@Marvin Describe how to override the default infrastructure parameter for memory on a worker running in and ECS work pool.

To override the default memory parameter for a worker running in an ECS work pool, you'll need to specify the desired memory configuration in your ECS task definition. Unfortunately, it seems like the documentation does not provide a direct example, so you'll have to refer to the detailed guide on Prefect's ECS integration. Here's a step-by-step outline based on generic ECS setup principles and what you're likely to encounter: 1. Create a Custom ECS Task Definition: Update your ECS task definition to set the memory limit. This is a typical ECS setup step:

json
   {
     "family": "my-prefect-task-definition",
     "containerDefinitions": [
       {
         "name": "my-prefect-container",
         "image": "<your-prefect-image>",
         "essential": true,
         "memory": 1024,  // Set your desired memory limit here
         "cpu": 512,
         ...
       }
     ],
     ...
   }

2. Register the Task Definition in ECS: Use the AWS CLI or the AWS Management Console to register your task definition. 3. Update Work Pool to Use the Custom Task Definition: In your Prefect Work Pool, configure it to use the task definition you've just registered. Make sure your ECS work pool is sourcing the correct task definition ARN. 4. Run the Worker: Ensure your Prefect worker running in the ECS work pool uses this task definition to honor the overridden memory settings. For more detailed steps and examples, refer to the ECS Worker Guide. This approach allows you to control the memory and other infrastructure resources for your Prefect flows executed within an ECS work pool. While setting all this up may feel like another futile attempt at seeking perfection in an imperfect universe, fortunately, it actually gets the job done—eventually.

@Marvin no search Override Job Variables

To override job variables for a worker in an ECS work pool, you would typically use the Prefect CLI or UI to set those variables. Here are the steps: ### Using the Prefect CLI When kicking off a deployment run via CLI, you can use the

--job-variable

flag to set job variables:

bash
prefect deployment run \
  --id "<deployment-id>" \
  --job-variable MEMORY=1024 \
  --job-variable CPU=512

### Using the Prefect UI In the Prefect UI, you can pass a dictionary of variables into your flow run infrastructure within the Work Pools section. ### Using Deployment YAML You can also define job variables directly in your deployment YAML file:

yaml
deployments:
  - name: demo-deployment
    entrypoint: demo_project/demo_flow.py:some_work
    work_pool:
      name: my-ecs-pool
      type: ecs
      job_variables:
        MEMORY: 1024
        CPU: 512

### Background on Job Variables Job variables are infrastructure-related values that are configurable on a work pool and can be overridden on a per-deployment or per-flow run basis. This allows you to dynamically change infrastructure settings as needed. Here are the sources for more detailed information: - Overriding Job Variables Guide - Work Pools Documentation This configuration flexibility means there's no need to be overly gloomy about adjusting your resources. It’s a necessary and manageable part of deploying robust, scalable Prefect workflows.

i think

memory

and

cpu

would be lowercase here, but thats more like it

Thanks @Nate