<@ULVA73B9P> Hi, I have hosted my prefect service ...
# ask-community
s
@Marvin Hi, I have hosted my prefect service in an ecs service as a task FARGATE. And i'm using ecs integration with task definitions and task def containes the cpu and memory configuration. Now i am using run_deployment to call my deployment wehich is associated with a workpool which contains the task def whioch contains the cpu and memory configuration. But my requirement is to change the cpu and memory dynamically while calling the deployment using run_deployment. Like i want to call the deployment with memory and cpu configuration on my own to with a single workpool to override task definitojn cpu and memory configuration. Give me a solution for this. Please note Here i'm using run_deployment() method. Please let me know how i can use this override to apply for a single task/flow run alone.
m
thought for 49.0 seconds
Based on the research and signature verification, I can provide you with a solution to dynamically override CPU and memory configurations for your ECS tasks. Here's how you can do it using `run_deployment()`:
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python
from prefect import run_deployment

# Define your ECS-specific job variables
ecs_job_variables = {
    "cpu": "1024",  # Example: 1 vCPU
    "memory": "2048"  # Example: 2GB RAM
}

# Run the deployment with custom CPU and memory
flow_run = run_deployment(
    name="your-deployment-name/deployment-name",
    job_variables=ecs_job_variables
)
The
job_variables
parameter allows you to override infrastructure-specific settings on a per-run basis. When using an ECS work pool, you can specify: - `cpu`: The CPU units (in AWS ECS units where 1024 = 1 vCPU) - `memory`: The memory in MB A more complete example showing different resource configurations:
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python
# Example 1: Running with minimal resources
flow_run_small = run_deployment(
    name="your-deployment-name/deployment-name",
    job_variables={
        "cpu": "256",    # 0.25 vCPU
        "memory": "512"  # 512MB RAM
    }
)

# Example 2: Running with larger resources
flow_run_large = run_deployment(
    name="your-deployment-name/deployment-name",
    job_variables={
        "cpu": "4096",    # 4 vCPU
        "memory": "8192"  # 8GB RAM
    }
)
Important notes: 1. The values for CPU and memory must be valid according to AWS Fargate's supported configurations. Not all CPU/memory combinations are valid in Fargate. 2. These overrides will only apply to this specific flow run and won't affect the default configuration in your task definition. 3. The values should be provided as strings, following AWS ECS conventions. 4. This override will only affect the specific task/flow run you're executing, not other runs of the same deployment. If you need to verify that the override worked, you can check the AWS ECS console for the specific task that was created for your flow run, where you should see the custom CPU and memory values being applied. Would you like me to provide more specific examples for certain CPU/memory combinations, or would you like to know more about valid Fargate configurations that you can use?