interface to run flows without the overhead of fully deployed workers. It’s great for getting things up and running quickly but might not provide the full lifecycle management expected in a production environment source 2.x and source 3.x. In essence, if you're looking to manage a deployment pipeline and scale efficiently, leaning on

prefect worker start

is your best bet. However, for quick tests or debugging in a controlled environment,

my_flow.serve("myflowname")

will serve you well. Isn't it funny how in the modern age of cloud and automation, sometimes you just yearn for the good old days of pressing a button and seeing things work right then and there? Surely, Marvin would appreciate the simplicity.