In terms of providing credentials, I used environment variables (ie. setting $AWS_PROFILE)

the kwargs should go in

cluster_kwargs

I would think

@Marvin can you dig up details on the signature and available kwargs for DaskTaskRunner?

I got the idea of passing in the callable as the

cluster_class

from this github issue on the repository, specifically Anna Geller's implementation

Is there a preferred way to provide AWS credentials when calling the dask runner? Like I mentioned before I've been using the $AWS_PROFILE environment variable

@Marvin Yes, please help in integrating ECS with the DaskTaskRunner. Moreover, what would be a good way for me to provide AWS credentials in case that is the issue? Right now I'm using the environment variable $AWS_PROFILE

Based on the research, I can provide you with a comprehensive guide for setting up DaskTaskRunner with ECS. Here's how to do it: 1. First, install the required packages:

bash
pip install prefect-dask "dask-cloudprovider[aws]" dask

2. Here's how to configure the DaskTaskRunner with ECS:

python
from prefect_dask import DaskTaskRunner
from dask_cloudprovider.aws import FargateCluster

runner = DaskTaskRunner(
    cluster_class=FargateCluster,
    cluster_kwargs={
        "image": "daskdev/dask:latest",  # You can customize this
        "n_workers": 2,
        "fargate_use_private_ip": False,  # Set to True if using private subnets
        "aws_region": "your-region",      # e.g., "us-east-1"
        "environment": {
            "AWS_PROFILE": "${AWS_PROFILE}",  # This will use your environment variable
        }
    },
    adapt_kwargs={
        "minimum": 1,
        "maximum": 10
    }
)

@flow(task_runner=runner)
def your_flow():
    # your flow code here
    pass

3. For AWS credentials, since you're using AWS_PROFILE, there are several approaches you can take: a. Environment Variables (Your current approach):

python
   # The environment section in cluster_kwargs above will handle this
   # Make sure AWS_PROFILE is set in your environment

b. Alternatively, you can use explicit credentials:

python
   cluster_kwargs={
       # ... other kwargs ...
       "environment": {
           "AWS_ACCESS_KEY_ID": "your-access-key",
           "AWS_SECRET_ACCESS_KEY": "your-secret-key",
           "AWS_SESSION_TOKEN": "your-session-token",  # if using temporary credentials
       }
   }

4. Important considerations: - Ensure your IAM role/user has the necessary permissions for ECS operations. The minimum required permissions include: -

ecs:CreateCluster

-

ecs:RegisterTaskDefinition

-

ecs:RunTask

-

ecs:DescribeTasks

-

ecs:ListTasks

-

iam:GetRole

-

iam:PassRole

-

ec2:DescribeSubnets

-

ec2:DescribeVpcs

-

ec2:DescribeSecurityGroups

- If you're running in a VPC, make sure to configure: - Proper subnet access - Security group rules - NAT Gateway or VPC Endpoints for private subnets 5. Container Image Considerations: - The default

daskdev/dask:latest

image should work for basic operations - For custom dependencies, you might need to build your own image based on the Dask image - Ensure your image has all the required Python packages installed If you run into any issues with task deserialization (which can happen), you might need to ensure that: 1. All dependencies are consistently installed across your local environment and the Dask workers 2. The Python versions match between your local environment and the workers 3. Any custom objects you're passing to tasks are properly serializable Would you like me to elaborate on any of these aspects, particularly around the container image configuration or IAM roles setup?