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<@ULVA73B9P> Describe how to override the default infrastructure parameter for memory on a worker ru...
# marvin-in-the-wild
u
@Marvin Describe how to override the default infrastructure parameter for memory on a worker running in and ECS work pool via flow.deploy() method.
m
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:
Copy code
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.
u
@Marvin How to include schedules config in flow.deploy() ?
m
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.
r
@Marvin How to specify the entrypoint in my_flow().deploy()?
m
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.
r
@Marvin The deploy() function seems to lack an 
entrypoint
argument.
m
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.
r
@Marvin I need to deploy pipelines via python for customization.
m
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`:
Copy code
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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