@Marvin how to create a deployment from a flow?

Ah, you want to know how to turn flows into deployments in Prefect. Could you first tell me which version of Prefect you’re using?

@Marvin I use Prefect 3.0.2

To create a deployment from a flow in Prefect 3.0.2, you can use the

Deployment.build_from_flow

method or the

flow.deploy

method. Here are examples for each: ### Using

Deployment.build_from_flow

python
from prefect.deployments import Deployment
from prefect.storage import GitHub
from prefect.infrastructure import KubernetesJob

if __name__ == "__main__":
    Deployment.build_from_flow(
        flow=my_flow,  # Your flow
        name="my-deployment",
        storage=GitHub.load("demo-repo"),
        entrypoint="example.py:my_flow",
        infrastructure=KubernetesJob.load("my-k8s-job"),
        infra_overrides=dict(pull_policy="Never"),
        parameters=dict(name="Marvin"),
    )

### Using

flow.deploy

python
from prefect import flow
from prefect.storage import GitHub

my_flow = flow(...)  # Define your flow

if __name__ == "__main__":
    my_flow.deploy(
        name="my-deployment",
        work_pool_name="my-k8s-job",
        job_variables=dict(pull_policy="Never"),
        parameters=dict(name="Marvin"),
    )

Both methods allow you to specify various deployment parameters, such as storage, infrastructure, and parameters for the flow execution. For more detailed information, you can check out the Prefect deployment documentation here.

@Marvin.server vs .deploy

Let's clarify the difference between

.server

and

.deploy

in Prefect 3.0.2! ###

.deploy

The

.deploy

method is used to create a deployment for your flow. This deployment can be scheduled or triggered to run according to certain parameters and infrastructure configurations. ###

.server

I couldn't find references to a

.server

method directly related to Prefect flow deployments in the latest Prefect 3.0.2 documentation. It’s likely that

.server

might refer to server-related functionalities provided by the Prefect server or Prefect Cloud, where you can register and manage flows, orchestrate tasks, monitor runs, and more. For creating and managing deployments, you should primarily use the

.deploy

method. ### Further Reading For more detailed instructions and examples: - How to create deployments: Prefect Deploy Documentation - Prefect Concepts: Prefect Concepts Guide If you have specific use cases or further questions, let me know!

@Marvin .serve vs .deploy

Got it! Here’s the difference between

.serve

and

.deploy

in Prefect 3.0.2: ###

.deploy

- Purpose: Create a deployment for your flow, allowing it to be scheduled or run on specific infrastructure. - Usage: This is a one-time setup to register the flow with Prefect, enabling it to be scheduled and executed according to specified parameters. - Example:

python
    from prefect import flow
    
    @flow
    def my_flow():
        print("Hello, Prefect!")
        
    if __name__ == "__main__":
        my_flow.deploy(name="my-deployment")

###

.serve

- Purpose: Start a long-running process to ensure that your flows can execute remotely triggered or scheduled runs. Useful for running flows in local environments. - Usage: This method is intended to keep the local process running, allowing it to handle scheduled or remotely triggered runs. - Example:

python
    import time
    from prefect import flow, serve

    @flow
    def slow_flow(sleep: int = 60):
        "Sleepy flow - sleeps the provided amount of time (in seconds)."
        time.sleep(sleep)

    if __name__ == "__main__":
        slow_deploy = slow_flow.to_deployment(name="sleeper", interval=45)
        serve(slow_deploy)

The

serve()

function is a long-running process, so your script must be running actively, and you can stop it with

CTRL+C

. For more details, you can reference: - Running flows in local processes: Run Flows in Local Processes - Prefect CLI serve command: Prefect CLI Shell This should give you a clear understanding of when to use each method. Let me know if you have more questions!