@Marvin I am trying to launch a kubernetes job from prefect. I do have a kubernetes worker set up on the cloud and an image stored on ECR. Wondering what the best python class and strategy is in general.

Based on the information I found, you can use the

KubernetesJob

infrastructure to execute flow runs in a Kubernetes Job. Here are some requirements for `KubernetesJob`: -

kubectl

must be available. - You must configure remote Storage. Local storage is not supported for Kubernetes. - The ephemeral Prefect API won't work with Docker and Kubernetes. You must have a Prefect server or Prefect Cloud API endpoint set in your agent's configuration. You can verify your Prefect worker deployment by running

kubectl get pods -n prefect

. You should see the Prefect worker pod running. The worker will now be able to communicate with Prefect Cloud and execute your Prefect flows. For more detailed information, you can refer to these documents: - KubernetesJob - Verify Deployment Please note that the versions of the documents might have changed, so it's always a good idea to check the latest documentation.

just heads up, workers have superseded agents, so the second link is what you want you'll put your

arn::....::/image:tag

as the

image

field on your k8s work pool

I already have a worker running. Mostly thinking about job creation , deployment and running the flow via python api

nice! there's two main paths for deployment definition / creation •

prefect.yaml

to define deployments,

prefect deploy

via CLI to create them (recommended for situations like yours where youll be dynamically allocating infra with k8s) ◦ example yaml where I can deploy with

prefect deploy -n prefect-github-digest

or

prefect deploy --all

•

Flow.deploy

if you want to define / create deployments in python (docs) either way you go, you can invoke the eventual deployment via REST api directly, cli with

prefect deployment run <name>

or

run_deployment

from the sdk

Thanks. So if I need to programatically run multiple concurrent flows from a deployment. The best strategy is to have prefect.yaml file and deploy it via cli which makes sense since it doesnt happen often. Then I need to use

run_deployment

and override parameters to run an async flow. Is it possible to invoke multiple concurrent flows with different sets of params?

something like this?

Yeah I need independent pods since I may have different infra requirements.

I see. Then it makes sense to deploy at runtime. I see this

infrastructure_must_have_capabilities

method here . If I do under work pool's base template does that mean I have to create a work pool upon runtime as well? Probably not but would you refer me to the work pool base where this should be overriden? Looking at Deployment interface here , work pool name is the only reference I get. So does that mean I should expect each workpool to support a specific type of infra?

> Then it makes sense to deploy at runtime this makes sense to me > work pool base where this should be overriden? visually, you can check it out in the advanced tab of your work pools Base Job Template section. there's also this so if you're gonna create deployments more dynamically like this, maybe

.deploy

would be easiest you shouldnt need to change the work pool's actual base template for each one, you can just pass

job_variables

to each

.deploy

to customize the pool's base template for that deployment with something like

from prefect import flow

@flow
def whatever():
   ...

job_manifests = {"small": {... dict version of some k8s manifest ...}, "big": {...}}

if __name__ == "__main__":
   for name, manifest in job_manifests.items():
       whatever.deploy(
           name=f"{name}-deployment", 
           work_pool_name="my-k8s-pool", 
           job_variables={"job_manifest": manifest}
           image="repo/whatever:tag",
           push=False
       )

Thanks. You are better than Marvin

sure thing! and lol, i would hope so, not ready to superseded by my ai child 😂