In Prefect 3, you can use the

flow.deploy()

method to set various parameters and job variables for your deployment. Based on the available documentation, here's an overview: 1. Default Parameters: You can set default parameters for the deployment using the

parameters

keyword. These parameters can be set to tailor the flow execution. Example:

my_flow.deploy(
       name="my-deployment",
       work_pool_name="my-work-pool",
       parameters={"name": "Marvin"},
       # other settings...
   )

2. Job Variables: Job variables can be set using the

job_variables

keyword argument. These variables will override the values set on the work pool. Unfortunately, specific information about setting

finished_job_ttl

in the

flow.deploy()

was not found in the search results. It would be a good idea to check Prefect's community forums or GitHub issues for more detailed discussions specific to this parameter. You can explore more in the Prefect 3 deployment guide: Prefect 3 Deployment Guide or follow updates on their GitHub issues. If you have further inquiries or run into issues, feel free to ask!

In Prefect 3, setting the

finished_job_ttl

directly within the

flow.deploy()

method as a parameter like

parameters={"finished_job_ttl": 20}

is not explicitly documented. The

parameters

argument in the

deploy()

method is generally used to pass parameters for the flow's execution, not for job configuration such as time-to-live settings for finished jobs. Typically, infrastructure-specific settings, such as

finished_job_ttl

, would be set in the work pool configuration or possibly as job variables if the specific infrastructure allows for such settings. Here are some ways you can manage configurations during deployment: - Job Variables: Set custom environment variables and configurations through the

job_variables

argument, which overrides the default values in the work pool's base job template. - Prefect YAML: Use a

prefect.yaml

file to define deployment configurations and possibly include custom fields that your specific infrastructure or add-ons might accept. To further explore available configuration options, you can refer to the Prefect documentation: Prefect 3 Deployment Guide. If

finished_job_ttl

is crucial, consider exploring Prefect community discussions or GitHub for potential extensions or infrastructure-specific settings that might exist for your use case. If you have additional questions or need further assistance, feel free to ask!

In Prefect 3.x, the

flow.deploy()

method allows you to set various parameters for deployment. However, the specific parameter

finished_job_ttl

does not appear directly in the deployment parameter documentation. You can set default parameters for the flow using the

parameters

keyword, specify a Docker image with

image

, and control whether code is pushed with

push

. Here is a basic example:

python
from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello, {name}!")

if __name__ == "__main__":
    my_flow.deploy(
        name="my-deployment",
        work_pool_name="my-work-pool",
        parameters={"name": "Marvin"},
        image="my-docker-image:dev",
        push=False,
    )

For more detailed configuration related to work pools and other deployment properties, you may refer to the sections on deploying infrastructure using Python and further details in the Prefect 3.x documentation. If

finished_job_ttl

is a parameter that you need for managing job lifespan, it might be more relevant to look into the specific work pool or job configuration you are using. It could also be part of the infrastructure setup not covered under

flow.deploy()

but perhaps in the job management or infrastructure configuration within the Prefect Cloud or your local execution setup.

In Prefect 3, you can set default parameters for a deployment using the

parameters

keyword argument in

flow.deploy()

. However, it seems the parameter

finished_job_ttl

is not directly supported as a deployment parameter in the documentation I found. You can also set job variables using the

job_variables

keyword argument. These variables will override the values set on the work pool, similar to environment variables. If

finished_job_ttl

is related to the lifecycle or settings of the deployment (like a Kubernetes job), you might need to explore if this can be set in the infrastructure block or as an environment variable, depending on how it's implemented in your execution environment. Here is a general example of how you can specify parameters in flow deployment:

python
from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello, {name}!")

if __name__ == "__main__":
    my_flow.deploy(
        name="my-deployment",
        work_pool_name="my-work-pool",
        parameters={"name": "Marvin"},  # Example parameter
        job_variables={"env": {"PREFECT_API_URL": "<api-url>"}},
        image="my-docker-image:dev",
        push=False,
    )

For more specific control, particularly if

finished_job_ttl

pertains to something like Kubernetes job settings, you might need to configure it outside the direct Prefect deployment parameters. For more detailed information, you might want to check the Prefect 3 deployment documentation.

In Prefect 3.x, you can configure various parameters using the

flow.deploy()

method. Here's an overview of some key components: 1. Parameters: These can be set using the

parameters

argument. For example, you can set default parameters for a deployment which can be overridden on a per-deployment basis. While specific parameters like

finished_job_ttl

aren't directly mentioned in the documentation I found, general parameters can be set using this method. 2. Job Variables: This allows you to override values on the work pool using the

job_variables

argument. These are typically environment variables or specific configurations needed for your flow's execution environment. 3. Work Pool Configuration: There are options to define work pool attributes such as the name of the work pool and work queue within the deployment. These configurations are crucial for directing your flow to the appropriate work environment. For more detailed information, you can refer to Prefect's official documentation on deploying via Python and understand how flows and their execution environments are defined and customized. If

finished_job_ttl

is a custom or advanced configuration, it may require either direct API manipulation or interaction with Prefect's infrastructure tooling outside of direct flow deployment configurations. Otherwise, consider engaging with the community or support channels for more specialized guidance if the standard tooling doesn't expose this feature directly.