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<@ULVA73B9P> When working with ECS to host a Prefe...
# ask-marvin
d
@Marvin When working with ECS to host a Prefect worker for work pools. What could be some possible reasons why I am getting an exit code 1 for the container?
m
✅ thought for 71.0 seconds
Based on the research and common patterns with ECS containers, here are the most likely reasons why you might be getting an exit code 1 for your Prefect worker container in ECS: 1. Environment Variables Misconfiguration - Missing or incorrect PREFECT_API_KEY - Missing or incorrect PREFECT_API_URL - Incorrect work pool name or queue configuration 2. Resource Constraints - Container running out of memory - CPU limits too restrictive - Disk space issues 3. Network Configuration Issues - Unable to reach Prefect Cloud/Server - VPC/subnet configuration problems - Security group rules blocking necessary connections 4. Container Configuration Problems - Incorrect Docker image specification - Wrong command or entrypoint configuration - Missing required dependencies in the container 5. Permission Issues - IAM roles not properly configured - Missing permissions for accessing AWS services - Insufficient ECS task execution role permissions To diagnose the specific cause, here are some steps you can take: 1. Check CloudWatch Logs - Look at the container logs in CloudWatch for specific error messages - Pay attention to startup logs which often contain initialization errors 2. Verify Task Definition - Ensure all required environment variables are properly set - Verify the container command and entrypoint are correct - Check resource allocations (memory/CPU) are sufficient 3. Test Connectivity - Verify the container can reach Prefect Cloud/Server - Check if all required AWS services are accessible - Validate network configuration (VPC, subnets, security groups) 4. Validate Permissions - Review IAM roles attached to the ECS task - Check ECS task execution role permissions - Verify any AWS service permissions required by your flows For more detailed guidance, you can refer to the Prefect ECS Guide. Would you like me to dive deeper into any of these specific areas? Also, it would be helpful if you could share: 1. Any specific error messages you're seeing in the logs 2. The configuration of your task definition 3. Whether this is a new deployment or if it was working previously
d
@Marvin Here is the error message on the Prefect logs... Container 'prefect' exited with non-zero exit code 1.
m
✅ thought for 175.1 seconds
Based on the research and the specific error message you're seeing, here are the most likely causes and solutions for your ECS container exiting with code 1: 1. Worker Configuration Issues - Ensure your worker command is correctly formatted in the ECS task definition - The basic command should look like:
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bash
     prefect worker start -p "your-work-pool-name"
- Make sure all required environment variables are set: - 
PREFECT_API_KEY
(if using Prefect Cloud) - 
PREFECT_API_URL
(if using Prefect Cloud or custom server) 2. Task Definition Requirements - Verify the task definition includes: - Correct Docker image - Appropriate memory and CPU allocations - Proper networking mode - Required environment variables 3. IAM Permissions - Ensure the ECS task role has the necessary permissions: - 
AmazonECSTaskExecutionRolePolicy
for basic ECS operations - Permissions to pull Docker images - CloudWatch logging permissions - Any additional permissions needed by your flows 4. Container Health Checks - If you have health checks configured, ensure they're properly set up - The worker needs time to initialize before health checks begin To troubleshoot this: 1. Check CloudWatch Logs - Look for logs before the exit code 1 - Search for error messages or stack traces - Check if the worker is starting up correctly 2. Verify Task Definition
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json
   {
     "containerDefinitions": [
       {
         "name": "prefect-worker",
         "image": "your-image:tag",
         "essential": true,
         "environment": [
           {
             "name": "PREFECT_API_KEY",
             "value": "your-api-key"
           },
           {
             "name": "PREFECT_API_URL",
             "value": "your-api-url"
           }
         ],
         "logConfiguration": {
           "logDriver": "awslogs",
           "options": {
             "awslogs-group": "your-log-group",
             "awslogs-region": "your-region",
             "awslogs-stream-prefix": "prefect"
           }
         }
       }
     ]
   }
3. Test Locally First - Try running the worker locally with the same configuration - This can help isolate if it's an ECS-specific issue Would you be able to share: 1. The complete CloudWatch logs leading up to the exit code 1? 2. Your ECS task definition (with sensitive information redacted)? 3. Whether this worker was previously working or if this is a new deployment? This information would help pinpoint the exact cause of the exit code 1 error.
d
@Marvin Now I'm getting a different error message... "RuntimeError: Timed out after 301.83046412467957s while watching task for status RUNNING." Also note that these are new deployments
m
✅ thought for 54.1 seconds
Based on the research and GitHub issues, this timeout error is a known issue that occurs when an ECS task fails to reach the RUNNING state within the expected timeframe. Since these are new deployments, let me provide you with a structured approach to resolve this: 1. Common Causes - Insufficient resources in your ECS cluster - Network configuration issues - IAM permission problems - Docker image pull delays - VPC/subnet configuration issues 2. Immediate Solutions a. Increase Task Start Timeout
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python
   from prefect_aws import ECSWorker
   
   worker = ECSWorker(
       task_start_timeout_seconds=600,  # Increase from default
       # other configurations...
   )
b. Verify ECS Task Definition Resources - Ensure sufficient CPU and memory allocation - Example minimal configuration:
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json
   {
     "cpu": "256",
     "memory": "512",
     "essential": true,
     "image": "your-image:tag"
   }
3. Checklist for Troubleshooting a. Network Configuration - Ensure your VPC has internet access (NAT Gateway or Internet Gateway) - Verify security groups allow necessary outbound traffic - Check subnet configurations have proper routing b. IAM Permissions - Verify ECS task role has:
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json
     {
       "Version": "2012-10-17",
       "Statement": [
         {
           "Effect": "Allow",
           "Action": [
             "ecs:RunTask",
             "ecs:StopTask",
             "ecs:DescribeTasks",
             "iam:PassRole"
           ],
           "Resource": "*"
         }
       ]
     }
- Ensure task execution role has permissions for: - ECR image pulling - CloudWatch logging - SSM parameter access (if used) c. Docker Image - Verify the image exists and is accessible - Check if the image size is large (might cause pull timeouts) - Consider using ECR in the same region as your ECS cluster 4. Diagnostic Steps - Check CloudWatch logs for the ECS task - Look in ECS Events tab for task placement failures - Verify ECS service events for any capacity or resource issues 5. Best Practices for New Deployments - Start with a simple task definition and gradually add complexity - Test with a minimal flow first - Use AWS Fargate (managed infrastructure) to reduce complexity - Implement proper logging to CloudWatch - Consider using AWS App Mesh for better networking visibility Would you be able to provide: 1. Your current ECS task definition? 2. The CloudWatch logs from when the timeout occurs? 3. Are you using AWS Fargate or EC2 for your ECS tasks? This information would help identify if this is a resource, networking, or configuration issue. Also, since these are new deployments, we should verify that all the basic infrastructure components (VPC, subnets, security groups, IAM roles) are properly configured.
d
@Marvin I'm using ECS Fargate. Can you explain what is the point of having ECR linked?
m
✅ thought for 72.3 seconds
Here's why ECR (Elastic Container Registry) is valuable when working with Prefect and ECS Fargate: 1. Private and Secure Image Storage - ECR provides a secure, private repository for your Docker images - Images containing your Prefect flows, dependencies, and custom code stay within your AWS environment - Access is controlled through IAM roles and policies 2. Performance Benefits - Faster image pulls since ECR is in the same AWS region as your ECS tasks - Reduced latency compared to pulling from Docker Hub or other external registries - Better reliability as you're not dependent on external services 3. Cost Efficiency - No data transfer costs when pulling images within the same AWS region - No need to pay for external registry services - Better bandwidth utilization 4. Integration Advantages
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python
   # Example of specifying an ECR image in your Prefect configuration
   from prefect_aws import ECSWorker
   
   worker = ECSWorker(
       image="<http://123456789012.dkr.ecr.us-east-1.amazonaws.com/my-prefect-image:latest|123456789012.dkr.ecr.us-east-1.amazonaws.com/my-prefect-image:latest>",
       # other configurations...
   )
5. Operational Benefits - Version control for your container images - Built-in vulnerability scanning - Automatic image cleanup policies - Integration with AWS CloudWatch for monitoring 6. Common Use Cases - Storing custom images with specific dependencies - Managing different versions of your flow environments - Ensuring consistent environments across development and production - Maintaining private packages and proprietary code 7. Without ECR, You Might Face: - Rate limiting from public registries like Docker Hub - Slower image pull times - Less control over image security - Higher costs for external registry services - Potential reliability issues 8. Best Practices - Tag images with meaningful versions instead of using 'latest' - Implement lifecycle policies to manage old images - Use multi-stage builds to keep images small - Cache layers effectively to speed up builds Example ECR workflow:
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bash
# Build your custom Prefect image
docker build -t my-prefect-image .

# Tag for ECR
docker tag my-prefect-image:latest <http://123456789012.dkr.ecr.us-east-1.amazonaws.com/my-prefect-image:latest|123456789012.dkr.ecr.us-east-1.amazonaws.com/my-prefect-image:latest>

# Push to ECR
docker push <http://123456789012.dkr.ecr.us-east-1.amazonaws.com/my-prefect-image:latest|123456789012.dkr.ecr.us-east-1.amazonaws.com/my-prefect-image:latest>
Required IAM permissions for ECS to pull from ECR:
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json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ecr:GetAuthorizationToken",
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage"
            ],
            "Resource": "*"
        }
    ]
}
If you're experiencing the timeout issues we discussed earlier, using ECR could actually help by: 1. Reducing image pull times 2. Providing more reliable access to your container images 3. Giving you better visibility into image pull issues through CloudWatch logs Would you like me to explain more about how to set up ECR for your specific use case, or would you prefer more information about how it integrates with your current Prefect workflow?
d
@Marvin Now I'm getting another error "prefect.deployments.steps.core.StepExecutionError: Encountered error while running prefect.deployments.steps.set_working_directory"
m
✅ thought for 44.9 seconds
Based on the research and GitHub issues, this error occurs when Prefect is unable to set the working directory during deployment. Here are the common causes and solutions: 1. Common Causes - The specified working directory doesn't exist - Permissions issues with the directory - Path format issues - Container filesystem limitations - Previous deployment artifacts causing conflicts 2. Solutions a. Check Your Deployment Configuration
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yaml
   pull:
     - prefect.deployments.steps.set_working_directory:
         directory: "/opt/prefect"  # Make sure this directory exists
b. Ensure Directory Exists and Has Proper Permissions
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bash
   # In your container or environment
   mkdir -p /opt/prefect
   chmod 755 /opt/prefect
c. Clean Up Previous Deployments
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bash
   # Remove any existing deployment artifacts
   rm -rf /path/to/working/directory/*
3. Best Practices a. Use Absolute Paths
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python
   from prefect.deployments import Deployment
   from prefect.filesystems import LocalFileSystem
   
   deployment = Deployment(
       name="my-deployment",
       working_dir="/opt/prefect"  # Use absolute paths
   )
b. Verify Container Environment - Ensure the working directory is mounted correctly if using volumes - Check that the container user has appropriate permissions - Verify the directory exists in the container filesystem 4. Debugging Steps a. Check Directory Structure
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bash
   # Inside your container or environment
   ls -la /path/to/working/directory
   pwd
   whoami
b. Verify Permissions
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bash
   # Check ownership and permissions
   stat /path/to/working/directory
c. Review Logs
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bash
   # Check container logs
   docker logs <container-id>
   
   # Check ECS logs in CloudWatch
   aws logs get-log-events --log-group-name <your-log-group>
5. For ECS Specific Setup
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python
   from prefect_aws import ECSWorker
   
   worker = ECSWorker(
       working_dir="/opt/prefect",  # Make sure this exists in your container
       task_definition_arn="your-task-definition",
       cluster="your-cluster"
   )
To help diagnose your specific issue, could you share: 1. The content of your deployment configuration (prefect.yaml or deployment code)? 2. The directory structure you're trying to use? 3. Are you using any specific volume mounts in your ECS task definition? This information would help identify whether this is a configuration issue, a permissions problem, or a container filesystem limitation. Also, since you mentioned you're using ECS Fargate, make sure that: 1. The working directory is properly defined in your task definition 2. Any necessary volumes are mounted correctly 3. The container has the necessary permissions to access and modify the working directory Would you like me to provide more specific guidance for any of these areas?
d
@Marvin Can you update this example .yaml file that is given on the ECS worker documentation page to the latest prefect version (I ran 
prefect init
and got 3.4.10). Also, where would I specify which base image to use when the deployment creates images? Right now it uses 
prefecthq/prefect:3.4.10-python3.13
but I want to use 
prefecthq/prefect:3-latest
m
✅ thought for 154.8 seconds
Here's the updated version of the ECS worker YAML file for Prefect 3.4.10, including how to specify a custom base image:
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yaml
prefect:
  version: 3.4.10
  name: ecs-example

build:
  # Specify the base image to use
  base_image: prefecthq/prefect:3-latest
  python_version: "3.9"
  requirements:
    - boto3
    - s3fs

push:
  # If using ECR, specify your repository here
  registry: <http://your-aws-account.dkr.ecr.region.amazonaws.com|your-aws-account.dkr.ecr.region.amazonaws.com>
  image_name: your-flow-image
  tag: latest
  # Alternatively, if not using ECR:
  # registry: <http://docker.io|docker.io>
  # image_name: your-dockerhub-username/your-flow-image

deployments:
  - name: ecs-example-flow
    version: 1
    tags: ["ecs"]
    description: "Example ECS deployment"
    entrypoint: flows/example_flow.py:example_flow  # Update this to your flow's path
    schedule: null
    work_pool:
      name: ecs-pool
      work_queue_name: default
      job_variables:
        task_definition_arn: your-task-definition-arn
        cluster: your-ecs-cluster
        launch_type: FARGATE
        network_configuration:
          awsvpcConfiguration:
            subnets:
              - subnet-xxxxx
            securityGroups:
              - sg-xxxxx
            assignPublicIp: ENABLED
        container_name: prefect
        execution_role_arn: your-execution-role-arn
        task_role_arn: your-task-role-arn
        env:
          EXTRA_PIP_PACKAGES: "your-extra-packages"
          # Add any other environment variables needed
        cpu: "256"
        memory: "512"
Key changes and explanations: 1. Base Image Specification
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yaml
   build:
     base_image: prefecthq/prefect:3-latest
This tells Prefect to use the 
prefecthq/prefect:3-latest
image instead of the default version-specific image. 2. Version Update
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yaml
   prefect:
     version: 3.4.10
Updated to match your Prefect version. 3. Build Configuration - The 
build
section specifies how your deployment image should be built - You can specify Python version and requirements - The 
base_image
field determines which Prefect image to use as a starting point 4. Push Configuration - If using ECR, specify your repository details - If using DockerHub or another registry, adjust accordingly 5. Work Pool Configuration - Updated to use the newer work pool structure instead of the older infrastructure blocks - Includes essential ECS configuration parameters To use this configuration: 1. Save and Deploy
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bash
   prefect deploy
2. Start a Worker
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bash
   prefect worker start -p 'ecs-pool'
Additional Tips: 1. Custom Dockerfile If you need more customization, you can specify a custom Dockerfile:
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yaml
   build:
     dockerfile: path/to/your/Dockerfile
2. Environment Variables Add any necessary environment variables under the 
env
section:
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yaml
   job_variables:
     env:
       AWS_DEFAULT_REGION: us-east-1
       OTHER_VAR: value
3. Resource Allocation Adjust CPU and memory as needed:
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yaml
   job_variables:
     cpu: "256"    # 0.25 vCPU
     memory: "512" # 512 MB
Would you like me to explain any specific part of this configuration in more detail? Or would you like guidance on setting up specific aspects like ECR integration or custom Dockerfile creation?
d
@Marvin there is no 
base_image
attribute in the 
prefect-docker
integration. What if I want to pull from a private repository instead?
m
✅ thought for 47.7 seconds
You're right about wanting to use a private repository. Here's the correct way to configure your 
prefect.yaml
to use a private Docker image:
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yaml
prefect:
  version: 3.4.10
  name: your-project-name

build:
  # If you need to build custom images
  requirements:
    - your-requirements

push:
  # Configuration for pushing to your private registry
  registry: <http://your-private-registry.com|your-private-registry.com>
  image_name: your-image-name
  tag: latest
  credentials:
    # If using AWS ECR
    aws:
      profile_name: default  # or your AWS profile name
    # If using another private registry
    username: ${DOCKER_USERNAME}
    password: ${DOCKER_PASSWORD}

deployments:
  - name: your-deployment
    version: 1
    tags: ["ecs"]
    description: "Your deployment description"
    entrypoint: "flows/your_flow.py:flow_function"
    work_pool:
      name: your-ecs-pool
      work_queue_name: default
      job_variables:
        image: <http://your-private-registry.com/your-image-name:latest|your-private-registry.com/your-image-name:latest>  # Specify the full image path here
        task_definition_arn: your-task-definition-arn
        cluster: your-ecs-cluster
        launch_type: FARGATE
        network_configuration:
          awsvpcConfiguration:
            subnets:
              - subnet-xxxxx
            securityGroups:
              - sg-xxxxx
            assignPublicIp: ENABLED
        container_name: prefect
        execution_role_arn: your-execution-role-arn
        task_role_arn: your-task-role-arn
For different private registry types: 1. For AWS ECR:
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yaml
job_variables:
  image: <http://123456789012.dkr.ecr.region.amazonaws.com/your-image:tag|123456789012.dkr.ecr.region.amazonaws.com/your-image:tag>
  # Make sure your ECS task execution role has ECR permissions
2. For Private DockerHub:
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yaml
job_variables:
  image: <http://docker.io/your-org/your-image:tag|docker.io/your-org/your-image:tag>
  # Include registry credentials in your ECS task definition
3. For Other Private Registries:
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yaml
job_variables:
  image: <http://your-registry.com/your-org/your-image:tag|your-registry.com/your-org/your-image:tag>
  # Include appropriate authentication in your ECS task definition
Important considerations: 1. Authentication: - For ECR: Your ECS task execution role needs ECR permissions - For other registries: Include registry credentials in your ECS task definition 2. ECS Task Definition: If using a private registry other than ECR, you'll need to include repository credentials in your task definition:
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json
   {
     "containerDefinitions": [{
       "repositoryCredentials": {
         "credentialsParameter": "arn:aws:secretsmanager:region:aws_account_id:secret:secret_name"
       }
     }]
   }
3. Environment Variables: You can use environment variables for sensitive information:
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yaml
   job_variables:
     env:
       DOCKER_USERNAME: ${DOCKER_USERNAME}
       DOCKER_PASSWORD: ${DOCKER_PASSWORD}
Would you like me to: 1. Provide specific examples for a particular private registry type? 2. Explain how to set up the authentication for your specific use case? 3. Show how to configure the ECS task definition for private registry access?
d
@Marvin Now my ECS task gets to the RUNNING state, however I get the following error message... 
Running set_working_directory step...
[Errno 2] No such file or directory: DIRECTORY
where DIRECTORY is the local directory of my script
m
✅ thought for 172.1 seconds
This error occurs because Prefect is trying to use a local directory path inside the container, but that directory doesn't exist in the container's filesystem. Here's how to fix this: 1. Option 1: Use Absolute Container Path Update your 
prefect.yaml
to use an absolute path that exists in the container:
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yaml
   deployments:
     - name: your-deployment
       entrypoint: "flows/your_flow.py:flow_function"
       work_pool:
         name: your-ecs-pool
         job_variables:
           # ... other configurations ...
           working_dir: "/opt/prefect"  # Use a directory that exists in the container
2. Option 2: Add Pull Steps If you need to pull code from a repository, add pull steps to your deployment:
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yaml
   pull:
     - prefect.deployments.steps.set_working_directory:
         directory: "/opt/prefect"  # Use an absolute path in the container
     - prefect.deployments.steps.git_clone:
         repository: "<https://github.com/your/repo.git>"
         branch: "main"
3. Option 3: Modify Your Dockerfile Ensure your working directory exists in the container by modifying your Dockerfile:
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dockerfile
   FROM prefecthq/prefect:3-latest
   
   # Create the working directory
   RUN mkdir -p /opt/prefect
   WORKDIR /opt/prefect
   
   # Copy your flow code
   COPY flows/ /opt/prefect/flows/
4. Option 4: Mount a Volume If you need to persist data, you can mount an EFS volume:
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yaml
   deployments:
     - name: your-deployment
       work_pool:
         name: your-ecs-pool
         job_variables:
           volumes:
             - name: efs-volume
               efsVolumeConfiguration:
                 fileSystemId: fs-xxxxxx
                 rootDirectory: /
           mount_points:
             - sourceVolume: efs-volume
               containerPath: /opt/prefect
               readOnly: false
Here's a complete example configuration:
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yaml
prefect:
  version: 3.4.10
  name: your-project-name

pull:
  - prefect.deployments.steps.set_working_directory:
      directory: "/opt/prefect"  # Absolute path in container

deployments:
  - name: your-deployment
    version: 1
    tags: ["ecs"]
    description: "Your deployment description"
    entrypoint: "flows/your_flow.py:flow_function"
    work_pool:
      name: your-ecs-pool
      work_queue_name: default
      job_variables:
        image: your-image:tag
        task_definition_arn: your-task-definition-arn
        cluster: your-ecs-cluster
        launch_type: FARGATE
        working_dir: "/opt/prefect"  # Match the directory in pull steps
        network_configuration:
          awsvpcConfiguration:
            subnets:
              - subnet-xxxxx
            securityGroups:
              - sg-xxxxx
            assignPublicIp: ENABLED
        container_name: prefect
        execution_role_arn: your-execution-role-arn
        task_role_arn: your-task-role-arn
To troubleshoot: 1. Check Container Directory Structure
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bash
   # Add this to your Dockerfile or run it in the container
   RUN ls -la /opt/prefect
2. Verify Permissions
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bash
   # Ensure the prefect user has access
   RUN chown -R prefect:prefect /opt/prefect
3. Debug in Container Add these environment variables to your task definition:
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yaml
   job_variables:
     env:
       PREFECT_LOGGING_LEVEL: "DEBUG"
Would you like me to: 1. Provide a specific example for your directory structure? 2. Help modify your Dockerfile to ensure the correct directory exists? 3. Show how to set up volume mounts if you need persistent storage?
d
@Marvin I'm using the following setup in my .py script to deploy, instead of a .yaml file...
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if __name__=="__main__":
    main.from_source(
        source=str((p := Path(__file__)).parent.resolve()),
        entrypoint=f"{p.name}:main"
    ).deploy(name="foo", work_pool_name="foo")
Instead of my source being local, I want to do so through gitlab, as I have a gitlab repository storing my code (including this current one)
m
✅ thought for 94.0 seconds
Based on the research, here's how to modify your code to deploy from a GitLab repository. You'll need to first install the GitLab integration:
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bash
pip install prefect-gitlab
Then, here's how to modify your deployment code:
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python
from pathlib import Path
from prefect import flow
from prefect_gitlab import GitLabCredentials, GitLabRepository

if __name__ == "__main__":
    # Option 1: Using Personal Access Token (PAT)
    gitlab_credentials = GitLabCredentials(token="your-gitlab-token")
    # Or load from a saved block
    # gitlab_credentials = GitLabCredentials.load("gitlab-credentials")

    # Create GitLab repository reference
    gitlab_repo = GitLabRepository(
        repository="<https://gitlab.com/your-username/your-repo.git>",  # Your GitLab repo URL
        reference="main",  # or your branch name
        credentials=gitlab_credentials
    )

    # Deploy using GitLab source
    main.from_source(
        source=gitlab_repo,
        entrypoint="path/to/your/script.py:main"  # Relative path in your repo
    ).deploy(
        name="foo",
        work_pool_name="foo"
    )
Alternative approaches: 1. Using HTTPS with credentials in URL:
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python
main.from_source(
    source="<https://oauth2:YOUR-TOKEN@gitlab.com/your-username/your-repo.git>",
    entrypoint="path/to/your/script.py:main"
).deploy(name="foo", work_pool_name="foo")
2. Using GitLab Credentials Block (recommended for security):
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python
from prefect.blocks.system import GitLabCredentials

# First, create and save credentials (do this once)
gitlab_credentials = GitLabCredentials(
    token="your-gitlab-token"
)
gitlab_credentials.save("gitlab-credentials")

# Then in your deployment code
credentials = GitLabCredentials.load("gitlab-credentials")
gitlab_repo = GitLabRepository(
    repository="<https://gitlab.com/your-username/your-repo.git>",
    credentials=credentials
)

main.from_source(
    source=gitlab_repo,
    entrypoint="path/to/your/script.py:main"
).deploy(name="foo", work_pool_name="foo")
3. Additional Configuration Options:
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python
gitlab_repo = GitLabRepository(
    repository="<https://gitlab.com/your-username/your-repo.git>",
    reference="main",  # or specific branch/tag
    credentials=credentials,
    git_depth=1,  # Shallow clone for faster operations
)
Important considerations: 1. Authentication: - Create a GitLab Personal Access Token with appropriate permissions (at least 
read_repository
) - Store credentials securely using Prefect blocks 2. Path Configuration: - Use paths relative to the root of your GitLab repository - The entrypoint should point to the correct path within your repo 3. Security Best Practices: - Never commit tokens or credentials in your code - Use environment variables or Prefect blocks for sensitive information
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python
   import os
   
   gitlab_credentials = GitLabCredentials(
       token=os.environ["GITLAB_TOKEN"]
   )
Would you like me to: 1. Provide more details about setting up GitLab credentials? 2. Show how to handle different repository structures? 3. Explain how to troubleshoot common GitLab integration issues?
d
@Marvin Instead of gitlab, how would I do so through AWS ECR?
m
✅ thought for 21.7 seconds
Let me clarify something - ECR (Elastic Container Registry) is used for storing Docker images, not source code. If you want to store your source code, you'd want to use AWS CodeCommit, S3, or another code repository service. However, I'll show you both options: 1. For Source Code Storage, you have several AWS options:
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python
# Option 1: Using AWS CodeCommit
from prefect_aws import CodeCommitCredentials, CodeCommitRepository

if __name__ == "__main__":
    # Create or load AWS credentials
    aws_creds = CodeCommitCredentials(
        aws_access_key_id="your-access-key",
        aws_secret_access_key="your-secret-key"
    )
    # Or load existing credentials
    # aws_creds = CodeCommitCredentials.load("aws-credentials")

    # Deploy from CodeCommit
    main.from_source(
        source="<https://git-codecommit.region.amazonaws.com/v1/repos/your-repo>",
        entrypoint="path/to/your/script.py:main"
    ).deploy(
        name="foo",
        work_pool_name="foo"
    )

# Option 2: Using S3
from prefect_aws import S3Bucket

if __name__ == "__main__":
    s3_block = S3Bucket(
        bucket_name="your-bucket",
        aws_access_key_id="your-access-key",
        aws_secret_access_key="your-secret-key"
    )
    
    main.from_source(
        source=s3_block,
        entrypoint="path/to/your/script.py:main"
    ).deploy(
        name="foo",
        work_pool_name="foo"
    )
2. For Docker Image Storage in ECR:
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python
if __name__ == "__main__":
    main.from_source(
        source=str((p := Path(__file__)).parent.resolve()),
        entrypoint=f"{p.name}:main"
    ).deploy(
        name="foo",
        work_pool_name="foo",
        image="<http://123456789012.dkr.ecr.region.amazonaws.com/your-image:tag|123456789012.dkr.ecr.region.amazonaws.com/your-image:tag>"
    )
3. Combined Approach (using both ECR for images and CodeCommit for source):
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python
from prefect_aws import CodeCommitCredentials, CodeCommitRepository

if __name__ == "__main__":
    # Set up source code repository
    repo = CodeCommitRepository(
        repository="<https://git-codecommit.region.amazonaws.com/v1/repos/your-repo>",
        reference="main",  # or your branch name
        credentials=CodeCommitCredentials.load("aws-credentials")
    )

    # Deploy with both source code and ECR image
    main.from_source(
        source=repo,
        entrypoint="path/to/your/script.py:main"
    ).deploy(
        name="foo",
        work_pool_name="foo",
        image="<http://123456789012.dkr.ecr.region.amazonaws.com/your-image:tag|123456789012.dkr.ecr.region.amazonaws.com/your-image:tag>",
        job_variables={
            "aws_access_key_id": "your-access-key",
            "aws_secret_access_key": "your-secret-key",
            "aws_region": "your-region"
        }
    )
4. Using AWS IAM Roles (recommended for ECS):
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python
if __name__ == "__main__":
    main.from_source(
        source=str((p := Path(__file__)).parent.resolve()),
        entrypoint=f"{p.name}:main"
    ).deploy(
        name="foo",
        work_pool_name="foo",
        image="<http://123456789012.dkr.ecr.region.amazonaws.com/your-image:tag|123456789012.dkr.ecr.region.amazonaws.com/your-image:tag>",
        job_variables={
            "task_role_arn": "arn:aws:iam::123456789012:role/your-task-role",
            "execution_role_arn": "arn:aws:iam::123456789012:role/your-execution-role"
        }
    )
Important considerations: 1. ECR Authentication: - Ensure your ECS task execution role has permissions to pull from ECR - Required IAM permissions:
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json
     {
         "Version": "2012-10-17",
         "Statement": [
             {
                 "Effect": "Allow",
                 "Action": [
                     "ecr:GetAuthorizationToken",
                     "ecr:BatchCheckLayerAvailability",
                     "ecr:GetDownloadUrlForLayer",
                     "ecr:BatchGetImage"
                 ],
                 "Resource": "*"
             }
         ]
     }
2. Image Naming: - ECR image URLs follow the format: 
<http://registry-id.dkr.ecr.region.amazonaws.com/repository-name:tag|registry-id.dkr.ecr.region.amazonaws.com/repository-name:tag>
- Always specify a tag (avoid using 'latest' in production) 3. Security Best Practices: - Use IAM roles instead of access keys when possible - Store sensitive credentials using Prefect blocks - Use environment variables for sensitive information Would you like me to: 1. Provide more details about setting up AWS credentials and IAM roles? 2. Show how to handle ECR image updates and versioning? 3. Explain how to troubleshoot common ECR/ECS integration issues?
d
@Marvin Show me what a sample .yaml file would look like that makes use of AWS ECR to push, store and pull from the flow code during deployment
m
✅ thought for 96.5 seconds
Here's a detailed 
prefect.yaml
configuration for using AWS ECR:
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yaml
# Basic project information
prefect:
  version: 3.4.10
  name: your-project-name

# Build configuration for creating the Docker image
build:
  # Python version for the environment
  python_version: "3.9"
  
  # Additional system dependencies if needed
  apt_packages:
    - git
    - curl

  # Python package dependencies
  requirements:
    - boto3
    - pandas
    - prefect-aws

  # Optional: Specify a custom Dockerfile
  dockerfile: Dockerfile
  
# Push configuration for AWS ECR
push:
  # Your ECR repository details
  registry: 
    # Format: <http://aws_account_id.dkr.ecr.region.amazonaws.com|aws_account_id.dkr.ecr.region.amazonaws.com>
    url: "${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.<http://amazonaws.com|amazonaws.com>"
  
  # Image details
  image_name: "your-repository-name"
  tag: "${PREFECT_FLOW_VERSION:-latest}"  # Use flow version or default to 'latest'

  # AWS authentication (if not using IAM roles)
  credentials:
    aws:
      # These can be environment variables or AWS profile
      profile_name: "default"
      region_name: "${AWS_REGION}"

# Pull configuration for getting code during deployment
pull:
  - prefect.deployments.steps.set_working_directory:
      directory: "/opt/prefect"  # Working directory in container
  
  # Optional: If you need to pull additional code/resources from S3
  - prefect_aws.deployments.steps.pull_from_s3:
      bucket: "your-bucket-name"
      folder: "flows"
      aws_credentials: "aws-block-name"  # Reference to saved AWS credentials block

# Deployment configurations
deployments:
  default:
    name: ecr-flow
    version: "${PREFECT_FLOW_VERSION:-0.1.0}"
    tags: ["aws", "ecr"]
    description: "Flow deployment using ECR"
    
    # Path to your flow file relative to working directory
    entrypoint: "flows/your_flow.py:flow_function"
    
    # Work pool configuration
    work_pool:
      name: your-ecs-pool
      work_queue_name: default
      
      # Job-specific variables for ECS
      job_variables:
        # Use the same image we pushed
        image: "${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.<http://amazonaws.com/${IMAGE_NAME}:${TAG}|amazonaws.com/${IMAGE_NAME}:${TAG}>"
        
        # ECS specific configuration
        cluster: "your-ecs-cluster"
        launch_type: "FARGATE"
        task_definition_arn: "your-task-definition-arn"
        
        # IAM roles
        execution_role_arn: "arn:aws:iam::${AWS_ACCOUNT_ID}:role/your-execution-role"
        task_role_arn: "arn:aws:iam::${AWS_ACCOUNT_ID}:role/your-task-role"
        
        # Network configuration
        network_configuration:
          awsvpcConfiguration:
            subnets:
              - "subnet-xxxxx"
            securityGroups:
              - "sg-xxxxx"
            assignPublicIp: "ENABLED"
        
        # Container configuration
        cpu: "256"
        memory: "512"
        
        # Environment variables
        env:
          AWS_DEFAULT_REGION: "${AWS_REGION}"
          PREFECT_LOGGING_LEVEL: "INFO"
          
        # Optional: Container overrides
        container_name: "prefect"
        
        # Optional: Volume mounts if needed
        volumes:
          - name: "flow-storage"
            efsVolumeConfiguration:
              fileSystemId: "fs-xxxxx"
              rootDirectory: "/"
        mount_points:
          - sourceVolume: "flow-storage"
            containerPath: "/opt/prefect"
            readOnly: false
To use this configuration: 1. Set Required Environment Variables:
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bash
export AWS_ACCOUNT_ID="123456789012"
export AWS_REGION="us-east-1"
export PREFECT_FLOW_VERSION="1.0.0"
export IMAGE_NAME="your-repository-name"
export TAG="latest"
2. Create Required AWS Resources: - ECR repository - ECS cluster - Task definition - IAM roles - VPC, subnets, security groups 3. Deploy Your Flow:
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bash
prefect deploy
4. Start a Worker:
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bash
prefect worker start -p 'your-ecs-pool'
Important considerations: 1. IAM Permissions: Your task execution role needs: ```json
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{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "ecr:GetAuthorizationToken",
        "ecr:BatchCheckLayerAvailability",
        "ecr:GetDownloadUrlForLayer",
        "ecr:BatchGetImage"
      ],
      "Resource": "*"
    }
  ]
}
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2. *Security Best Practices*:
   - Use IAM roles instead of access keys
   - Keep secrets in AWS Secrets Manager
   - Use specific tags instead of 'latest'
   - Implement proper network security in VPC

3. *Optional Additions*:
   ```yaml
   # Add health checks
   job_variables:
     healthcheck:
       command: ["CMD-SHELL", "prefect version || exit 1"]
       interval: 30
       timeout: 5
       retries: 3

   # Add logging configuration
   job_variables:
     logging:
       logDriver: "awslogs"
       options:
         awslogs-group: "/prefect/flows"
         awslogs-region: "${AWS_REGION}"
         awslogs-stream-prefix: "ecr"
Would you like me to: 1. Explain any specific part of the configuration in more detail? 2. Show how to set up the required AWS resources? 3. Provide examples of different deployment patterns?
d
@Marvin The documentation provides the following for the build section...
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# build section allows you to manage and build docker images
build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: <my-ecr-repo>
    tag: latest
    dockerfile: auto
How do I make it so that it uses a custom Dockerfile I have instead?
m
✅ thought for 11.0 seconds
To use a custom Dockerfile instead of the auto-generated one, you can specify the path to your Dockerfile in the configuration. Here's how:
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yaml
build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build_image
      requires: prefect-docker>=0.3.1
      image_name: <my-ecr-repo>
      tag: latest
      dockerfile: path/to/your/Dockerfile  # Relative path to your custom Dockerfile
For example, if your project structure looks like this:
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your-project/
├── flows/
│   └── your_flow.py
├── Dockerfile
└── prefect.yaml
Then your configuration would be:
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yaml
build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build_image
      requires: prefect-docker>=0.3.1
      image_name: <my-ecr-repo>
      tag: latest
      dockerfile: ./Dockerfile  # or just "Dockerfile" if in same directory
If your Dockerfile is in a different directory:
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your-project/
├── flows/
│   └── your_flow.py
├── docker/
│   └── Dockerfile
└── prefect.yaml
Then:
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yaml
build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build_image
      requires: prefect-docker>=0.3.1
      image_name: <my-ecr-repo>
      tag: latest
      dockerfile: docker/Dockerfile
The path to the Dockerfile should be relative to the location of your 
prefect.yaml
file. Make sure your custom Dockerfile includes all necessary dependencies and configurations for your Prefect flows to run properly. Would you like me to provide an example of a custom Dockerfile that works well with Prefect deployments?
d
@Marvin Now when I run the deployment with the Dockerfile, I get the following error when it tries the 
push
steps... 
requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url:
m
✅ thought for 69.0 seconds
Here's a step-by-step troubleshooting guide: 1. First, verify AWS ECR authentication:
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bash
# Login to ECR
aws ecr get-login-password --region your-region | docker login --username AWS --password-stdin <http://your-aws-account.dkr.ecr.your-region.amazonaws.com|your-aws-account.dkr.ecr.your-region.amazonaws.com>
2. Check your AWS credentials and permissions. Make sure you have these permissions:
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json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ecr:GetAuthorizationToken",
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:GetRepositoryPolicy",
                "ecr:DescribeRepositories",
                "ecr:CreateRepository",
                "ecr:PutImage",
                "ecr:InitiateLayerUpload",
                "ecr:UploadLayerPart",
                "ecr:CompleteLayerUpload"
            ],
            "Resource": "*"
        }
    ]
}
3. Update your 
prefect.yaml
to include explicit AWS authentication:
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yaml
build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build_image
      requires: prefect-docker>=0.3.1
      image_name: <my-ecr-repo>
      tag: latest
      dockerfile: path/to/your/Dockerfile

push:
  - prefect_docker.deployments.steps.push_docker_image:
      requires: prefect-docker>=0.3.1
      image_name: <my-ecr-repo>
      tag: latest
      credentials:
        aws:
          profile_name: default  # or your AWS profile name
          region_name: your-region
4. Verify your ECR repository exists:
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bash
aws ecr describe-repositories --repository-names your-repo-name --region your-region
If it doesn't exist, create it:
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bash
aws ecr create-repository --repository-name your-repo-name --region your-region
5. Check if your image builds locally:
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bash
docker build -t <http://your-aws-account.dkr.ecr.your-region.amazonaws.com/your-repo:latest|your-aws-account.dkr.ecr.your-region.amazonaws.com/your-repo:latest> .
docker push <http://your-aws-account.dkr.ecr.your-region.amazonaws.com/your-repo:latest|your-aws-account.dkr.ecr.your-region.amazonaws.com/your-repo:latest>
6. Environment Variables: Make sure these environment variables are set:
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bash
export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
export AWS_DEFAULT_REGION=your_region
7. Updated Deployment Configuration:
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python
from prefect import flow
from prefect_aws import AwsCredentials

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

if __name__ == "__main__":
    aws_creds = AwsCredentials(
        aws_access_key_id="your_access_key",
        aws_secret_access_key="your_secret_key",
        region_name="your_region"
    )
    aws_creds.save("aws-creds")

    your_flow.from_source(
        source=".",
        entrypoint="flow.py:your_flow"
    ).deploy(
        name="your-deployment",
        work_pool_name="your-pool",
        image="<http://your-aws-account.dkr.ecr.your-region.amazonaws.com/your-repo:latest|your-aws-account.dkr.ecr.your-region.amazonaws.com/your-repo:latest>"
    )
8. Debugging Steps: Add these to your deployment configuration:
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yaml
job_variables:
  env:
    PREFECT_LOGGING_LEVEL: "DEBUG"
    AWS_DEFAULT_REGION: "your-region"
  logging:
    driver: awslogs
    options:
      awslogs-group: "/prefect/flows"
      awslogs-region: "your-region"
      awslogs-stream-prefix: "ecr"
Could you please provide: 1. The complete error message from the logs? 2. Your current AWS configuration (redacting sensitive information)? 3. The contents of your 
prefect.yaml
file? This would help me provide more specific guidance for your situation.
d
@Marvin Assume I just pushed the Dockerfile I want to use to ECR directly, how would I modify my 
from_source()
implementation so that it uses/pulls that image from ECR instead of having to build and push first?
m
✅ thought for 12.7 seconds
If you've already pushed your Docker image to ECR, you can simply specify that image in your 
from_source()
deployment. Here's how:
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python
if __name__ == "__main__":
    main.from_source(
        source=str((p := Path(__file__)).parent.resolve()),
        entrypoint=f"{p.name}:main"
    ).deploy(
        name="foo",
        work_pool_name="foo",
        image="<http://123456789012.dkr.ecr.region.amazonaws.com/your-repository:your-tag|123456789012.dkr.ecr.region.amazonaws.com/your-repository:your-tag>"  # Your ECR image
    )
Or with more configuration options:
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python
if __name__ == "__main__":
    main.from_source(
        source=str((p := Path(__file__)).parent.resolve()),
        entrypoint=f"{p.name}:main"
    ).deploy(
        name="foo",
        work_pool_name="foo",
        image="<http://123456789012.dkr.ecr.region.amazonaws.com/your-repository:your-tag|123456789012.dkr.ecr.region.amazonaws.com/your-repository:your-tag>",
        job_variables={
            # Optional: ECS-specific configurations
            "task_role_arn": "your-task-role-arn",
            "execution_role_arn": "your-execution-role-arn",
            "cpu": "256",
            "memory": "512",
            # Ensure ECS has permissions to pull from ECR
            "network_configuration": {
                "awsvpcConfiguration": {
                    "subnets": ["subnet-xxxxx"],
                    "securityGroups": ["sg-xxxxx"],
                    "assignPublicIp": "ENABLED"
                }
            }
        }
    )
The key points here are: 1. No build/push steps needed - just specify the full ECR image URL 2. Make sure your ECS task execution role has permissions to pull from ECR 3. The image URL format is: 
{account-id}.dkr.ecr.{region}.<http://amazonaws.com/{repository}:{tag}|amazonaws.com/{repository}:{tag}>
Would you like me to provide more details about: 1. Setting up the necessary IAM permissions? 2. Additional ECS configuration options? 3. How to handle image updates and versioning?
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