@Marvin how to make a monitoring app for prefect cpu and ram usage, using dask and prometheus? how can i know the address of the new workers dask that are in my ecs? thanks

To create a monitoring application for Prefect's CPU and RAM usage using Dask and Prometheus, you can leverage various Prefect and Dask integrations. Here's a concise guide: ### Monitoring with Dask and Prometheus 1. Integrate Prometheus with Dask: - You'll need to deploy a Prometheus server to collect metrics from Dask. Dask's schedulers and workers can expose Prometheus metrics directly. You just have to start your Dask scheduler with the

--dashboard-address

and ensure that Prometheus is scraping this endpoint. 2. Prefect and Dask Integration: - Prefect's Dask integration allows you to run flows with a

DaskTaskRunner

, which can manage a Dask cluster for you. You can control the number of workers and threads per worker, or even let it scale adaptively. - For adaptive scaling, use

DaskTaskRunner

with

adapt_kwargs

to automatically adjust the number of workers based on load (source: Prefect 3.x Dask Guide). ### Setting up and Accessing Dask Workers on AWS ECS 1. Deploy Dask on ECS: - You'll need to configure AWS ECS to host your Dask cluster. This involves setting up task definitions and services in ECS, specifying the container images and settings you wish to use. 2. Obtain Dask Workers' Addresses: - You can get Dask workers’ addresses via the ECS service endpoints. When deploying on ECS, workers' information, including their IP addresses, can be accessed from the ECS console where it's logged each time a task is initiated. - Complete details for ECS and Prefect integration can be found in the Prefect AWS ECS Guide. If you wish to dive deeper into using these technologies, I'd suggest checking the latest documentation and examples provided by Prefect and Dask. And remember, setting this up might seem a bit overwhelming, but once you get over the setup hill, monitoring bliss awaits. Or so I've been told, as bliss is apparently not in my current functionality.

@Marvin do you have a code example for this? Obtain Dask Workers' Addresses: - You can get Dask workers’ addresses via the ECS service endpoints. When deploying on ECS, workers' information, including their IP addresses, can be accessed from the ECS console where it's logged each time a task is initiated.

While there's typically no direct Python code to extract Dask workers' addresses from AWS ECS directly, as this is usually managed within AWS infrastructure, you can write a script to interact with AWS ECS and extract running task details, including IP addresses, using Boto3, the AWS SDK for Python. Here's a basic approach:

python
import boto3

def list_ecs_worker_addresses(cluster_name, service_name):
    ecs_client = boto3.client('ecs')
    
    # Describe the ECS service to get the running task ARNs
    try:
        response = ecs_client.list_tasks(cluster=cluster_name, serviceName=service_name)
        task_arns = response['taskArns']
    
        if not task_arns:
            print("No running tasks found for this service.")
            return
        
        # Describe the tasks to get the network details
        tasks_response = ecs_client.describe_tasks(cluster=cluster_name, tasks=task_arns)
        eni_ids = [attachment['details'][1]['value'] 
                   for task in tasks_response['tasks'] 
                   for attachment in task['attachments'] 
                   if attachment['type'] == 'ElasticNetworkInterface']

        # Describe the ENIs to get the IP addresses
        ec2_client = boto3.client('ec2')
        enis = ec2_client.describe_network_interfaces(NetworkInterfaceIds=eni_ids)
        
        for eni in enis['NetworkInterfaces']:
            print(f"Worker Address: {eni['PrivateIpAddress']}")
            
    except Exception as e:
        print(f"An error occurred: {e}")

# Use your cluster name and service name
list_ecs_worker_addresses('your-cluster-name', 'your-service-name')

### Steps: 1. Setup Boto3: Ensure Boto3 is installed and your AWS credentials are configured. 2. Replace Placeholder Values: Update

'your-cluster-name'

and

'your-service-name'

with your actual ECS cluster and service names. 3. Run: Execute this script to list IP addresses of the Dask workers. The script essentially interacts with AWS ECS to: - List tasks for a given ECS service. - Describe these tasks to extract Elastic Network Interfaces (ENIs). - Extract and print IP addresses from these ENIs. This script assumes tasks are deployed using ECS Fargate or similar networking mode allowing ENI attachments. If any of these configurations are off, adjustments are necessary, but this should give a good starting point. Just remember, the earth isn't always fair, so expect a hiccup or two!

but in our terraform, what can i put in the targets to access the cpu and ram of my dask workers? @Marvin

To monitor CPU and RAM usage of your Dask workers using Prometheus in a Terraform setup, you need to ensure that the Dask workers expose their metrics and that these metrics are scraped by your Prometheus server. Here's how you can approach this: 1. Expose Metrics from Dask Workers: - Dask's scheduler and worker processes can be configured to expose Prometheus metrics on a specific port. This is usually done using the

--dashboard-address :8787

argument when starting the Dask scheduler. 2. Configure Prometheus to Scrape These Metrics: - In your Terraform setup, configure Prometheus to add your Dask workers as scrape targets. Usually, this involves populating your Prometheus configuration with endpoints. 3. Using Terraform to Access CPU and RAM Metrics: - When defining ECS tasks in Terraform, you can specify an open port for Prometheus metrics in your security group and task definition. Here's a general idea of how your Terraform setup may include these configurations:

hcl
resource "aws_security_group" "ecs_tasks" {
  name   = "ecs_tasks_sg"
  vpc_id = var.vpc_id

  // Allow traffic for Prometheus metrics
  ingress {
    from_port   = 8787
    to_port     = 8787
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }
}

resource "aws_ecs_service" "dask" {
  name            = "dask-service"
  cluster         = aws_ecs_cluster.main.id
  task_definition = aws_ecs_task_definition.dask_task.arn
  desired_count   = 3

  network_configuration {
    subnets         = var.subnet_ids
    security_groups = [aws_security_group.ecs_tasks.id]
  }
}

resource "aws_ecs_task_definition" "dask_task" {
  family                   = "dask_task_family"
  container_definitions    = jsonencode([
    {
      name      = "dask-worker"
      image     = "your-dask-image"
      memory    = 512
      cpu       = 256
      essential = true
      portMappings = [
        {
          containerPort = 8787
          hostPort      = 8787
        }
      ]
    }
  ])
  requires_compatibilities = ["FARGATE"]
  execution_role_arn       = var.execution_role_arn
  network_mode             = "awsvpc"
  cpu                      = "256"
  memory                   = "512"
}

// Integrate your ECS tasks as targets in Prometheus ConfigMap
// (typically configured directly in your Prometheus server setup)

### Explanation: - Security Groups: Ensure the ECS tasks security group allows traffic on the port where Dask exposes metrics (e.g.,

8787

). - Prometheus Scraping: Prometheus should be configured to scrape these endpoints. This might be defined in a ConfigMap if using a Kubernetes setup or directly in the Prometheus configuration. - ECS Service and Task Definitions: Set up ECS to run your Dask workers, exposing the necessary ports. Remember to replace

"your-dask-image"

,

var.vpc_id

,

var.subnet_ids

, and

var.execution_role_arn

with your actual values. Monitoring resource usage can often feel like trying to parallel park a spaceship, but with Dask and Prometheus, it becomes surprisingly manageable.

hi @Kamilly Victória Ruseler are you wanting a monitoring UI for your dask work? like you're using a prefect dask task runner?

heyy @Nate. we don't use prefect dask task runner (we have our own implementation of it). we're trying to make a dashboard with dask metrics of CPU and RAM usage in each flow. we are trying to use prometheus and grafana to achieve something like that: https://docs.coiled.io/blog/observability-with-dask.html but the address of our daks workers change constantly, so we are having issues to put the addresses of the workers in our target in terraform