Ah, I see. Let me provide you with a proper implementation. The key is to ensure you're using the correct setup with

prefect-dask

and

dask-cloudprovider

. Here's a complete example:

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner
from dask_cloudprovider.aws import FargateCluster
import time

def create_fargate_cluster(**kwargs):
    return FargateCluster(
        image="daskdev/dask:latest",  # or your custom image
        n_workers=2,
        fargate_use_private_ip=True,
        # AWS-specific configuration
        region_name="us-east-1",  # specify your region
        vpc="vpc-xxxxxx",  # your VPC ID
        cluster="your-ecs-cluster-name",
        subnets=["subnet-xxxxx"],  # your subnet IDs
        security_groups=["sg-xxxxx"],  # your security group IDs
        **kwargs
    )

@flow(
    task_runner=DaskTaskRunner(
        cluster_kwargs={"cluster_class": create_fargate_cluster}
    )
)
def my_dask_flow():
    # Your flow tasks here
    results = []
    for i in range(10):
        results.append(my_task.submit(i))
    return results

@task
def my_task(x):
    time.sleep(1)
    return x * 2

if __name__ == "__main__":
    my_dask_flow()

To make this work, you'll need to: 1. Install the required packages:

bash
pip install prefect-dask dask-cloudprovider

2. Ensure you have proper AWS credentials configured (either through environment variables, AWS CLI configuration, or IAM roles) 3. Configure your AWS infrastructure: - Set up an ECS cluster - Configure VPC, subnets, and security groups - Ensure proper IAM roles and permissions Key points to prevent local cluster creation: - Always specify the full AWS configuration in the

FargateCluster

constructor - Make sure your AWS credentials are properly configured - Set

fargate_use_private_ip=True

if your tasks need to communicate within a VPC - Ensure your security groups allow proper communication between the scheduler and workers Common issues to watch for: 1. If a local cluster is being created, it's often because the Fargate cluster creation failed silently. Check your AWS CloudWatch logs for details. 2. Ensure your security groups allow inbound/outbound traffic on the Dask ports (typically 8786-8787) 3. The VPC needs internet access (via NAT Gateway or Internet Gateway) for pulling Docker images The

prefect-dask

integration ensures proper lifecycle management of your Fargate cluster - it will be created when the flow starts and terminated when the flow completes, preventing unnecessary AWS costs. Remember to replace the placeholder values (vpc-xxxxxx, subnet-xxxxx, etc.) with your actual AWS infrastructure details. Also, consider using Prefect blocks to store and manage these configuration details securely. If you're still seeing local cluster creation, enable Dask logging to debug the issue:

python
import logging
logging.basicConfig(level=<http://logging.INFO|logging.INFO>)
logger = logging.getLogger("distributed")
logger.setLevel(logging.DEBUG)

This will help you identify where the cluster creation process might be failing and falling back to local mode.

Here's the complete code with the logging configuration properly placed:

python
# Place logging configuration at the very top of your script,
# before any other imports or code
import logging
logging.basicConfig(level=<http://logging.INFO|logging.INFO>)
logger = logging.getLogger("distributed")
logger.setLevel(logging.DEBUG)

# Now proceed with the rest of your imports
from prefect import flow, task
from prefect_dask import DaskTaskRunner
from dask_cloudprovider.aws import FargateCluster
import time

def create_fargate_cluster(**kwargs):
    return FargateCluster(
        image="daskdev/dask:latest",
        n_workers=2,
        fargate_use_private_ip=True,
        region_name="us-east-1",
        vpc="vpc-xxxxxx",
        cluster="your-ecs-cluster-name",
        subnets=["subnet-xxxxx"],
        security_groups=["sg-xxxxx"],
        **kwargs
    )

@flow(
    task_runner=DaskTaskRunner(
        cluster_kwargs={"cluster_class": create_fargate_cluster}
    )
)
def my_dask_flow():
    # Your flow tasks here
    results = []
    for i in range(10):
        results.append(my_task.submit(i))
    return results

@task
def my_task(x):
    time.sleep(1)
    return x * 2

if __name__ == "__main__":
    my_dask_flow()

The logging configuration must be placed at the very beginning of your script because: 1. It needs to be configured before any Dask components are imported or initialized 2. Once loggers are created by imported modules, changing the basic configuration won't affect those already-created loggers You can also add more detailed logging by including additional loggers:

python
# At the top of your script
import logging
logging.basicConfig(level=<http://logging.INFO|logging.INFO>)

# Dask distributed logger
distributed_logger = logging.getLogger("distributed")
distributed_logger.setLevel(logging.DEBUG)

# Dask cloud provider logger
cloudprovider_logger = logging.getLogger("dask_cloudprovider")
cloudprovider_logger.setLevel(logging.DEBUG)

# Add a stream handler if you want to see output in the console
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
distributed_logger.addHandler(handler)
cloudprovider_logger.addHandler(handler)

This enhanced logging configuration will give you visibility into: - The Dask cluster creation process - Worker initialization and communication - Any AWS Fargate-specific issues - Task scheduling and execution The logs will appear in your console output and can help diagnose why a local cluster might be created instead of the intended Fargate cluster.

Based on the GitHub issues and your symptoms, here are the most likely causes and solutions for your Fargate cluster falling back to local mode: 1. Network Configuration Issues - The scheduler task is starting but can't be reached, which causes the fallback to local mode - Common causes: - Security groups not properly configured - VPC/subnet configuration issues -

fargate_use_private_ip

setting conflicts with your network setup 2. Required Configuration Here's a more robust configuration that should help prevent the fallback to local:

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner
from dask_cloudprovider.aws import FargateCluster
import time

def create_fargate_cluster():
    return FargateCluster(
        image="daskdev/dask:latest",
        n_workers=2,
        fargate_use_private_ip=True,
        scheduler_timeout="30 minutes",  # Increased timeout
        find_address_timeout=300,  # 5 minutes to find the scheduler
        skip_cleanup=True,  # Helpful for debugging
        environment={  # Add any needed environment variables
            "EXTRA_PIP_PACKAGES": "prefect-dask dask-cloudprovider",
        },
        # AWS Configuration
        region_name="us-east-1",  # Your region
        vpc="vpc-xxxxx",
        cluster="your-ecs-cluster",
        subnets=["subnet-xxxxx"],
        security_groups=["sg-xxxxx"],
        # Logging configuration
        cloudwatch_logs_group="/dask/fargate",
        cloudwatch_logs_stream_prefix="dask-",
    )

@flow(
    task_runner=DaskTaskRunner(
        cluster_kwargs={"cluster_class": create_fargate_cluster}
    )
)
def my_dask_flow():
    # Your flow code here
    pass

# Add logging configuration at the TOP of your script
import logging
logging.basicConfig(level=<http://logging.INFO|logging.INFO>)
distributed_logger = logging.getLogger("distributed")
distributed_logger.setLevel(logging.DEBUG)
cloudprovider_logger = logging.getLogger("dask_cloudprovider")
cloudprovider_logger.setLevel(logging.DEBUG)

# Add handlers for better console output
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
distributed_logger.addHandler(handler)
cloudprovider_logger.addHandler(handler)

3. Security Group Requirements Make sure your security groups allow: - Inbound TCP 8786 (Dask scheduler) - Inbound TCP 8787 (Dask dashboard) - All TCP traffic between workers and scheduler (easiest within the same security group) - Outbound internet access for pulling Docker images 4. VPC Requirements - Ensure your VPC has: - A NAT Gateway (for private subnets) - An Internet Gateway (for public subnets) - Proper routing tables - DNS hostnames enabled - DNS resolution enabled 5. Troubleshooting Steps 1. Check ECS console for the scheduler task logs 2. Enable detailed logging as shown above 3. Increase timeouts to allow for slower startup 4. Use

skip_cleanup=True

to keep resources for investigation 5. Check CloudWatch logs for the scheduler and worker tasks If you're still seeing issues after implementing these changes, please share: 1. The complete logs from the scheduler task in ECS 2. The security group configurations 3. Whether the VPC is using public or private subnets