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Hi <@ULVA73B9P> I keep getting a ```Flow run infra...
# ask-marvin
j
Hi @Marvin I keep getting a
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Flow run infrastructure exited with non-zero status code:
 Task flow-run-e2436ff7-ca28-42ef-ae21-f65e022fdd61-b643cc869097gvjwk-task0 failed with message: The container exited with an error. (Error Code: 1)
When I make a deployment to a prefect managed work-pool. The task is a simple print line so not sure what’s going on.
m
The error message you're encountering indicates that the container running your Prefect task is exiting with a non-zero status code, which generally means there's an error occurring during execution. Since your task is a simple print statement, the issue might not be with your code but rather with the configuration or environment setup. Here are some potential areas to investigate: 1. Environment Configuration: Ensure that the environment in which the container is running has all necessary configurations and dependencies. Even for a simple print statement, the environment needs to be correctly set up. 2. Logs and Error Messages: Check the detailed logs for more specific error messages. Prefect's cloud or server UI should provide logs that might give more insight into why the container is failing. 3. Work Pool Configuration: Review the work pool settings to ensure there are no constraints or misconfigurations that might be causing the issue. Here's a link to the work pool documentation for reference. 4. Docker Image: If you're using a custom Docker image, ensure it's correctly built and compatible with Prefect's requirements. 5. Resource Limits: Make sure that the container has sufficient resources allocated to it. Sometimes, insufficient memory or CPU can cause containers to exit unexpectedly. If these steps don't resolve the issue, you might want to try running the flow locally to see if the problem persists outside of the managed work pool environment.
j
@Marvin What environment configuration do I need to change for a print statement in a flow?
m
For a simple print statement in a Prefect flow, the environment configuration should be minimal. However, there are a few basic things to check: 1. Python Environment: Ensure that the Python environment within the container or execution environment is correctly set up. This includes having the correct Python version and any necessary packages installed. 2. Prefect Version: Make sure you have a compatible version of Prefect installed. Since you're using a managed work pool, it should align with Prefect 3.x. Verify that the Prefect version in your environment matches the version expected by your deployment. 3. Network and Permissions: Ensure that the container has the necessary network permissions to communicate with Prefect's API if required. This includes any firewall rules or network policies that might affect connectivity. 4. Logging Configuration: Ensure that logging is configured to capture stdout, which is where print statements are output. This might be configured in your Docker setup or execution environment. 5. Base Image: If using Docker, ensure the base image is compatible and has all necessary components to run Python scripts, such as a Linux distribution with Python installed. If you have checked these configurations and the issue persists, it might be worth testing the flow locally to isolate whether the problem is with the environment or the Prefect setup.
j
@Marvin How do I configure a python environment in the prefect cloud UI for the work pool?
m
To configure a Python environment for a work pool in Prefect Cloud, you'll typically need to manage this through the infrastructure settings associated with the work pool rather than directly through the Prefect Cloud UI. Here’s how you can handle it: 1. Work Pool Creation: When you create a work pool, you can specify details about the infrastructure that will execute runs. This includes configuring the environment where the flow will run. You can do this through the Prefect CLI or directly in the Prefect Cloud UI. 2. Base Job Template: You can specify a base job template that includes the configuration for the environment. This template is often a JSON file that defines the Docker image or other environment settings. 3. Docker Image: If you're using Docker, specify a Docker image that has the desired Python environment. This image should include the necessary Python version and any dependencies your flows require. 4. CLI Commands: Use the Prefect CLI to create or update work pools with specific configurations: - To create a work pool: 
prefect work-pool create --name <NAME> --base-job-template <PATH_TO_JSON>
- To update a work pool: 
prefect work-pool update --name <NAME> --base-job-template <PATH_TO_JSON>
5. Infrastructure Type: Choose the infrastructure type that supports your environment needs (e.g., Kubernetes, Docker). For more details, you can refer to the work pools documentation.
j
@Marvin I keep getting
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Failed due to a(n) crashed flow run - The flow run process exited with a non-zero status code 1, indicating a crash. Further investigation is needed to determine the cause of the crash.
Even when I select 
prefecthq/prefect:3-python3.12
as base image
m
The issue you're experiencing with the flow run crashing and exiting with a non-zero status code 1, even when using the 
prefecthq/prefect:3-python3.12
base image, might be related to several factors as seen in related GitHub issues: 1. Environment and Dependencies: Ensure that all necessary dependencies are included in the Docker image. Even though the base image is specified, additional dependencies required by your flow might not be present. 2. Memory and Resource Limits: The process might be getting killed due to insufficient memory or CPU resources. Check if the container has enough resources allocated to it. This can sometimes cause the operating system to terminate the process with a SIGKILL signal, often reflected as a non-zero exit code. 3. Network and External Access: If your flow requires access to external resources (like a GitLab repository), ensure that the necessary network permissions and credentials are correctly set up. 4. Error in Flow Code: While your flow only contains a print statement, ensure there are no hidden issues in the deployment configuration or in any imported modules that could cause a crash. 5. Prefect Version and Compatibility: Make sure the Prefect version in the Docker image aligns with your deployment configuration. Sometimes, mismatches can lead to unexpected behavior. For further troubleshooting, you might want to: - Check the detailed logs for more specific error messages. - Test the flow locally to isolate whether the issue is with the environment or the Prefect setup. - Review any recent changes in your flow or deployment configuration that might have introduced the issue. For additional context, you can refer to the related GitHub issue where similar problems have been discussed.
j
hey! is your deployment configured for remote code storage?
j
Hi @Jake Kaplan, I’m not sure. I’m pretty new to prefect. I run a local python script and call the 
.deploy
method on a flow.
j
No worries! If you take a look at: https://docs.prefect.io/v3/deploy/infrastructure-examples/managed#images If you want to use prefect managed work pools, you'll need use the 
.from_source
method before you can call 
.deploy
. That indicates to the deployment that your code should be pulled in remotely (from source) at the start of your flow run
otherwise your code won't exist inside of a Managed Flow run, which I think why it's crashing
j
I see 🙂.
Thanks for pointing that out! Will setup a github repo now 😄
🙌 1
@Jake Kaplan I can correctly deploy my code but am running in the same issue when calling a flow from an imported file. As:
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from prefect import flow
from prefect.runner.storage import GitRepository
from prefect_github import GitHubCredentials
from sync_from_airbyte_flow import sync_from_airbyte

github_repo = GitRepository(
    url="<https://github.com/webbio/AirByte>",
    credentials=GitHubCredentials.load("github"),
)

@flow
def master_flow():
    sync_result = sync_from_airbyte()
    return sync_result

if __name__ == "__main__":
    master_flow.from_source(
        source=github_repo,
        entrypoint="orchestration/master_flow.py:master_flow",
    ).deploy(
        name="Sync from Airbyte and run DBT transform",
        work_pool_name="webbio-workpool",
        image="prefecthq/prefect:3-python3.12",
        push=True,
        cron="0 0 * * *",
    )
Any ideas?
j
hey, sorry could you explain a little more what you're seeing? Do you have any error logs?
j
Hey, yes currently the flow is stuck in Late and not showing anything as response
When running this on a local worker it runs fine
Not sure what to do here
j
Hmm. The free tier of prefect managed only supports 1 active run at a time. Could you try deleting any left over in-progress runs? If that doesn't work could you try deleting and re-creating the work pool (and then running your deploy to update the work pool reference to the new one)
j
Already tried all of that unfortunately without any result.
Can my account be bricked?
j
Possibly something has gone wrong on our end. You definitely should be able to keep running things, could you share your workspace id and I can take a quick look?
j
Sure 
077a193c-378f-4a78-b38b-8ce7640ce2c5
j
Can you delete the flow run 
outstanding-oryx
? It looks like it's still in "Cancelling" which counts as in progress state
j
Done, ok let me try again on the managed work pool
Ok that’s it 🙂
Thanks for pointing that out
j
np! Sorry for the friction, we're working on improving the UX for indicating what what runs are blocking. Let me know if you hit any other roadblocks
j
Np, I’m happy that I’m back to my original problem 😅. I keep getting an exited 
exited with status code: 1
. Despite having configured 
["prefect-github"]
in the work-pool.
Anything else I’m missing by any chance?
j
exited with status code: 1
is a general error that the worker is reporting the container didn't succeed. I would expect 
prefect
(that's running your code in the container) to log an error here. let me take a look
j
Hi @Jake Kaplan, no need anymore already solved it 😄
🙌 1
Thanks!
j
always happy to do nothing haha 😅
what was it?
j
Last question I have though.
what was it?
I think I was a bit too fast. When I did a rerun it starting running my code in the flow.
Another question; I have a server running in AWS (Airbyte). The prefect worker needs to make a API call to this instance. Is there any set of IP’s managed prefect workers use? So I can add them to a whitelist for inbound traffic.
j
Unfortunately we do not have a list of static ips for managed pools at the moment, but it's on our roadmap to offer
j
👍 , ok. Thanks for all your help! 😄
a
@Jake Kaplan Hey Jake, I have same problem here. The error is equal that Joeri share here. Can help me with this error ?
j
Taking a quick look at your work pool I think your pip packages are incorrectly formatted. It looks like you have one giant string as the first array entry: 
["package1, package2, package3"]
instead of each python package as a string item in the array 
["package1", "package2", "package3"]
🙌 1
We're releasing some changes soon that should hopefully surface more helpful error messages here
✅ 1
a
Thanks for a quick look; I see that the placeholder is changed. I fixed and activated it, but I ran another flow, and the error continued. https://app.prefect.cloud/account/0b154ac9-02ee-48c5-b054-3bba2f75c5b4/workspace/75c7[…]-e3ed0a574d9f?entity_id=9cca4220-14eb-4654-b491-37ccd0019f71
j
I think you're trying to install 
smtplib
but that's not a package available via pip, it's just part of the python standard library. you can just import it w/o installing anything extra
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