Based on the available information, when using Dask with AWS Fargate through Prefect, you'll need the following IAM permissions: 1. Core ECS Permissions: -

ecs:RegisterTaskDefinition

-

ecs:RunTask

-

ecs:DescribeTasks

-

ecs:ListTasks

-

ecs:StopTask

-

ecs:DeregisterTaskDefinition

2. Networking Permissions: -

ec2:DescribeSubnets

-

ec2:DescribeVpcs

-

ec2:DescribeSecurityGroups

3. IAM Permissions: -

iam:GetRole

-

iam:PassRole

(for both task and execution roles) 4. CloudWatch Permissions (for logging): -

logs:CreateLogGroup

-

logs:CreateLogStream

-

logs:PutLogEvents

-

logs:DescribeLogStreams

Here's a sample IAM policy that includes these permissions:

json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ecs:RegisterTaskDefinition",
                "ecs:RunTask",
                "ecs:DescribeTasks",
                "ecs:ListTasks",
                "ecs:StopTask",
                "ecs:DeregisterTaskDefinition",
                "ec2:DescribeSubnets",
                "ec2:DescribeVpcs",
                "ec2:DescribeSecurityGroups",
                "iam:GetRole",
                "iam:PassRole",
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents",
                "logs:DescribeLogStreams"
            ],
            "Resource": "*"
        }
    ]
}

Important Notes: 1. You'll need to ensure your ECS task role and execution role have the necessary permissions to access any other AWS services your tasks need (like S3, if you're reading/writing data). 2. For security best practices, you should restrict the

Resource

field to specific ARNs rather than using "*". 3. If you're using custom VPC configurations, you might need additional networking permissions. 4. Make sure your Fargate tasks have access to the necessary networking components (VPC, subnets, security groups) to communicate with each other. If you encounter permission-related issues, they typically manifest in the CloudWatch logs, so make sure to check there for detailed error messages.

Looking at your code, there are several issues and missing configurations that could be causing problems: 1. Missing Required Dependencies:

python
   from prefect_dask import DaskTaskRunner
   from dask_cloudprovider.aws import FargateCluster

2. Cluster Configuration: Your current configuration is missing several important parameters. Here's a more complete version:

python
def fargate_cluster(
    n_workers=4,
    scheduler_task_definition_arn='{task_definition_arn}',
    execution_role_arn='{execution_role}',
):
    return FargateCluster(
        n_workers=n_workers,
        scheduler_task_definition_arn=scheduler_task_definition_arn,
        execution_role_arn=execution_role_arn,
        # Add these important configurations:
        scheduler_timeout="15 minutes",  # Increase if needed
        security_groups=["your-security-group-id"],  # Required
        cluster_arn="your-ecs-cluster-arn",  # Required
        vpc_subnets=["your-subnet-id"],  # Required
        environment={  # Optional but recommended
            "EXTRA_PIP_PACKAGES": "prefect-dask dask distributed",
        }
    )

@flow(
    task_runner=DaskTaskRunner(
        cluster_class=fargate_cluster,
        cluster_kwargs={
            "n_workers": 4,
            "scheduler_task_definition_arn": "your-task-definition-arn",
            "execution_role_arn": "your-execution-role-arn",
        }
    )
)
def test_cluster():
    # Your flow code here
    pass

3. Common Requirements: - Make sure your task definition includes the necessary Python packages (

prefect-dask

,

dask

,

distributed

,

dask-cloudprovider

) - The task definition needs sufficient memory and CPU - The execution role needs permissions to create and manage ECS tasks - The task role needs permissions for any AWS services your flow will interact with 4. Networking: - Ensure your VPC has internet access (via NAT Gateway for private subnets) - Security groups must allow communication between scheduler and workers - DNS resolution must be enabled in your VPC 5. Troubleshooting Tips: - Check CloudWatch logs for both scheduler and worker tasks - Verify that your task definition has the correct container image - Ensure your ECS cluster has sufficient capacity for the number of workers - Monitor the ECS console for task status and potential placement errors If you're still having issues, could you share: 1. Any error messages you're seeing? 2. The contents of your task definition? 3. Whether the tasks are showing up in your ECS cluster?

Yes, there are several ways to confirm where your Dask cluster is running. Here's how you can verify: 1. Check the Dashboard URL When your flow starts, Prefect will log the Dask dashboard URL. The format will tell you where it's running: - Local cluster:

<http://127.0.0.1:8787>

or

<http://localhost:8787>

- AWS ECS:

http://<public-ip>:8787

2. Explicit Local Configuration To ensure you're running locally, you can explicitly use the LocalCluster:

python
from distributed import LocalCluster
from prefect_dask import DaskTaskRunner

@flow(
    task_runner=DaskTaskRunner(
        cluster_class=LocalCluster,
        cluster_kwargs={
            "n_workers": 2,
            "threads_per_worker": 1
        }
    )
)
def your_flow():
    pass

3. Check Task Runner Logs The Prefect logs will indicate cluster creation: - Local: Will mention "Creating a new Dask LocalCluster" - ECS: Will mention "Creating a new Dask cluster with FargateCluster" 4. Diagnostic Task You can add a diagnostic task to your flow:

python
from prefect import task, flow
from prefect_dask import DaskTaskRunner
import socket
import os

@task
def check_environment():
    return {
        "hostname": socket.gethostname(),
        "ip": socket.gethostbyname(socket.gethostname()),
        "environment_vars": dict(os.environ),
        "current_path": os.getcwd()
    }

@flow(task_runner=DaskTaskRunner())
def diagnostic_flow():
    env_info = check_environment()
    print(f"Running in environment: {env_info}")
    return env_info

5. Monitor System Resources If running locally, you should see Python processes for the Dask scheduler and workers in your system's task manager or using

ps aux | grep dask

. To explicitly ensure local execution, use this configuration:

python
from distributed import LocalCluster
from prefect_dask import DaskTaskRunner

def local_cluster():
    return LocalCluster(
        n_workers=2,
        threads_per_worker=1,
        memory_limit="1GB"
    )

@flow(
    task_runner=DaskTaskRunner(
        cluster_class=local_cluster
    )
)
def definitely_local_flow():
    # Your flow code here
    pass

If you're seeing any AWS resources being created or AWS-related logs, then your configuration might still be pointing to AWS. Could you share the full configuration you're currently using, including any environment variables that might be set?