@Marvin When I deploy using the .yaml do I also need to have a .deploy method in the python ETL?

To provide you with an accurate answer, I need to confirm the version of Prefect you are using, as deployment methods can vary between Prefect 2.x and 3.x. Could you please let me know the version you're using? If you're unsure, I can help you check!

@Marvin Prefect 3.x

In Prefect 3.x, you can manage deployments using a

prefect.yaml

file, which allows for multiple deployment configurations of the same flow without needing to have a

.deploy()

method within your Python ETL script. This method supports version control and independent deployment management. Here's a quick summary: - You can have multiple deployment configurations in a single

prefect.yaml

file, each with a unique name. - Use the

prefect deploy

command to handle these configurations. - The

entrypoint

for each deployment needs to be relative to the root directory of your project. For more detailed information, you can refer to the Prefect 3.x documentation on deployment using

prefect.yaml

files: Prefect 3.x YAML Deployment.

@Marvin So in the python , do I just execute a .serve method if I'm using the prefect.yaml?

In Prefect 3.x, the

.serve

method is typically associated with running shell commands on a schedule and is not directly linked to managing deployments via a

prefect.yaml

file. The

serve

command is used to create a Prefect deployment for regular execution of shell tasks, like running a scheduled shell command. If you are implementing a Prefect workflow using a

prefect.yaml

file for deployment purposes, you don't generally need to call a

.serve

method in your Python ETL script. The

prefect.yaml

file handles deployment configurations, including scheduling, so there's usually no need to combine this with a

.serve

method in the script itself. For detailed guidance on using

prefect.yaml

, see Prefect 3.x YAML Deployment.

@Marvin What's the parameters of a "deployments" block of the prefect.yaml ?

In Prefect 3.x, a

deployments

block within the

prefect.yaml

file contains several key parameters to manage flow deployments. Here are some of the important parameters you can include: - name: The unique name of the deployment. - entrypoint: A reference to the location of the flow code. It's usually the path to the Python file containing the flow, followed by the flow function name (e.g.,

flows/my_flow.py:my_flow

). - parameters: A dictionary of parameters to pass to the flow. These can be used to override default values within the flow. - tags: Optional tags for the deployment. - infra_overrides: Allows overriding the default infrastructure settings for the deployment. - schedule: Defines the schedule for the flow runs, using interval or cron syntax. - work_pool: - name: The name of the work pool to use for scheduling flow runs. - work_queue_name: The specific work queue within the work pool to use. - job_variables: Values to override defaults in the work pool’s job template. These parameters provide flexibility to customize deployments and manage them efficiently using YAML configuration. You can find more detailed information in the official Prefect 3.x documentation here.