@Marvin How do I specify the ECS cluster for the worker deployment?

To specify the ECS cluster for a worker deployment in Prefect, you need to follow these steps: 1. Configure the ECS Task Definition: Create a task definition that specifies the Docker image for the Prefect worker, the resources it requires, and the command it should run.

json
   {
       "family": "prefect-worker-task",
       "networkMode": "awsvpc",
       "requiresCompatibilities": ["FARGATE"],
       "cpu": "512",
       "memory": "1024",
       "executionRoleArn": "<your-ecs-task-role-arn>",
       "taskRoleArn": "<your-ecs-task-role-arn>",
       "containerDefinitions": [
           {
               "name": "prefect-worker",
               "image": "prefecthq/prefect:2-latest",
               "cpu": 512,
               "memory": 1024,
               "essential": true,
               "command": [
                   "/bin/sh", "-c",
                   "pip install prefect-aws && prefect worker start --pool my-ecs-pool --type ecs"
               ],
               "environment": [
                   { "name": "PREFECT_API_URL", "value": "<your-prefect-api-url>" }
               ]
           }
       ]
   }

2. Launch an ECS Service: Create an ECS service that uses the task definition above and specify the ECS cluster where the service should run. 3. Define Job Configuration in Prefect: In your Prefect deployment spec, specify the ECS cluster name.

python
   from prefect_aws.ecs import ECSCluster

   cluster = ECSCluster(
       task_definition='prefect-worker-task',
       cluster='your-ecs-cluster-name'
   )

   cluster.submit(command="prefect worker start --pool my-ecs-pool")

### Documentation and Resources - ECS Worker Setup: Prefect ECS Worker Documentation - ECS Guide: Prefect ECS Guide These provide detailed steps on setting up and configuring ECS workers and clusters for use with Prefect. If you need further assistance or run into issues, you might want to check this GitHub discussion which might provide additional insights.

@Marvin Please provide an example prefect.yaml with work_pool: job_variables: cluster:

Here's a Prefect

prefect.yaml

file example that includes configuration for

work_pool

and

job_variables

specifying an ECS cluster:

yaml
name: my-prefect-deployment
description: A deployment for my flow.
version: 1.0

entrypoint: path/to/your_flow.py:your_flow_function

work_pool:
  name: my-ecs-pool
  job_variables:
    cluster: your-ecs-cluster-name

infrastructure:
  type: ecs
  image: prefecthq/prefect:2-latest
  command: ["prefect", "worker", "start", "--pool", "my-ecs-pool"]

# If you have additional environment variables or resource configurations
job_variables:
  env:
    PREFECT_API_URL: "<https://api.prefect.io>"
    OTHER_ENV_VAR: "value"
  resources:
    cpu: "512"
    memory: "1024"
    taskRoleArn: "arn:aws:iam::account-id:role/your-ecs-task-role"
    executionRoleArn: "arn:aws:iam::account-id:role/your-ecs-task-role"
    networkConfiguration:
      awsvpcConfiguration:
        subnets: ["subnet-12345"]
        securityGroups: ["sg-12345"]
        assignPublicIp: "ENABLED"

# Optionally specify schedules
schedule: null

### References - Work Pools Documentation: Prefect Work Pools - Job Variables Guide: Overriding Job Variables These example values must be replaced with actual values from your AWS and Prefect configurations.

@Marvin Why would a flow run be stuck in pending even though the ecs task completes successfully?

If a flow run is stuck in the

Pending

state even though the ECS task completes successfully, there are several potential reasons for this issue: 1. Worker Issues: The ECS worker might not be reporting the status back to Prefect Cloud/API correctly. Ensure that the ECS worker is properly configured to communicate with Prefect API, and the worker itself is not experiencing any connectivity issues. 2. Incomplete Task Definition: Check if the ECS task definition is correctly set up and includes all necessary environment variables and configurations for Prefect. Specifically, make sure the

PREFECT_API_URL

is correctly set, and any necessary permissions or roles are in place. 3. Missing or Incorrect Flow Run IDs: Ensure that the flow run ID is being passed and handled correctly within your ECS task. The task must be aware of the flow run ID and report its status correctly back to Prefect. 4. Concurrency Limits or Priority Issues: There might be concurrency limitations or priority settings that are affecting the execution order of your flow runs. Check the configuration of your work pool for any such settings that might be delaying the execution. ### Key Points from Documentation - Work Pools and Priority: Priority is evaluated for flow runs to determine the order of execution. Even if the ECS task completes, other settings in the work pool might delay the execution if there are concurrency limits or higher priority tasks. - Work Pools Documentation - Flow Execution Details: Ensure that the flow definition and entry points are correctly specified and that the flow run is not stuck due to internal issues such as long-running tasks or failed task dependencies. - Flow Concepts Documentation - ECS Configuration: Detailed configuration for ECS workers, including common issues and their resolutions. Ensure that after completing the task, the ECS worker is not terminated prematurely and is correctly reporting back. - ECS Worker Guide If the problem persists, it might be helpful to check logs from the ECS tasks and the Prefect worker to identify any communication issues or errors.

@Marvin How do I disable the pull step?

If you want to disable the

pull

step in your Prefect deployment, you can either omit the

pull

section altogether or set it to an empty list in your

prefect.yaml

file. Here's how you can disable the

pull

step: ### Method 1: Omit the

pull

Step Simply do not include the

pull

section in your

prefect.yaml

file.

yaml
name: my-prefect-deployment
description: A deployment for my flow.
version: 1.0

entrypoint: path/to/your_flow.py:your_flow_function

work_pool:
  name: my-ecs-pool

infrastructure:
  type: ecs
  image: prefecthq/prefect:2-latest
  command: ["prefect", "worker", "start", "--pool", "my-ecs-pool"]

job_variables:
  env:
    PREFECT_API_URL: "<https://api.prefect.io>"
    OTHER_ENV_VAR: "value"
  resources:
    cpu: "512"
    memory: "1024"

schedule: null

### Method 2: Set

pull

to an Empty List Explicitly set the

pull

section to an empty list.

yaml
name: my-prefect-deployment
description: A deployment for my flow.
version: 1.0

entrypoint: path/to/your_flow.py:your_flow_function

work_pool:
  name: my-ecs-pool

infrastructure:
  type: ecs
  image: prefecthq/prefect:2-latest
  command: ["prefect", "worker", "start", "--pool", "my-ecs-pool"]

pull: []

job_variables:
  env:
    PREFECT_API_URL: "<https://api.prefect.io>"
    OTHER_ENV_VAR: "value"
  resources:
    cpu: "512"
    memory: "1024"

schedule: null

### References - Deployment Configuration Guide: Prefect Deployment Configuration Omitting or disabling the

pull

step allows you to run the deployment without pulling any additional files or configuration from external storage or repositories.

Neither of those work