Based on the research, to properly help you troubleshoot this issue, I'll need some additional information: 1. Can you share your ECS task definition or the relevant parts of it? 2. Are you able to access the CloudWatch logs for the failed container? These logs would show the actual error message from the Prefect worker. 3. Which work pool type are you using (process, ecs, kubernetes)? 4. Are you using a custom Docker image or the official Prefect image? Here are some initial troubleshooting steps you can take: 1. Check CloudWatch Logs: - Look at the CloudWatch logs for the ECS task to see the detailed error message - The logs should show what's happening before the container exits 2. Verify Resource Allocations: - Ensure your task definition allocates sufficient CPU and memory - For Prefect workers, a minimum of 1GB memory is recommended 3. Check IAM Roles: - Verify that your ECS task role has the necessary permissions - At minimum, you need permissions for: - CloudWatch Logs access - ECR access (if using private repositories) - Any other AWS services your flows might interact with 4. Network Configuration: - Ensure your security groups allow necessary outbound traffic - If using private subnets, verify NAT gateway configuration - Check that the VPC has internet access (required for the worker to communicate with Prefect Cloud) 5. Environment Variables: - Verify that all required environment variables are set, especially: -

PREFECT_API_KEY

(if using Prefect Cloud) -

PREFECT_API_URL

(if using Prefect Cloud) - Work pool name and other relevant configurations Could you provide some of these details so I can give you more specific guidance? The CloudWatch logs would be particularly helpful in identifying the root cause.

Based on the partial error log and research, this looks like a connectivity issue between your ECS worker and the Prefect API. Here are the most likely causes and their solutions: 1. Network Configuration Issues: - Ensure your ECS task is running in a subnet with internet access - If using a private subnet, verify your NAT Gateway is properly configured - Check security group rules allow outbound HTTPS traffic (port 443) 2. Proxy Configuration: If your network uses a proxy:

HTTPS_PROXY=your-proxy-url
   HTTP_PROXY=your-proxy-url
   NO_PROXY=169.254.169.254,169.254.170.2,/var/run/docker.sock

3. API Configuration: Verify these environment variables are correctly set:

PREFECT_API_KEY=your-api-key
   PREFECT_API_URL=<https://api.prefect.cloud/api/accounts/[ACCOUNT-ID]/workspaces/[WORKSPACE-ID]>

4. SSL/Certificate Issues: If you're seeing SSL-related errors, you might need to set:

SSL_CERT_FILE=/path/to/cert.pem  # If using custom certificates

To better help you, could you please provide: 1. The complete error traceback 2. Your ECS task definition (with sensitive information redacted) 3. Confirmation whether you're using Prefect Cloud or self-hosted 4. The network configuration of your ECS task (subnet type, security group rules) This will help me provide more specific guidance for your setup.

Given that you're using a self-hosted server with

--host 0.0.0.0

, here are several key points to check: 1. Server Accessibility: - Your self-hosted server needs to be accessible from the ECS task's network - Using

0.0.0.0

only means the server is listening on all network interfaces of its host machine - The server still needs to be exposed via a proper URL that the ECS worker can reach 2. Environment Variables: Your ECS task definition should include:

PREFECT_API_URL=<http://YOUR-SERVER-ADDRESS:4200/api>

Replace

YOUR-SERVER-ADDRESS

with either: - The internal DNS name if the server is in the same VPC - The public DNS/IP if the server is exposed publicly - Make sure to use the correct protocol (http/https) and port 3. Network Configuration: - If your Prefect server is in the same VPC: - Ensure the ECS task's security group allows outbound traffic to the server's security group - The server's security group should allow inbound traffic on port 4200 from the ECS task's security group - If your Prefect server is external: - The ECS task needs internet access (via IGW or NAT Gateway) - The server needs to be publicly accessible - The server's firewall/security group needs to allow inbound traffic from your VPC's IP range 4. Verify Connectivity: You can test connectivity by: - Adding a temporary container to your ECS task definition that runs:

bash
     curl ${PREFECT_API_URL}/health

- This should return a successful response if connectivity is working Could you please provide: 1. The URL you're using for

PREFECT_API_URL

in your ECS task definition 2. Where is your self-hosted Prefect server running (EC2, local machine, etc.)? 3. Is the server in the same VPC as the ECS tasks? This will help me provide more specific guidance for your network configuration. Also, since you mentioned the default VPC configuration allows all traffic, please note that: 1. This is likely the inbound rules for your security group 2. You still need to ensure: - The subnet has a route to the internet (if server is external) - The ECS task has the correct network mode in its task definition - The server is actually reachable from the ECS task's network perspective

Ah, now I understand the issue! The worker is failing because

127.0.0.1

(localhost) in the ECS container refers to the container itself, not your local machine. The ECS container running in AWS cannot directly connect to your local machine's localhost. Here are your options: 1. Temporary Solution - Expose Local Server: You can expose your local server to the internet temporarily using a service like ngrok:

bash
   ngrok http 4200

Then use the ngrok URL in your ECS task:

PREFECT_API_URL=<https://your-ngrok-url/api>

⚠️ Note: This is good for testing but not recommended for production use. 2. Recommended Production Solutions: a. Deploy Prefect Server on AWS: - Run the server on an EC2 instance in the same VPC as your ECS tasks - Or use ECS/Fargate to run the server itself - Then use the internal DNS name or IP for communication b. Use Prefect Cloud: - Switch to using Prefect Cloud instead of self-hosting - This provides a managed, reliable solution - URL would be

<https://api.prefect.cloud/>...

3. Alternative Development Approach: - Run both your flows and worker locally during development - Only move to ECS when deploying to production - This makes development/testing easier Here's a quick comparison of these options: | Solution | Pros | Cons | |----------|------|------| | ngrok (temporary) | - Quick to set up<br>- Good for testing | - Not secure for production<br>- Dependent on third-party service | | AWS-hosted server | - Proper production setup<br>- Secure internal communication | - Additional AWS costs<br>- More infrastructure to manage | | Prefect Cloud | - No infrastructure management<br>- Built-in monitoring | - Monthly cost<br>- Less control over infrastructure | | Local development | - Simplest for development<br>- No cloud costs | - Different from production environment | For your immediate needs, I recommend either: 1. Use ngrok for testing if you just want to validate your ECS setup 2. Or switch to local development if you're still in the development phase For production use, you should: 1. Deploy the Prefect server properly in AWS 2. Or consider using Prefect Cloud Would you like me to provide more detailed instructions for any of these approaches? Let me know which direction you'd prefer to go, and I can provide specific setup steps.

For local development, you'll want to use a "process" work pool instead of ECS. Here's how to modify your prefect.yaml file for local development:

yaml
name: local-dev-guide
prefect-version: 2.14.20

# For local development, we don't need the build and push sections
# since we're not using Docker containers

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: my_local_deployment
  version:
  tags: []
  description:
  entrypoint: flow.py:my_flow
  parameters: {}
  work_pool:
    name: local-process-pool  # We'll create this process pool
    work_queue_name: default
  schedules: []

Here are the steps to set this up: 1. Create a Process Work Pool:

bash
   prefect work-pool create local-process-pool --type process

2. Start a Worker:

bash
   prefect worker start --pool local-process-pool

3. Deploy Your Flow:

bash
   prefect deploy

Some key differences from the ECS version: 1. Removed the

build

and

push

sections since we're not using Docker 2. Changed the work pool type to

process

instead of

ecs

3. Removed Docker-specific configurations like

image

4. Removed the

pull

section since we're running locally Benefits of this setup: - Simpler configuration - Faster development cycle - No need for Docker or AWS during development - Same deployment workflow as production Once you've tested everything locally and you're ready to move to ECS, you can: 1. Create a new prefect.yaml for production (e.g.,

prefect.prod.yaml

) 2. Copy back the AWS-specific configurations 3. Create an ECS work pool for production Would you like me to provide more details about: 1. How to run and test flows with this local setup? 2. How to manage different configurations for development and production? 3. How to transition from local to ECS when you're ready?

Based on the research, here's how to ensure you can see your print statements: 1. Modify your flow code to enable print logging:

python
from prefect import flow

@flow(log_prints=True)  # Add this parameter
def your_flow():
    print("This will be captured in logs")
    # your flow code here

2. View the output: - Terminal: Watch the terminal where you started the worker - Prefect UI: 1. Go to http://127.0.0.1:4200 (your local server) 2. Click on "Flow Runs" 3. Find your flow run 4. Click on the "Logs" tab Alternatively, you can use Prefect's logger for more structured logging:

python
from prefect import flow, get_run_logger

@flow
def your_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("This is a log message")
    # your flow code here

The advantage of using

get_run_logger()

over print statements is that you can: - Use different log levels (debug, info, warning, error) - Include structured metadata - Better control over log formatting - Easier to filter and search in the UI Would you like me to provide more examples of logging patterns or explain how to customize the logging behavior?

To capture Dask-related logs in your Prefect logs, you'll need to configure the logging levels for both Prefect and Dask. Here's how to do it: 1. Configure Logging Levels via Environment Variables:

bash
# Enable debug logging for prefect-dask
export PREFECT_LOGGING_LEVEL="DEBUG"
export PREFECT_LOGGING_EXTRA_LOGGERS='["prefect_dask", "distributed.scheduler", "distributed.worker"]'

2. Or Configure in Your Flow Code:

python
from prefect import flow
from prefect_dask import DaskTaskRunner
import logging

# Configure logging before creating the flow
logging.getLogger("prefect_dask").setLevel(logging.DEBUG)
logging.getLogger("distributed.scheduler").setLevel(<http://logging.INFO|logging.INFO>)
logging.getLogger("distributed.worker").setLevel(<http://logging.INFO|logging.INFO>)

@flow(
    log_prints=True,
    task_runner=DaskTaskRunner(
        cluster_kwargs={"n_workers": 2},
        # Optionally add debug=True to see more Dask details
        debug=True
    )
)
def your_flow():
    print("This will be captured in logs")
    # your flow code here

3. Alternative: Use a logging configuration file: Create a

logging.yaml

file:

yaml
version: 1
formatters:
  prefect:
    format: "[%(asctime)s] %(levelname)s - %(message)s"

handlers:
  console:
    class: logging.StreamHandler
    formatter: prefect
    stream: <ext://sys.stdout>

loggers:
  prefect:
    level: DEBUG
  prefect_dask:
    level: DEBUG
  distributed.scheduler:
    level: INFO
  distributed.worker:
    level: INFO

root:
  handlers: [console]
  level: INFO

Then set the environment variable:

bash
export PREFECT_LOGGING_SETTINGS_PATH="/path/to/your/logging.yaml"

4. View the Logs: - In the Prefect UI (http://127.0.0.1:4200): - Go to Flow Runs - Click on your specific flow run - Check the Logs tab - In your terminal where the worker is running Important notes: 1. The

debug=True

parameter in DaskTaskRunner will show more detailed Dask operations 2. You might want to adjust the log levels based on your needs: - DEBUG: Most verbose, shows all details - INFO: Standard operational messages - WARNING: Only shows warnings and errors Would you like me to: 1. Provide more detailed examples of logging configuration? 2. Explain how to filter specific types of Dask logs? 3. Show how to customize the log format for Dask-specific messages?

There are a few ways to handle a Parquet file parameter in your flow deployment. Let me show you the options: 1. Define the Flow with Parameter:

python
# flow.py
from prefect import flow, Parameter
import pandas as pd
from pathlib import Path

@flow
def process_parquet(parquet_path: str):
    # Convert string path to Path object if needed
    path = Path(parquet_path)
    df = pd.read_parquet(path)
    # Your processing logic here
    print(f"Processing file: {parquet_path}")
    print(f"DataFrame shape: {df.shape}")

2. Configure Deployment in prefect.yaml:

yaml
deployments:
- name: process-parquet-deployment
  entrypoint: flow.py:process_parquet
  work_pool:
    name: local-process-pool
  parameters:
    parquet_path: "data/default.parquet"  # Default value if needed

3. Options for Running the Flow: A. Using the CLI with --param:

bash
# Deploy the flow
prefect deploy

# Run with specific parameter
prefect deployment run process-parquet/process-parquet-deployment --param parquet_path="path/to/your/file.parquet"

B. Using the Prefect UI: 1. Go to http://127.0.0.1:4200 2. Navigate to your deployment 3. Click "Run" 4. You'll see a form where you can input the parquet_path parameter C. Programmatically:

python
from prefect import flow
from prefect.deployments import Deployment

@flow
def process_parquet(parquet_path: str):
    # Your flow code here
    pass

# Create deployment
deployment = Deployment.build_from_flow(
    flow=process_parquet,
    name="process-parquet-deployment",
    work_pool_name="local-process-pool",
    parameters={"parquet_path": "data/default.parquet"}
)
deployment.apply()

4. Best Practices for File Paths: A. Relative Paths: If your Parquet files are part of your project:

your_project/
├── flows/
│   └── flow.py
├── data/
│   └── input.parquet
└── prefect.yaml

Use relative paths in your parameters:

yaml
parameters:
  parquet_path: "../data/input.parquet"

B. Absolute Paths: For files outside your project:

yaml
parameters:
  parquet_path: "/absolute/path/to/data.parquet"

C. Environment Variables: For flexible paths:

python
@flow
def process_parquet(parquet_path: str = None):
    path = parquet_path or os.getenv("DEFAULT_PARQUET_PATH")
    # ... rest of your flow

5. Validation Example:

python
from prefect import flow
from pathlib import Path

@flow
def process_parquet(parquet_path: str):
    path = Path(parquet_path)
    
    # Validate file exists and is parquet
    if not path.exists():
        raise FileNotFoundError(f"Parquet file not found: {parquet_path}")
    if path.suffix != '.parquet':
        raise ValueError(f"File must be a parquet file, got: {path.suffix}")
        
    # Your processing logic here

Would you like me to: 1. Show how to handle multiple Parquet files as parameters? 2. Demonstrate how to validate Parquet file content before processing? 3. Explain how to handle different file locations (local vs remote storage)? 4. Show how to set up scheduled runs with different parameter values?