In Prefect 3.x, when deploying from a Git repository, you can specify a particular branch using the

reference

parameter in the

from_source()

method. Here's the syntax:

python
from prefect import flow
from prefect.deployments import Deployment

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

deployment = flow.from_source(
    source="<https://github.com/username/repo>",
    reference="my-branch-name"  # Specify your branch here
).deploy(name="my-deployment")

You can also specify the branch when deploying via the CLI:

bash
prefect deploy path/to/flow.py:my_flow \
    --name my-deployment \
    --source <https://github.com/username/repo> \
    --reference my-branch-name

A few important notes: - If you don't specify a

reference

, it defaults to the repository's default branch (usually

main

or

master

) - The

reference

parameter can be: - A branch name (e.g.,

develop

) - A tag (e.g.,

v1.0.0

) - A specific commit SHA For more details, you can refer to the deployment versioning documentation.

This error typically occurs when there's a fundamental issue preventing your flow from even starting. Here are the most common causes and how to troubleshoot them: 1. Missing Dependencies - Ensure all required packages are listed in your

requirements.txt

or

setup.py

- Double-check that the requirements file is being properly referenced in your deployment 2. Import Errors - Verify that all imports in your flow code are available in the execution environment - Check for any absolute imports that might not work in the deployed context - Ensure your code structure matches your import statements 3. Python Version Mismatch - Confirm the Python version in your deployment matches what your code expects - Check for any syntax that might not be compatible with the Python version 4. Environment Variables - Verify all required environment variables are properly set in Prefect Cloud - Check for any hardcoded local paths or environment-specific configurations To troubleshoot: 1. First, try running your flow locally with the exact same configuration:

python
from prefect import flow
from prefect.deployments import Deployment

deployment = flow.from_source(
    source="your-source",
    entrypoint="path/to/flow.py:flow_name"
).load()

deployment.flow.serve(name="test-local")

2. Add explicit logging at the very start of your flow:

python
from prefect import flow
import logging

@flow
def your_flow():
    <http://logging.info|logging.info>("Flow starting - basic setup complete")
    # rest of your flow

3. Check the infrastructure configuration in your deployment:

python
deployment = flow.from_source(
    source="your-source",
    entrypoint="path/to/flow.py:flow_name"
).deploy(
    name="my-deployment",
    work_pool_name="your-work-pool",
    image="your-image-if-using-custom"
)

4. Review the work pool settings in the Prefect Cloud UI to ensure they match your expectations. If these steps don't resolve the issue, could you share: 1. The basic structure of your flow code 2. Your deployment configuration 3. Any requirements.txt or dependency specifications This would help pinpoint the exact cause of the failure.

No, having an async main function shouldn't cause this issue - Prefect 3.x fully supports async flows. However, I notice a potential issue with your deployment command. There appears to be a typo in your GitHub URL that might be causing the problem. Your current command:

bash
prefect-cloud deploy src/flows/sportradar.py:main --from <https://github.com/Marut-AI/nil_etl/prefect_deployment> --with-requirements requirements.txt  --name "Sportradar Sync"

The URL appears to have angle brackets

<>

and possibly an incorrect path structure. Try this instead:

bash
prefect-cloud deploy src/flows/sportradar.py:main --from <https://github.com/Marut-AI/nil_etl> --with-requirements requirements.txt --name "Sportradar Sync"

A few other things to check: 1. Flow Definition: Ensure your async flow is properly decorated:

python
from prefect import flow

@flow
async def main():
    # your async code here
    pass

2. Requirements File: Double-check that your

requirements.txt

includes: -

asyncio

(though it's usually part of Python's standard library) - Any other async-related packages you're using (like

aiohttp

,

httpx

, etc.) 3. Repository Structure: Make sure: - The path

src/flows/sportradar.py

exists relative to the root of your repository - The requirements.txt file is at the root of your repository 4. Debug Version: Try adding some immediate logging at the start of your flow:

python
from prefect import flow
import logging

@flow
async def main():
    <http://logging.info|logging.info>("Starting flow execution")
    print("Flow started")  # Sometimes print statements are helpful for quick debugging
    # ... rest of your code

If these adjustments don't resolve the issue, could you share: 1. The basic structure of your async flow (just the main function definition and first few lines) 2. The contents of your requirements.txt file 3. The exact error message you're seeing in the Prefect Cloud UI This would help identify if there's something specific about your async implementation that's causing issues.

Based on the research, here are several steps to debug and fix this issue: 1. Enable Detailed Logging First, modify your flow to include detailed logging at the earliest possible point:

python
from prefect import flow
import logging
import sys

# Configure logging to be as verbose as possible
logging.basicConfig(level=logging.DEBUG)

@flow
async def main():
    logging.debug("Python version: %s", sys.version)
    logging.debug("Starting flow execution")
    try:
        # Your existing code here
        pass
    except Exception as e:
        logging.exception("Error during flow execution")
        raise

2. Verify Work Pool Configuration In the Prefect Cloud UI: - Check that all environment variables are correctly set - Verify the Python version matches your local development environment - Ensure all requirements are properly listed - Check the infrastructure type and settings 3. Test Local Deployment Try running the deployment locally first:

bash
prefect deploy src/flows/sportradar.py:main \
    --name "Sportradar Sync Local" \
    --pool "process" \
    --with-requirements requirements.txt

4. Gradual Deployment Try deploying a minimal version of your flow first:

python
from prefect import flow

@flow
async def minimal_test():
    print("Hello from minimal test")
    return "Success"

If this works, gradually add back components until you identify the problematic part. 5. Check Requirements Ensure your requirements.txt includes all necessary packages. For async flows, you might need:

httpx  # if making HTTP requests
aiohttp  # if using aiohttp
asyncio  # usually built-in, but list if you have a specific version requirement

6. Infrastructure Logs In the Prefect Cloud UI: - Go to your flow run - Check the "Infrastructure Logs" tab - Look for any errors during container startup 7. Common Fixes - If using custom Docker images, ensure they have Python installed - Check that your repository URL is correct and accessible - Verify that the entrypoint path in your deployment matches your repository structure - Ensure any required credentials (like GitHub access) are properly configured Could you try the minimal async flow test above and let me know if it works? Also, it would be helpful to know: 1. What infrastructure type you're using in your work pool? 2. Are you using any custom Docker images? 3. Can you see any additional error messages in the infrastructure logs tab?

Ah, now we're getting somewhere! This is a repository access issue. Since you're working with a private repository, Prefect Cloud needs proper authentication to access your code. The recommended way to handle this is to set up the Prefect Cloud GitHub App. Here's how to fix this: 1. First, authenticate with GitHub using:

bash
prefect-cloud github setup

This will: - Install the Prefect Cloud GitHub App in your organization - Set up the necessary permissions - Handle authentication automatically for future deployments Alternatively, if you prefer not to use the GitHub App, you can provide credentials directly:

bash
prefect-cloud deploy src/flows/sportradar.py:main \
    --from <https://github.com/Marut-AI/nil_etl> \
    --with-requirements requirements.txt \
    --name "Sportradar Sync" \
    --credentials github-credentials

You'll need to first create a GitHub credentials block in the UI or via the CLI:

bash
prefect cloud block create github-credentials

Also, make sure to specify the correct branch if you're not using the default branch:

bash
prefect-cloud deploy src/flows/sportradar.py:main \
    --from <https://github.com/Marut-AI/nil_etl> \
    --reference your-branch-name \
    --with-requirements requirements.txt \
    --name "Sportradar Sync"

The "Essential container exited" error was occurring because Prefect couldn't access your repository to get the code in the first place. Once we fix the repository access, we can tackle any remaining issues if they exist. Would you like to try the GitHub App setup approach first? It's generally the most straightforward and secure method for private repositories.

For the more recent question: could you share a flow run id you're seeing exit without logs?

This might be the flow_run id: 0687067d-7f09-7fa1-8000-f7419c3bc113

It's weird, because it runs local and sends logs to cloud, and I can deploy it to a self-hosted and it works there. I authenticated via the github setup, and it seems like it's able to read from git and deploy, but when it runs it just crashes with no output other than the container crashed.

this is from the pip packages you have set on your work pool specifically

(As a side note: you're not required to set them there and can actually just rely on installing from your requirements file on your deployment, which I see you're already doing)

Looks like the pydantic version is the issue though, so I'll adjust that. Thanks!

Will open an issue for the error not making it back to you

@Marvin What is the correct syntax for retrieving values from the work pool env? I adjusted your basic sample to look like this, but when it prints the environmental the result is None: from prefect import flow, get_run_logger import os from dotenv import load_dotenv load_dotenv() @flow async def *minimal_test*(): logger = get_run_logger() logger.info("Hello from minimal test") SNR = os.getenv("SNOWFLAKE_ROLE") logger.info(f"Snowflake role: {SNR}") return "Success"

For accessing work pool environment variables, you should use

get_settings_context()

instead of

os.getenv()

. Here's the correct way to access your environment variables:

python
from prefect import flow, get_run_logger
from prefect.context import get_settings_context

@flow
async def minimal_test():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Hello from minimal test")
    
    # Get all settings including environment variables
    settings = get_settings_context()
    SNR = settings.get("SNOWFLAKE_ROLE")
    <http://logger.info|logger.info>(f"Snowflake role: {SNR}")
    
    return "Success"

Alternatively, if you prefer using environment variables directly, you can use:

python
from prefect import flow, get_run_logger
import os
from prefect.settings import PREFECT_API_KEY

@flow
async def minimal_test():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Hello from minimal test")
    
    # Direct environment variable access
    SNR = os.environ.get("SNOWFLAKE_ROLE")  # prefer .get() over getenv()
    <http://logger.info|logger.info>(f"Snowflake role: {SNR}")
    
    return "Success"

A few important notes: 1.

os.environ.get()

is preferred over

os.getenv()

as it's more explicit about dictionary access 2. Environment variables set in the work pool should be available when the flow runs 3. You can verify the environment variables are set correctly in your work pool by: - Going to the work pool settings in the UI - Checking the "Environment Variables" section - Ensuring your variable is listed there If you're still getting

None

, double-check: 1. The exact name of the environment variable in your work pool settings 2. That there are no typos or case mismatches 3. That the work pool is correctly selected for your deployment You can also add some debug logging to see all available environment variables (be careful not to log sensitive information in production):

python
from prefect import flow, get_run_logger
import os

@flow
async def minimal_test():
    logger = get_run_logger()
    
    # Log all environment variables (careful with sensitive data!)
    logger.debug("Available environment variables:")
    for key in sorted(os.environ.keys()):
        if not any(sensitive in key.lower() for sensitive in ['password', 'secret', 'key']):
            logger.debug(f"{key}: {os.environ.get(key)}")
    
    SNR = os.environ.get("SNOWFLAKE_ROLE")
    <http://logger.info|logger.info>(f"Snowflake role: {SNR}")
    
    return "Success"

This will help you verify what environment variables are actually available to your flow at runtime.

There are a few ways to fix this issue: 1. Check Work Pool Variable Configuration In the Prefect Cloud UI: - Go to your work pool - Click "Edit" - Go to the "Variables" tab (not just the environment variables section) - Make sure your variables are listed there - Save the changes 2. Set Variables at Deployment Time When creating your deployment, explicitly set the variables:

bash
prefect-cloud deploy src/flows/sportradar.py:main \
    --name "Sportradar Sync" \
    --pool your-pool-name \
    --var SNOWFLAKE_ROLE="your-role" \
    --var OTHER_VAR="other-value"

3. Use Job Configuration You can also set these in your deployment configuration:

python
from prefect import flow
from prefect.deployments import Deployment

@flow
async def your_flow():
    pass

deployment = flow.from_source(
    source="<https://github.com/Marut-AI/nil_etl>",
    entrypoint="src/flows/sportradar.py:main"
).deploy(
    name="Sportradar Sync",
    work_pool_name="your-pool-name",
    job_variables={
        "SNOWFLAKE_ROLE": "your-role",
        "OTHER_VAR": "other-value"
    }
)

4. Check Work Pool Base Job Template In the work pool settings, under the "Advanced" tab, verify the base job template includes environment variable handling. For a Prefect-managed work pool, you might need to add or modify the variables section:

json
{
  "variables": {
    "type": "object",
    "properties": {
      "SNOWFLAKE_ROLE": {
        "type": "string",
        "title": "Snowflake Role"
      }
    }
  }
}

5. Temporary Debug Flow To debug this, you can create a simple flow that prints all environment information:

python
from prefect import flow, get_run_logger
import os
import json
from prefect.context import get_settings_context

@flow
async def debug_env():
    logger = get_run_logger()
    
    # Log regular environment variables
    <http://logger.info|logger.info>("OS Environment Variables:")
    env_vars = {k: v for k, v in os.environ.items() 
                if not any(sensitive in k.lower() 
                          for sensitive in ['password', 'secret', 'key'])}
    <http://logger.info|logger.info>(json.dumps(env_vars, indent=2))
    
    # Log Prefect settings context
    <http://logger.info|logger.info>("Prefect Settings Context:")
    settings = get_settings_context()
    <http://logger.info|logger.info>(json.dumps(settings, indent=2))
    
    # Log specific variables you're looking for
    <http://logger.info|logger.info>(f"SNOWFLAKE_ROLE from os.environ: {os.environ.get('SNOWFLAKE_ROLE')}")
    <http://logger.info|logger.info>(f"SNOWFLAKE_ROLE from settings: {settings.get('SNOWFLAKE_ROLE')}")

    return "Debug Complete"

Could you: 1. Try running this debug flow to see exactly what variables are available? 2. Confirm how you initially set up the variables in the work pool (via UI, CLI, or deployment)? 3. Check if you're using any variable prefixes in the work pool configuration? This will help us pinpoint exactly where the configuration isn't being passed through correctly.