@Marvin: How to create workpool and workqueue using python scripts?

In Prefect, creating a work pool or work queue using Python scripts is typically done via the Prefect CLI or UI rather than directly through Python code. However, here's a general approach for each: 1. Creating a Work Pool: In Prefect 2.x and 3.x, you can create a work pool using the Prefect CLI with the following command:

prefect work-pool create [OPTIONS] NAME

This command requires a unique name for the work pool and offers several optional parameters such as

--paused

,

--type

,

--set-as-default

, and

--base-job-template

. 2. Creating a Work Queue: The process of creating work queues is also typically handled through UI or CLI commands, where you define the queue name, infrastructure type, priority, and concurrency limits etc. Since creating work pools and queues isn't directly supported through native Python functions in Prefect, using the CLI is recommended. However, if you're automating task execution, you might script these CLI commands in a broader Python script. For more detailed information, you can visit the Prefect documentation: - Prefect 3.x Work Pools: Link - Prefect 2.x Work Pools: Link Feel free to reach out if you have any more questions!

hi @Soham - what's the motivation to use python? lots of times, you can avoid creating work pools dynamically by leveraging deployment level overrides

@Nate: I am looking to automate the flow deployment and connecting it with specific work pools. I am simply taking flows that I want to create deployment for and then calling the

to_deployment()

and

apply()

to create the deployments. Since the project needs to support low resource needs I am not using

deplo()

which bakes into docker. Is there a better way to do it?

a couple thoughts:

Since the project needs to support low resource needs I am not using

deploy()

which bakes into docker

.deploy

doesn't need to bake your code into docker, you can say

build=False

and

push=False

and then instead of just

your_flow.deploy(...)

you can say

your_flow.from_source(
  source="<https://www.github.com/you/yourrepo.git>",
  entrypoint="path/relative/to/root/to_flow.py:flow_fn"
).deploy(
  work_pool_name="your_pool", build=False, push=False
)

to pull your code from a repo, so then your work pool (if its a containerized runtime like docker, ecs, k8s) can specify an

image

with your deps, that doesn't contain your flow code, because the flow code is cloned at runtime

I see, thanks for points that out. I will check this