You can override the CPU and memory settings for your ECS tasks in two ways: 1. Via the ECS Worker configuration: When creating your ECS worker, you can specify the default CPU and memory settings:

python
from prefect_aws.workers import ECSWorker

worker = ECSWorker(
    cpu="1024",  # 1 vCPU
    memory="2048",  # 2 GB
    # other configuration options...
)

2. Via the deployment configuration: You can override these settings per deployment using the

job_configuration

parameter:

python
from prefect import flow
from prefect_aws import AwsCredentials

@flow
def my_flow():
    # your flow code here
    pass

if __name__ == "__main__":
    my_flow.from_source(
        source="<https://github.com/your-repo>",
        entrypoint="path/to/file.py:my_flow",
    ).deploy(
        name="my-deployment",
        work_pool_name="my-ecs-pool",
        job_configuration={
            "cpu": "4096",  # 4 vCPU
            "memory": "16384",  # 16 GB
        }
    )

A few important notes: - CPU values are in AWS CPU units (1024 = 1 vCPU) - Memory values are in MB - Make sure your values are compatible with AWS Fargate's supported combinations of CPU and memory - The values you specify must be within the limits set by your ECS task role and AWS account quotas For Fargate, here are the valid CPU and memory combinations: - CPU (vCPU) : Memory (GB) options - 0.25 vCPU (256): 0.5GB, 1GB, 2GB - 0.5 vCPU (512): 1GB, 2GB, 3GB, 4GB - 1 vCPU (1024): 2GB, 3GB, 4GB, 5GB, 6GB, 7GB, 8GB - 2 vCPU (2048): Between 4GB and 16GB - 4 vCPU (4096): Between 8GB and 30GB - 8 vCPU (8192): Between 16GB and 60GB - 16 vCPU (16384): Between 32GB and 120GB

@Marvin my flow is defined in python but my deployment is in yaml. can you give me the sample yaml

Here's a sample

prefect.yaml

file that shows how to configure CPU and memory for your ECS tasks:

yaml
deployments:
  - name: my-flow-deployment
    entrypoint: path/to/your/flow.py:my_flow  # adjust to your flow's location
    work_pool:
      name: my-ecs-pool
      job_variables:
        cpu: "1024"        # 1 vCPU
        memory: "2048"     # 2 GB RAM

For a more complete example with additional common configurations:

yaml
deployments:
  - name: my-flow-deployment
    entrypoint: path/to/your/flow.py:my_flow
    work_pool:
      name: my-ecs-pool
      job_variables:
        # Task resource configuration
        cpu: "4096"        # 4 vCPU
        memory: "16384"    # 16 GB RAM
        
        # Optional: Additional ECS-specific configurations
        network_configuration:
          awsvpcConfiguration:
            subnets:
              - subnet-xxxxxxxxxxxxxx
            securityGroups:
              - sg-xxxxxxxxxxxxxx
            assignPublicIp: ENABLED
        
        # Optional: Environment variables for your flow
        env:
          MY_ENV_VAR: "value"
          DATABASE_URL: "postgresql://..."

Remember: - CPU values are in AWS CPU units (1024 = 1 vCPU) - Memory values are in MB - Make sure to use values that are valid Fargate combinations (as mentioned in my previous response) - The values should be strings (hence the quotes around numbers) - Your work pool must already be created and configured as an ECS work pool You can deploy this configuration using:

bash
prefect deploy --all

For ECS workers, you can configure this by customizing your task definition to use

uv run

instead of

python

. Here's how to do it in your deployment YAML:

yaml
deployments:
  - name: my-flow-deployment
    entrypoint: path/to/your/flow.py:my_flow
    work_pool:
      name: my-ecs-pool
      job_variables:
        command:
          - "uv"
          - "run"
          - "python"
          - "-m"
          - "prefect.engine"

Alternatively, if you're using a custom Docker image for your flows, you can bake this configuration into your Dockerfile:

dockerfile
FROM prefecthq/prefect:2-python3.11

# Install uv
RUN curl -LsSf <https://astral.sh/uv/install.sh> | sh

# Set uv as the default Python interpreter
ENTRYPOINT ["uv", "run", "python", "-m", "prefect.engine"]

Then reference this custom image in your deployment configuration:

yaml
deployments:
  - name: my-flow-deployment
    entrypoint: path/to/your/flow.py:my_flow
    work_pool:
      name: my-ecs-pool
      job_variables:
        image: your-custom-image:tag

A few important notes: - Make sure

uv

is installed in your container image - Test the configuration thoroughly in a development environment first - Remember that

uv run

might behave slightly differently than the standard Python interpreter in some edge cases - If you're using virtual environments, make sure they're properly configured to work with

uv

Based on this behavior, there are a few likely causes and solutions to try: 1. Most Likely Issue: The

uv run

command might not be properly forwarding signals to the Python process. Try modifying your command to use

uv run python

directly instead of trying to replace the Python interpreter entirely:

yaml
deployments:
  - name: my-flow-deployment
    entrypoint: path/to/your/flow.py:my_flow
    work_pool:
      name: my-ecs-pool
      job_variables:
        command: ["sh", "-c", "uv run python -m prefect.engine"]

2. Alternative Approach: Instead of using

uv run

, consider using

uv

only for dependency management and letting Python handle the execution:

dockerfile
FROM prefecthq/prefect:2-python3.11

# Install uv
RUN curl -LsSf <https://astral.sh/uv/install.sh> | sh

# Use uv for installing dependencies
COPY requirements.txt .
RUN uv pip install -r requirements.txt

# Keep the standard Python interpreter for execution
ENTRYPOINT ["python", "-m", "prefect.engine"]

3. Debugging Steps: - Check the ECS task logs for any error messages - Verify that your task has the necessary network access to reach the Prefect API - Ensure your task has sufficient memory and CPU resources - Try running a simple test flow with minimal dependencies to isolate the issue 4. Network Configuration: Make sure your ECS task has the proper network configuration:

yaml
deployments:
  - name: my-flow-deployment
    entrypoint: path/to/your/flow.py:my_flow
    work_pool:
      name: my-ecs-pool
      job_variables:
        network_configuration:
          awsvpcConfiguration:
            subnets:
              - subnet-xxxxxxxxxxxxxx
            securityGroups:
              - sg-xxxxxxxxxxxxxx
            assignPublicIp: ENABLED  # If needed for internet access

5. Logging Configuration: Add more verbose logging to help diagnose the issue:

yaml
deployments:
  - name: my-flow-deployment
    entrypoint: path/to/your/flow.py:my_flow
    work_pool:
      name: my-ecs-pool
      job_variables:
        env:
          PREFECT_LOGGING_LEVEL: "DEBUG"

Could you try the first solution (using

["sh", "-c", "uv run python -m prefect.engine"]

) and let me know if that resolves the hanging issue? If it doesn't, we can try the other approaches or investigate further based on any error messages you see in the logs.

The issue is that dependencies should be installed at container build time, not at runtime. Here's how to fix this: 1. First, create a custom Dockerfile that pre-installs all dependencies:

dockerfile
FROM prefecthq/prefect:2-python3.11

# Install uv
RUN curl -LsSf <https://astral.sh/uv/install.sh> | sh

# Copy your requirements file
COPY requirements.txt .

# Install dependencies during build
RUN uv pip install -r requirements.txt

# Set the entrypoint to use Python directly (not uv run)
ENTRYPOINT ["python", "-m", "prefect.engine"]

2. Build and push this image to your container registry (ECR or similar). 3. Update your deployment YAML to use this custom image:

yaml
deployments:
  - name: my-flow-deployment
    entrypoint: path/to/your/flow.py:my_flow
    work_pool:
      name: my-ecs-pool
      job_variables:
        image: <http://your-registry.amazonaws.com/your-image:tag|your-registry.amazonaws.com/your-image:tag>
        # Remove any command override if you had one
        # The ENTRYPOINT from the Dockerfile will be used

Key points: - Dependencies should be installed during image build, not at runtime - The container should be ready to run immediately when ECS starts it - Using

uv

for runtime execution isn't necessary - it's better used as a build-time tool - The standard Python interpreter from the container should be used to run the flow If you need to update dependencies: 1. Update your requirements.txt 2. Rebuild the Docker image 3. Push the new image 4. Update your deployment to use the new image tag This approach will be much more reliable and start faster since all dependencies are pre-installed in the image.