@Marvin have you every seen ECS containers fail with no log entries anda simple error message `

Reported flow run '...' as crashed: Flow run infrastructure exited with non-zero status code 1.

I am using Prefect Cloud and an ECS worker pool in my ecs environment.

It seems you're encountering an issue with ECS containers that crash with a non-zero status code 1 and no log entries. This is a known challenge when working with Prefect and ECS infrastructure. ### Potential Causes: 1. Infrastructure Mismatch: Ensure that the versions of Prefect used in both the agent and the flow are compatible. There have been issues reported where mismatched versions caused crashes (GitHub Issue). 2. Resource Constraints: ECS tasks may fail if they hit resource limits. Check the CPU and memory allocations for your ECS tasks. 3. Configuration Issues: Review your ECS task definitions and Prefect ECS configuration. Make sure they are set up correctly to handle the expected load and execution environment. ### Recommendations: - Logs and Monitoring: Use AWS CloudWatch to monitor your ECS tasks. Ensure that your logging configuration captures all necessary output. - Version Compatibility: Verify that both your Prefect agent and flow are running compatible versions. Consider testing with different versions if issues persist. - Review ECS Guide: For more details on setting up ECS with Prefect, refer to the ECS Worker Guide. If these steps do not resolve the issue, consider reaching out to Prefect support or checking the Prefect GitHub Issues for similar cases.

@Marvin so here's my flow deploy:

import os
import sys

# Ensure the parent directory is on the Python path.
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from flows.test_ecs_flow import my_flow

# from prefect_docker.deployments import push_docker_image, build_docker_image
from prefect.docker import DockerImage

# Define environment-specific values
# ECR_REPO = "dmp-etl-dev-ecr-repo"
ECR_REPO = "111111.dkr.ecr.ap-southeast-1.amazonaws.com/my-repo"

WORK_POOL_NAME = "ecs-work-pool"

# Optional: Use AWS environment variables if running in an AWS environment
AWS_REGION = os.getenv("AWS_REGION", "ap-southeast-1")

# ECS Task Role, Execution Role, and Networking Configurations
TASK_ROLE_ARN = "arn:aws:iam::111111:role/ecs-task-role"
EXECUTION_ROLE_ARN = (
    "arn:aws:iam::11111111:role/ecs-task-execution-role"
)
CLUSTER_NAME = "cluster_name"
SUBNETS = [
    "subnet-111111",
    "subnet-111111",
    "subnet-111111",
]
SECURITY_GROUPS = ["sg-111111"]


def main():

    # Define the Deployment (No `ECSTask` needed in Prefect 3)
    my_flow.deploy(
        name="test-ecs-deployment",
        work_pool_name=WORK_POOL_NAME,
        parameters={},
        description="Prefect 3 deployment for test ECS flow",
        version="1.0.0",
        image=DockerImage(
            name="prefecthq/prefect", tag="3-latest", dockerfile="Dockerfile.worker"
        ),
        job_variables={  # ✅ Overrides ECS settings per deployment
            "image": ECR_REPO,  # Image to use
            "cpu": 512,  # CPU Allocation
            "memory": 1024,  # Memory Allocation
            "cluster": CLUSTER_NAME,  # ECS Cluster Name
            "task_role_arn": TASK_ROLE_ARN,  # IAM Task Role
            "execution_role_arn": EXECUTION_ROLE_ARN,  # IAM Execution Role
            "subnets": SUBNETS,  # VPC Subnets
            "security_groups": SECURITY_GROUPS,  # Security Groups
            "assignPublicIp": "ENABLED",
            "networkConfiguration": {  # ✅ Ensures correct networking
                "awsvpcConfiguration": {
                    "subnets": [
                        "subnet-111111",
                        "subnet-111111",
                        "subnet-111111",
                    ],
                    "securityGroups": SECURITY_GROUPS,
                    "assignPublicIp": "ENABLED",
                }
            },
            "env": {  # Environment Variables (Secrets from AWS SSM & Secrets Manager)
                "PREFECT_API_URL": "arn:aws:ssm:ap-southeast-1:11111111:parameter/env/dev/PREFECT_API_URL",
                "PREFECT_API_KEY": "arn:aws:secretsmanager:ap-southeast-1:11111111:secret:/env/dev/PREFECT_API_KEY",
            },
        },
        push=True,
        build=True,
    )

(note, i have changed all unique numbers to 11111 for obfuscation) running this will result in the containter existing with 1. crashing.

Your message was too long by 792 tokens...

@Marvin so here's my flow deploy:

# includes

def main():

    # Define the Deployment (No `ECSTask` needed in Prefect 3)
    my_flow.deploy(
        name="test-ecs-deployment",
        work_pool_name=WORK_POOL_NAME,
        parameters={},
        description="Prefect 3 deployment for test ECS flow",
        version="1.0.0",
        image=DockerImage(
            name="prefecthq/prefect", tag="3-latest", dockerfile="Dockerfile.worker"
        ),
        job_variables={  # ✅ Overrides ECS settings per deployment
            "image": ECR_REPO,  # Image to use
            "cpu": 512,  # CPU Allocation
            "memory": 1024,  # Memory Allocation
            "cluster": CLUSTER_NAME,  # ECS Cluster Name
            "task_role_arn": TASK_ROLE_ARN,  # IAM Task Role
            "execution_role_arn": EXECUTION_ROLE_ARN,  # IAM Execution Role
            "subnets": SUBNETS,  # VPC Subnets
            "security_groups": SECURITY_GROUPS,  # Security Groups
            "assignPublicIp": "ENABLED",
            "networkConfiguration": {  # ✅ Ensures correct networking
                "awsvpcConfiguration": {
                    "subnets": [
                        "subnet-111111",
                        "subnet-111111",
                        "subnet-111111",
                    ],
                    "securityGroups": SECURITY_GROUPS,
                    "assignPublicIp": "ENABLED",
                }
            },
            "env": {  # Environment Variables (Secrets from AWS SSM & Secrets Manager)
                "PREFECT_API_URL": "arn:aws:ssm:ap-southeast-1:11111111:parameter/env/dev/PREFECT_API_URL",
                "PREFECT_API_KEY": "arn:aws:secretsmanager:ap-southeast-1:11111111:secret:/env/dev/PREFECT_API_KEY",
            },
        },
        push=True,
        build=True,
    )

(note, i have changed all unique numbers to 11111 for obfuscation) running this will result in the containter existing with 1. crashing.

Your message was too long by 263 tokens...

@Marvin do I need the tag on the ecr repo? ECR_REPO = "062920717978.dkr.ecr.ap-southeast-1.amazonaws.com/dmp-etl-dev-ecr-repo"

Yes, when specifying an ECR repository for your Docker image in ECS, you generally need to include the image tag. The tag specifies which version of the image to use. If you do not provide a tag, it defaults to

latest

, which may not be what you intend. Here's how you can specify it:

plaintext
ECR_REPO = "<http://062920717978.dkr.ecr.ap-southeast-1.amazonaws.com/dmp-etl-dev-ecr-repo:your-tag|062920717978.dkr.ecr.ap-southeast-1.amazonaws.com/dmp-etl-dev-ecr-repo:your-tag>"

Replace

your-tag

with the appropriate tag for the version of the image you want to use. This ensures that your ECS task pulls the correct image version from the ECR repository.

@Marvin does my job_variables look ok: Seems I am setting subnects twice. is that ok?

TASK_ROLE_ARN = "arn:aws:iam::111111:role/dmp-etl-dev-ecs-task-role"
EXECUTION_ROLE_ARN = (
    "arn:aws:iam::111111:role/dmp-etl-dev-ecs-task-execution-role"
)
CLUSTER_NAME = "ecs-cluster"
SUBNETS = [
    "subnet-111111",
    "subnet-111111",
    "subnet-111111",
]
SECURITY_GROUPS = ["sg-111111"]

job_variables={  # ✅ Overrides ECS settings per deployment
            "image": ECR_REPO,  # Image to use
            "cpu": 512,  # CPU Allocation
            "memory": 1024,  # Memory Allocation
            "cluster": CLUSTER_NAME,  # ECS Cluster Name
            # "service": "dmp-etl-dev-worker-ecs-service",  # TODO: Trying. dn't think this will work
            "task_role_arn": TASK_ROLE_ARN,  # IAM Task Role
            "execution_role_arn": EXECUTION_ROLE_ARN,  # IAM Execution Role
            "subnets": SUBNETS,  # VPC Subnets
            "security_groups": SECURITY_GROUPS,  # Security Groups
            "assignPublicIp": "ENABLED",
            "networkConfiguration": {  # ✅ Ensures correct networking
                "awsvpcConfiguration": {
                    "subnets": [
                        "subnet-111111",
                        "subnet-111111",
                        "subnet-111111",
                    ],
                    "securityGroups": SECURITY_GROUPS,
                    "assignPublicIp": "ENABLED",
                }
            },
            "env": {  # Environment Variables (Secrets from AWS SSM & Secrets Manager)
                "PREFECT_API_URL": "arn:aws:ssm:ap-southeast-1:062920717978:parameter/env/dev/PREFECT_API_URL",
                "PREFECT_API_KEY": "arn:aws:secretsmanager:ap-southeast-1:062920717978:secret:/env/dev/PREFECT_API_KEY",
            },
        },

Yes, you are indeed specifying the subnets twice in your

job_variables

. While it may not immediately cause a failure, it is redundant and could lead to confusion or unexpected behavior. Here's a streamlined version:

python
job_variables = {  # ✅ Overrides ECS settings per deployment
    "image": ECR_REPO,  # Image to use
    "cpu": 512,  # CPU Allocation
    "memory": 1024,  # Memory Allocation
    "cluster": CLUSTER_NAME,  # ECS Cluster Name
    "task_role_arn": TASK_ROLE_ARN,  # IAM Task Role
    "execution_role_arn": EXECUTION_ROLE_ARN,  # IAM Execution Role
    "networkConfiguration": {  # ✅ Ensures correct networking
        "awsvpcConfiguration": {
            "subnets": SUBNETS,  # VPC Subnets
            "securityGroups": SECURITY_GROUPS,  # Security Groups
            "assignPublicIp": "ENABLED",
        }
    },
    "env": {  # Environment Variables (Secrets from AWS SSM & Secrets Manager)
        "PREFECT_API_URL": "arn:aws:ssm:ap-southeast-1:062920717978:parameter/env/dev/PREFECT_API_URL",
        "PREFECT_API_KEY": "arn:aws:secretsmanager:ap-southeast-1:062920717978:secret:/env/dev/PREFECT_API_KEY",
    },
}

This version consolidates the subnet configuration under

networkConfiguration.awsvpcConfiguration

, which is the appropriate place for network settings in ECS. This should help maintain clarity and ensure the configuration is applied correctly.

@Marvin and tis is correct for prefect 3?

Yes, your configuration for

job_variables

in Prefect 3 appears to be correct. Prefect 3 uses a more flexible deployment model where you can specify infrastructure-specific settings, such as those for ECS, directly within your deployment configuration. ### Key Points: - Network Configuration: Placing the subnets and security groups under

networkConfiguration.awsvpcConfiguration

is appropriate for ECS tasks. - IAM Roles: Specifying

task_role_arn

and

execution_role_arn

is necessary for ECS tasks to have the correct permissions. - Environment Variables: Using AWS SSM and Secrets Manager for environment variables is a valid approach to manage sensitive information. For further details and examples, you may refer to the ECS Worker Guide in the Prefect documentation. This guide provides comprehensive information on configuring ECS tasks to run Prefect flows.

@Marvin one odd thing is sometimes it runs the flow container in a private subnet, not one I specify in the deplou script. why?

Running your ECS task in a subnet that you did not specify can occur due to several reasons. Here are some possibilities to consider: 1. Default VPC Configuration: If your ECS cluster is associated with a default VPC or if there is a misconfiguration in the ECS setup, it might default to using subnets that are not explicitly specified. 2. ECS Service Configuration: Check if there are any configurations at the ECS service or cluster level that might override the subnet settings specified in your deployment script. 3. IAM Permissions: Ensure that the IAM roles used by your ECS tasks have the necessary permissions to access the specified subnets. Lack of permissions might cause ECS to fall back to default configurations. 4. Network Configuration Mismatch: Double-check the

networkConfiguration

settings in your deployment script. Ensure that the subnets specified are correctly associated with the VPC and region that your ECS cluster is using. 5. AWS Console Overrides: If you've manually adjusted settings in the AWS Console for your ECS service or task definitions, those changes might conflict with your deployment script. Reviewing these areas should help identify why the ECS task might be running in unintended subnets. If the issue persists, consider checking the AWS CloudWatch logs for any indication of network-related errors or warnings.

@Marvin you mention ECS Service Configuration - the insturctions never told me to make an ECS service for the flow runners. onyl or the work queue.

In Prefect 3, typically, you don't need to manually create an ECS service for each flow run. Prefect uses ECS tasks to run flows, which are managed by the Prefect infrastructure. The ECS service you create is usually for running the Prefect agent or worker that manages these tasks. ### How It Works: - ECS Task: Each flow run is executed as an ECS task. Prefect dynamically launches and manages these tasks based on the flow runs queued in your work pool. - ECS Service: The service is usually set up to run the Prefect worker, which is responsible for polling the work queue and launching tasks as needed. Given this setup, if the subnets are not being honored as specified in your deployment script, it might be worth verifying: - Worker Configuration: Ensure that the ECS worker service is correctly set up with the intended network configurations. - Deployment Script: Double-check the deployment script for any discrepancies or overrides that might affect the network configuration. If everything seems correct and the problem persists, consider reviewing the AWS ECS console for any additional configurations or defaults that might be influencing the subnet selection.

@Marvin in my deplouy script DockerImage section, shoudl I point to the CCR image name or the prefecthq/prefect ?

In your deployment script, the

DockerImage

section should point to the image that contains your flow's environment and dependencies. Here's how you should decide: 1. Custom Image (ECR): If you have a custom Docker image in your ECR that includes all necessary dependencies, configurations, and possibly your flow code, you should use your ECR image name. This ensures that the environment is precisely tailored to your flow's requirements. 2. Prefect Base Image: If you are using the

prefecthq/prefect

image, it typically serves as a base image. You might want to extend it with your custom Dockerfile to include additional dependencies or configurations specific to your flow. ### Example: - Using ECR Image:

python
  image=DockerImage(name="<http://062920717978.dkr.ecr.ap-southeast-1.amazonaws.com/dmp-etl-dev-ecr-repo:your-tag|062920717978.dkr.ecr.ap-southeast-1.amazonaws.com/dmp-etl-dev-ecr-repo:your-tag>")

- Using Prefect Base Image: If you extend the Prefect base image in your Dockerfile:

dockerfile
  FROM prefecthq/prefect:3-latest
  # Add custom dependencies or configurations

In summary, if your ECR image is ready and includes everything your flow needs, use that. Otherwise, you might need to build a custom image starting from

prefecthq/prefect

and push it to ECR.

@Marvin I have this deploy (prefect 3): my_flow.deploy( name="test-ecs-deployment", work_pool_name=WORK_POOL_NAME, parameters={}, description="Prefect 3 deployment for test ECS flow", version="1.0.0", image=DockerImage( name="prefecthq/prefect", tag="3-latest", dockerfile="Dockerfile.flow" ), job_variables={ # ✅ Overrides ECS settings per deployment "image": ECR_REPO, # Image to use "cpu": 512, # CPU Allocation "memory": 1024, # Memory Allocation "cluster": CLUSTER_NAME, # ECS Cluster Name "task_role_arn": TASK_ROLE_ARN, # IAM Task Role "execution_role_arn": EXECUTION_ROLE_ARN, # IAM Execution Role "networkConfiguration": { # ✅ Ensures correct networking "awsvpcConfiguration": { "subnets": SUBNETS, "securityGroups": SECURITY_GROUPS, "assignPublicIp": "ENABLED", } }, "env": { # Environment Variables (Secrets from AWS SSM & Secrets Manager) "PREFECT_API_URL": PREFECT_API_URL, "PREFECT_API_KEY": PREFECT_API_KEY, }, }, push=True, build=True, )

Your message was too long by 1764 tokens...

@Marvin i don't understand the infrastructue section. I don't have one., DO i need? can you propose?

In Prefect 3, the infrastructure configuration is crucial for defining how and where your flow runs. You don't need to explicitly define an "infrastructure" section in your script, but you do need to ensure that your deployment configuration properly specifies the necessary details for running on ECS. ### Key Points: - Docker Image: Use the

DockerImage

configuration to specify the image your flow should use. This includes the image name and tag, and optionally a Dockerfile if you need to build a custom image. - ECS Configuration: Ensure that your ECS-specific settings, such as CPU, memory, and network configuration, are correctly specified in your

job_variables

. Here's how you can streamline your deployment script without needing an explicit "infrastructure" section:

python
my_flow.deploy(
    name="test-ecs-deployment",
    work_pool_name=WORK_POOL_NAME,
    parameters={},
    description="Prefect 3 deployment for test ECS flow",
    version="1.0.0",
    image=DockerImage(
        name=f"{ECR_REPO}:your-tag",  # Use your ECR image directly
        dockerfile="Dockerfile.flow"  # If you need to build an image
    ),
    job_variables={  # ECS settings
        "cpu": 512,
        "memory": 1024,
        "cluster": CLUSTER_NAME,
        "task_role_arn": TASK_ROLE_ARN,
        "execution_role_arn": EXECUTION_ROLE_ARN,
        "networkConfiguration": {
            "awsvpcConfiguration": {
                "subnets": SUBNETS,
                "securityGroups": SECURITY_GROUPS,
                "assignPublicIp": "ENABLED",
            }
        },
        "env": {
            "PREFECT_API_URL": PREFECT_API_URL,
            "PREFECT_API_KEY": PREFECT_API_KEY,
        },
    },
    push=True,
    build=True,
)

### Explanation: - DockerImage: This specifies the Docker image to use. If you're using an image from ECR, include the repository and tag directly. - ECS Settings: These are included in

job_variables

, which are passed to the ECS task when it runs. For more detailed examples and configurations, you can refer to the Prefect ECS Guide and the Docker Infrastructure Examples.

@Marvin ever seen this eeror from the task worker? exec /usr/bin/tini: exec format error

The error message

exec /usr/bin/tini: exec format error

typically indicates that there is a mismatch between the architecture of the Docker image and the platform on which it is running. Here are some steps to resolve this issue: 1. Check Image Architecture: Ensure that the Docker image you are using is built for the correct architecture (e.g.,

x86_64

for most ECS instances). You can verify the architecture of your Docker image using the command:

bash
   docker inspect --format='{{.Architecture}}' <image-name>

2. Rebuild the Image: If the architecture is incorrect, rebuild your Docker image on a machine with the correct architecture or specify the correct platform during the build process:

bash
   docker build --platform linux/amd64 -t <image-name> .

3. ECS Task Definition: Ensure that your ECS task definition is configured to use the correct platform. If you are using AWS Fargate, ensure that your task definition specifies the correct platform version. 4. Dockerfile: Check your Dockerfile to ensure that it does not inadvertently specify an incorrect base image or architecture. 5. ECR Repository: If you are using an image stored in ECR, make sure that the image pushed to ECR is the correct version and architecture. By ensuring that the Docker image and the ECS environment are compatible in terms of architecture, you should be able to resolve this error. If issues persist, consider checking the AWS ECS logs for additional context or errors.

@Marvin if i want to select x86_64, how do it set it?

To ensure that your Docker image is built for the

x86_64

architecture, you can specify the platform during the build process. Here's how you can do it: ### Building an Image for

x86_64

Architecture: 1. Docker Build Command: Use the

--platform

flag when building your Docker image:

bash
   docker build --platform linux/amd64 -t <image-name>:<tag> .

2. Dockerfile: Ensure your Dockerfile is compatible with the

x86_64

architecture. This typically involves using a base image that supports

x86_64

. 3. Docker Desktop (if applicable): If you're using Docker Desktop, make sure it is configured to use the correct architecture. This is especially relevant if you're on an Apple M1/M2 chip, which defaults to ARM architecture. ### Pushing to ECR: After building the image, push it to your ECR repository:

bash
docker tag <image-name>:<tag> <account-id>.dkr.ecr.<region>.<http://amazonaws.com/<repository-name>:<tag|amazonaws.com/<repository-name>:<tag>>
docker push <account-id>.dkr.ecr.<region>.<http://amazonaws.com/<repository-name>:<tag|amazonaws.com/<repository-name>:<tag>>

By specifying

linux/amd64

, you ensure that the image is built for the

x86_64

architecture, which should be compatible with most ECS instances.

@Marvin in my deploy, how do I set the platform: my_flow.deploy( name="test-ecs-deployment", work_pool_name=WORK_POOL_NAME, parameters={}, description="Prefect 3 deployment for test ECS flow", version="1.0.0", image=DockerImage( name=f"{ECR_REPO}", # Must be this and not prefect otherwise we can't push! dockerfile="Dockerfile.flow", ), job_variables={ # ✅ Overrides ECS settings per deployment # "image": ECR_REPO, # Image to use "cpu": 512, # CPU Allocation "memory": 1024, # Memory Allocation "cluster": CLUSTER_NAME, # ECS Cluster Name "task_role_arn": TASK_ROLE_ARN, # IAM Task Role "execution_role_arn": EXECUTION_ROLE_ARN, # IAM Execution Role "networkConfiguration": { # ✅ Ensures correct networking "awsvpcConfiguration": { "subnets": SUBNETS, "securityGroups": SECURITY_GROUPS, "assignPublicIp": "ENABLED", } }, "env": { # Environment Variables (Secrets from AWS SSM & Secrets Manager) "PREFECT_API_URL": PREFECT_API_URL, "PREFECT_API_KEY": PREFECT_API_KEY, }, }, push=True, build=True, )

In Prefect 3, when using the

DockerImage

class, you don't directly specify the platform (e.g.,

x86_64

) within the

deploy

method itself. Instead, you ensure that the Docker image is built for the correct platform before deploying. Here's how you can handle it: 1. Build the Docker Image with Platform Specification: Use the

docker build

command with the

--platform

flag to ensure the image is built for `x86_64`:

bash
   docker build --platform linux/amd64 -t <image-name>:<tag> -f Dockerfile.flow .

2. Push the Image to ECR: After building, push the image to your ECR repository:

bash
   docker tag <image-name>:<tag> <account-id>.dkr.ecr.<region>.<http://amazonaws.com/<repository-name>:<tag|amazonaws.com/<repository-name>:<tag>>
   docker push <account-id>.dkr.ecr.<region>.<http://amazonaws.com/<repository-name>:<tag|amazonaws.com/<repository-name>:<tag>>

3. Deploy with Prefect: In your Prefect deployment script, reference this pre-built image:

python
   my_flow.deploy(
       name="test-ecs-deployment",
       work_pool_name=WORK_POOL_NAME,
       parameters={},
       description="Prefect 3 deployment for test ECS flow",
       version="1.0.0",
       image=DockerImage(
           name=f"{ECR_REPO}:<tag>",  # Include the tag of the built image
           dockerfile="Dockerfile.flow",  # Only if you need to rebuild, otherwise omit
       ),
       job_variables={  # ECS settings
           "cpu": 512,
           "memory": 1024,
           "cluster": CLUSTER_NAME,
           "task_role_arn": TASK_ROLE_ARN,
           "execution_role_arn": EXECUTION_ROLE_ARN,
           "networkConfiguration": {
               "awsvpcConfiguration": {
                   "subnets": SUBNETS,
                   "securityGroups": SECURITY_GROUPS,
                   "assignPublicIp": "ENABLED",
               }
           },
           "env": {
               "PREFECT_API_URL": PREFECT_API_URL,
               "PREFECT_API_KEY": PREFECT_API_KEY,
           },
       },
       push=False,  # Set to False if the image is already pushed
       build=False,  # Set to False if the image is already built
   )

By ensuring the image is built with the correct platform before deploying, you avoid platform-related issues during execution.