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<@ULVA73B9P> I'm using the dask task_runner and wa...
# ask-marvin
d
@Marvin I'm using the dask task_runner and want to create a temporary cluster on AWS ECS. Is there a way so that each flow-run creates a new Log group on CloudWatch? Right now all my runs default to using the 
dask-ecs
log group
m
❌ Error: No module named 'prefect_dask'
✅ thought for 65.3 seconds
Based on my research, I can provide some information about your CloudWatch logging situation with Dask on ECS, though I need to note that there isn't a direct built-in way to create a new log group per flow run. Here's what you should know: 1. By default, when using Dask with ECS, logs are sent to the 
dask-ecs
log group in CloudWatch as you've noticed. 2. While you can't automatically create a new log group per flow run through Prefect's built-in configuration, you have a few potential workarounds: a. You can modify the log group configuration when creating the FargateCluster by specifying custom container definitions. This would need to be done in your cluster configuration. b. You could create a custom logging configuration that includes the flow run ID in the log stream name within the existing log group, which would help with organization. Here's a basic example of how you might customize the logging configuration:
Copy code
python
from dask_cloudprovider.aws import FargateCluster
from prefect import task, flow
from prefect_dask.task_runners import DaskTaskRunner

def create_fargate_cluster(n_workers=2):
    return FargateCluster(
        n_workers=n_workers,
        container_definitions=[{
            # Your existing container definition settings
            'logConfiguration': {
                'logDriver': 'awslogs',
                'options': {
                    'awslogs-group': f'your-custom-log-group-name',
                    'awslogs-region': 'your-region',
                    'awslogs-stream-prefix': 'dask'
                }
            }
        }]
    )

@flow(task_runner=DaskTaskRunner(
    cluster_class=create_fargate_cluster,
    cluster_kwargs={"n_workers": 2}
))
def your_flow():
    # Your flow code here
    pass
Important notes: 1. You'll need to ensure your ECS task role has the appropriate permissions to create and write to CloudWatch log groups if you want to use custom log groups. 2. The exact configuration might need to be adjusted based on your specific AWS setup and requirements. 3. Be aware that creating new log groups for each flow run could have cost implications and require additional CloudWatch log group management. If you really need separate log groups per flow run, you might need to implement a custom solution that: 1. Creates a new log group before the flow run starts 2. Passes that log group name to your Dask cluster configuration 3. Handles cleanup of old log groups according to your retention requirements Would you like me to provide more specific details about any of these approaches, or would you like to explore a particular solution further?
d
@Marvin I noticed that the custom log settings are in 
container_definitions
, is this related to the 
image
cluster_kwarg that can be passed in? If I'm passing in a custom image, could I add these log configurations to that image I'm using?
m
✅ thought for 32.0 seconds
Let me explain the key differences and relationships: 1. Custom Images (
image
parameter) vs Container Definitions: - The 
image
parameter specifies WHAT container image to use (the actual Docker image) - 
container_definitions
specify HOW the container should run (including environment, logging, resources, etc.) - These are separate concerns - logging configuration cannot be embedded in the image itself 2. Container Definitions vs Docker Image: - Docker image: Contains your code, dependencies, and filesystem - Container definitions: Runtime configuration that ECS uses to determine how to run containers - Logging configuration belongs in container definitions because it's about how the container interfaces with AWS services Here's an example to illustrate:
Copy code
python
from dask_cloudprovider.aws import FargateCluster
from prefect_dask.task_runners import DaskTaskRunner

def create_fargate_cluster(n_workers=2):
    return FargateCluster(
        n_workers=n_workers,
        image="your-custom-image:latest",  # WHAT to run
        container_definitions=[{
            # HOW to run it
            'logConfiguration': {
                'logDriver': 'awslogs',
                'options': {
                    'awslogs-group': 'your-custom-log-group',
                    'awslogs-region': 'your-region',
                    'awslogs-stream-prefix': 'dask'
                }
            },
            # Other container configuration options
            'memory': '4096',
            'cpu': '1024',
            # ... other settings
        }]
    )

@flow(task_runner=DaskTaskRunner(
    cluster_class=create_fargate_cluster,
    cluster_kwargs={"n_workers": 2}
))
def your_flow():
    # Your flow code here
    pass
Important points: 1. You cannot embed the CloudWatch logging configuration in your custom image - it must be specified in the container definitions 2. The custom image you're using will still respect the logging configuration specified in the container definitions 3. If you need to modify logging behavior that happens INSIDE your application, that would go in your custom image 4. The container definitions control how ECS handles the container's logs at the platform level Would you like me to provide more specific details about what can be configured in container definitions, or would you like to explore how to combine custom images with specific logging configurations?
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