< Marvin> Describe how to override the default infrastructur Prefect Community #marvin-in-the-wild

<@ULVA73B9P> Describe how to override the default ...

中谷瞭

08/09/2024, 9:14 AM

@Marvin Describe how to override the default infrastructure parameter for memory on a worker running in and ECS work pool via flow.deploy() method.

Marvin

08/09/2024, 9:14 AM

To override the default memory infrastructure parameter for a worker running in an ECS work pool via the

flow.deploy()

method, you can set the desired configuration in the deployment using the

job_variables

parameter. Here is how you can adjust it: 1. Modify your ECS Job Configuration: You need to specify the memory allocation in your ECS work pool configuration or directly override it in your deployment. 2. Deploy the flow specifying the ECS work pool:

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python
from prefect import flow

@flow
def my_flow():
    print("Running a flow...")

# Deploy the flow
my_flow.deploy(
    name="my_ecs_deployment",
    work_pool_name="my-ecs-work-pool",
    job_variables={
        "memory": "2048Mi"  # Override memory allocation
    }
)

Reference: For more details on configuring and deploying to ECS, refer to the ECS Worker Guide. Example: See this Prefect code example for more context on how flow deployments typically look.

中谷瞭

08/09/2024, 9:21 AM

@Marvin How to include schedules config in flow.deploy() ?

Marvin

08/09/2024, 9:22 AM

To include a schedule configuration in your

flow.deploy()

method, you need to specify the

schedule

argument. Here is an example of how you can do this with a simple `CronSchedule`:

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python
from prefect import flow
from prefect.client.schemas.schedules import CronSchedule

@flow
def my_flow():
    print("Running my scheduled flow...")

# Deploy the flow with a schedule
my_flow.deploy(
    name="my_scheduled_deployment",
    work_pool_name="your-work-pool",
    schedule=CronSchedule(cron="0 0 * * *", timezone="America/Chicago")  # Daily at midnight, Chicago time
)

Reference: For more details on scheduling in Prefect, refer to the Schedules documentation. Example: See this Prefect code example for more context on flow deployment.

Ryo Nakatani

08/13/2024, 2:34 AM

@Marvin How to specify the entrypoint in my_flow().deploy()?

Marvin

08/13/2024, 2:34 AM

To specify the entrypoint in the

my_flow().deploy()

method, you can use the

entrypoint

parameter. The entrypoint should be a string that uses Python's standard path-to-object syntax (e.g., "path/to/file.py:function_name"). Here is an example:

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python
from prefect import flow

@flow
def my_flow():
    print("Running my flow...")

# Deploy the flow with an entrypoint
my_flow.deploy(
    name="my_deployment",
    work_pool_name="your-work-pool",
    entrypoint="my_file.py:my_flow"  # Entry point to the flow defined in 'my_file.py'
)

Reference: For more details on deployment entrypoints, refer to the Deployments documentation. Example: See this Prefect code example for more context on flow deployment.

Ryo Nakatani

08/14/2024, 2:43 AM

@Marvin The deploy() function seems to lack an

entrypoint

argument.

Marvin

08/14/2024, 2:43 AM

You are correct. The

deploy()

method on a Flow object in Prefect does not directly accept an

entrypoint

argument. The

entrypoint

is typically specified in a configuration file like

prefect.yaml

. To include the entrypoint when deploying your flow, you should create a

prefect.yaml

file and use the

entrypoint

field in the deployment configuration. Here is a step-by-step approach: 1. Create a
prefect.yaml
configuration file:

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yaml
# prefect.yaml content
deployments:
  - name: my_deployment
    entrypoint: path/to/my_file.py:my_flow
    work_pool:
      name: your-work-pool

2. Use the Prefect CLI to deploy the flow:

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sh
prefect deploy --name my_deployment

References: - For more details on deployment configurations, refer to the Prefect Deployment Guide. - For details on the

entrypoint

and other fields in the

prefect.yaml

file, refer to the Deployments documentation.

Ryo Nakatani

08/14/2024, 2:44 AM

@Marvin I need to deploy pipelines via python for customization.

Marvin

08/14/2024, 2:45 AM

To deploy pipelines via Python, you can use the

Deployment.build_from_flow

method from the

prefect.deployments

module. Here is a complete example that includes specifying the entrypoint and other configurations: 1. Create the Deployment with `Deployment.build_from_flow`:

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python
from prefect import flow
from prefect.deployments import Deployment
from prefect.filesystems import S3  # Example for using an S3 storage block

@flow
def my_flow():
    print("Running my custom deployment...")

# Optionally, load pre-defined storage block if required
storage = S3.load("dev-bucket")

# Create the deployment
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="custom_deployment",
    version=1,
    work_queue_name="primary-queue",  # Specify your work queue name
    work_pool_name="default",
    entrypoint="path/to/my_file.py:my_flow",  # Specify the entrypoint
    storage=storage,
    schedule=CronSchedule(cron="0 0 * * *", timezone="America/Chicago"),  # Optional: include a schedule
    infra_overrides={
        "env": {
            "ENV_VAR": "value"  # Example: any environment variable overrides
        },
        "memory": "2048Mi"  # Optional: infrastructure overrides
    }
)

# Apply the deployment
deployment.apply()

2. Include Schedule (if needed): You can add a schedule as shown above using

CronSchedule

or any other schedule type. References: - Prefect Deployments Documentation - Example code for creating deployments from Prefect's guide. This approach allows you to customize and deploy your flow directly via Python scripts, giving you flexibility beyond using

prefect.yaml

configurations.

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