Based on the research, I can provide a comprehensive answer about

cluster_kwargs

for the Dask task runner. The specific arguments you can pass depend on the type of cluster you're creating. Here are the main cluster types and their key configurations: 1. Local Cluster (Default)

python
from prefect_dask import DaskTaskRunner

# Example with local cluster
runner = DaskTaskRunner(
    cluster_kwargs={
        "n_workers": 2,          # Number of workers
        "threads_per_worker": 2,  # Threads per worker
        "memory_limit": "2GB"     # Memory limit per worker
    }
)

2. SLURM Cluster (using

dask_jobqueue.SLURMCluster

)

python
from dask_jobqueue import SLURMCluster
from prefect_dask import DaskTaskRunner

runner = DaskTaskRunner(
    cluster_class=SLURMCluster,
    cluster_kwargs={
        "account": "my_account",
        "queue": "my_queue",
        "cores": 8,
        "memory": "16GB",
        "walltime": "02:00:00",
        "job_extra_directives": ["--exclusive"]
    }
)

3. Kubernetes Cluster (using

dask_kubernetes.operator.KubeCluster

)

python
runner = DaskTaskRunner(
    cluster_class="dask_kubernetes.operator.kubecluster.kubecluster.KubeCluster",
    cluster_kwargs={
        "image": "your-docker-image",
        "name": "dask-cluster-name",
        "namespace": "your-namespace",
        "resources": {
            "requests": {"memory": "4Gi", "cpu": "2"},
            "limits": {"memory": "8Gi", "cpu": "4"}
        }
    }
)

Important notes: 1. You'll need to install the appropriate packages: - For basic usage:

pip install prefect-dask

- For SLURM:

pip install dask-jobqueue

- For Kubernetes:

pip install dask-kubernetes

2. The exact

cluster_kwargs

you can use depend on the underlying cluster class you're using. You can always check the specific cluster class's documentation for all available options: - Dask Local Cluster - Dask JobQueue (for SLURM, PBS, etc.) - Dask Kubernetes 3. If you're having trouble configuring the cluster, you can inspect the job script (for job queue systems) using:

python
print(cluster.job_script())  # After cluster creation

4. When using resource annotations (like GPU resources), make sure to properly configure them in your cluster setup, as they need to be explicitly defined on the worker level. Would you like me to elaborate on any specific cluster type or configuration option?

For using Dask with AWS Fargate, you'll need to use the

dask-cloudprovider

package, which provides the

FargateCluster

implementation. Here's how to set it up: 1. First, install the required package:

bash
pip install dask-cloudprovider

2. Then you can configure the DaskTaskRunner like this:

python
from prefect_dask import DaskTaskRunner
from dask_cloudprovider.aws import FargateCluster

runner = DaskTaskRunner(
    cluster_class=FargateCluster,
    cluster_kwargs={
        # AWS Configuration
        "region_name": "your-region",  # e.g., "us-east-1"
        "aws_access_key_id": "your-access-key",  # Optional if using IAM roles
        "aws_secret_access_key": "your-secret-key",  # Optional if using IAM roles
        
        # Fargate Specific Configuration
        "n_workers": 2,  # Number of worker containers to launch
        "image": "your-docker-image",  # Docker image with Dask and your dependencies
        
        # Network Configuration
        "vpc": "vpc-xxxxxx",  # Your VPC ID
        "subnet": "subnet-xxxxxx",  # Your subnet ID
        "security_groups": ["sg-xxxxxx"],  # Security group IDs
        
        # Resource Configuration
        "cpu": 1024,  # CPU units (1024 = 1 vCPU)
        "memory": 4096,  # Memory in MB
        
        # Optional: Role ARNs if you want to specify exact roles
        "task_role_arn": "arn:aws:iam::...:role/your-task-role",
        "execution_role_arn": "arn:aws:iam::...:role/your-execution-role"
    }
)

@flow(task_runner=runner)
def my_flow():
    # Your flow code here
    pass

Important notes: 1. IAM Permissions: Your AWS credentials or IAM role need specific permissions to create and manage Fargate resources. The minimal set of permissions includes:

- ecs:DescribeTasks
   - ecs:ListAccountSettings
   - ecs:RegisterTaskDefinition
   - ecs:RunTask
   - ecs:StopTask
   - ecs:ListClusters
   - ecs:DescribeClusters
   - ecs:ListTaskDefinitions
   - ecs:DescribeTaskDefinition
   - ecs:DeregisterTaskDefinition
   - logs:DescribeLogGroups
   - logs:GetLogEvents

2. Docker Image: You need a Docker image that contains: - Dask - Prefect - Your flow's dependencies - The prefect-dask package 3. Network Configuration: Make sure your VPC and security groups are properly configured: - The security group should allow internal communication between the scheduler and workers - If you need internet access (e.g., for pulling Docker images), ensure your subnet has a route to an Internet Gateway 4. Cost Considerations: Remember that Fargate charges by the second based on the CPU and memory configuration you specify. The cluster will be created when the flow starts and terminated when it completes. 5. Debugging: You can access the Fargate task logs through CloudWatch Logs to debug any issues. Would you like me to elaborate on any of these aspects, such as the Docker image requirements or the networking configuration?