I apologize, but I need more specific information about the error you're experiencing to help effectively. Could you please provide: 1. The exact error message you're seeing 2. Whether this is happening in a deployment or just when running locally 3. A minimal example of the flow code that's causing the issue 4. Which version of Prefect you're using (2.x or 3.x) The term "crashed error" could refer to several different scenarios, and having these details will help me: - Identify if it's a known issue - Search through relevant GitHub issues - Provide targeted solutions for your specific case Could you share these details so I can better assist you?

@Marvin im running prefect 3, here are my deployment.yaml - name: avisena-prod version: tags: - avisena concurrency_limit: description: Prod etl flow from reading sql to uploading to backend pull: *git_prod_pull entrypoint: prefect_folder/main.py:healthannotate_flow parameters: {} work_pool: name: avisena-work-pool work_queue_name: job_variables: env: ENV: prod TIMEZONE: Asia/Kuala_Lumpur ORGANIZATION: avisena ORGANIZATION_GROUP_ID: 994bcd73-78df-cca4-906d-abac7bd5e65f enforce_parameter_schema: true schedules: - cron: "5 */3 * * *" timezone: "Asia/Kuala_Lumpur"

I notice a few potential issues with your deployment configuration and need some clarification: 1. First, in Prefect 3.x, the deployment configuration format has changed. The YAML format you're using looks more like Prefect 2.x style. In Prefect 3.x, deployments are typically created using either: - The

prefect deploy

CLI command - Python code using

flow.deploy()

or

flow.from_source().deploy()

2. I notice you're using a git pull configuration (

*git_prod_pull

) but I don't see the full YAML where this anchor is defined. 3. You mentioned it's "crashing" - could you please share: - The exact error message you're seeing - Whether the crash happens during deployment creation or during flow runs - Whether you're using a worker to run this deployment To help you move forward, here's how you could modernize this for Prefect 3.x:

python
from prefect import flow
from prefect.deployments import Deployment
from prefect.filesystems import GitHub

@flow(name="healthannotate_flow")
def your_flow():
    # your flow code here
    pass

# Option 1: Using flow.deploy()
flow.deploy(
    name="avisena-prod",
    work_pool_name="avisena-work-pool",
    tags=["avisena"],
    job_variables={
        "env": {
            "ENV": "prod",
            "TIMEZONE": "Asia/Kuala_Lumpur",
            "ORGANIZATION": "avisena",
            "ORGANIZATION_GROUP_ID": "994bcd73-78df-cca4-906d-abac7bd5e65f"
        }
    },
    cron="5 */3 * * *"
)

# Option 2: If using GitHub source
flow.from_source(
    source="<https://github.com/your-repo/your-project>",
    entrypoint="prefect_folder/main.py:healthannotate_flow"
).deploy(
    name="avisena-prod",
    work_pool_name="avisena-work-pool",
    tags=["avisena"],
    job_variables={
        "env": {
            "ENV": "prod",
            "TIMEZONE": "Asia/Kuala_Lumpur",
            "ORGANIZATION": "avisena",
            "ORGANIZATION_GROUP_ID": "994bcd73-78df-cca4-906d-abac7bd5e65f"
        }
    },
    cron="5 */3 * * *"
)

To help you further, I need: 1. The exact error message you're seeing 2. Confirmation of whether you're using a worker 3. The method you're using to create and run the deployment With this information, I can provide more targeted assistance and check for specific known issues that might be affecting your deployment.

Based on the research and your error message, here are several steps to debug and potentially fix the issue: 1. Enable Detailed Logging First, let's get more detailed logs. You can do this by:

bash
prefect config set PREFECT_LOGGING_LEVEL="DEBUG"

2. Check Worker Logs Run this command to see the detailed worker logs:

bash
prefect worker log avisena-work-pool

3. Verify Flow Run Logs In the Prefect UI, look for the specific flow run that failed and check its logs for more detailed error messages before the process exit. 4. Common Causes to Check: - Missing dependencies in your environment - Incorrect environment variables - File path issues in your deployment - Memory constraints - Python version mismatches 5. Verification Steps: a. First, try running your flow locally without the deployment:

python
   if __name__ == "__main__":
       healthannotate_flow()

b. Check if all required packages are installed in the environment where the worker is running c. Verify that all environment variables specified in your job_variables are accessible 6. Worker Configuration Let's check your work pool configuration:

bash
prefect work-pool inspect avisena-work-pool

Could you please: 1. Run the flow with debug logging enabled and share any additional error messages you see? 2. Confirm if the flow runs successfully when executed locally without deployment? 3. Share the contents of your

prefect_folder/main.py

file (or at least the flow definition)? This will help us pinpoint whether it's: - An environment issue (missing dependencies) - A configuration issue (incorrect work pool settings) - A code issue (something in the flow itself) - A deployment issue (incorrect deployment configuration) Also, make sure your process worker is running with sufficient permissions and resources to execute your flow. Sometimes process workers can fail if they don't have access to required system resources or if there are permission issues with file access.