@Marvin apologies for being that intensive... i get this error distributed.worker - Compute Failed Exception: "1 validation error for EngineContext\nresult_store.result_storage.basepath\ when I put result_storage='remote-file-system/minio-results' in the flow's definition and I have the following calls in my flow

futures = []
for i in range(1):
    futures.append(task.submit(i))

wait(futures)

No worries! It looks like you're encountering an error related to the result storage configuration in your Prefect flow. To better assist you, I need to confirm which version of Prefect you're using, as the syntax and configuration options can differ between versions. Could you please specify if you're using Prefect 2.x or Prefect 3.x? If you're not sure, you can check your installed Prefect version by running the command

prefect version

in your terminal or Jupyter notebook. Let me know, and I'll guide you accordingly!

@Marvin prefect 3

The error you're encountering seems to be related to the configuration of the result storage in your Prefect environment. Here's some guidance based on what you've shared and the findings from the Prefect documentation: 1. Result Storage Configuration: In Prefect 3.x, result storage needs to be correctly configured, especially when specifying the storage backend. The error message suggests there might be an issue with how the

result_storage

is specified in your flow. 2. Setting up Remote File Systems: If you're using a remote file system like MinIO for result storage, ensure that you have set up a Storage Block correctly in Prefect. You might need to define a block in your Prefect deployment or in the configuration that specifies the connection details to your MinIO server. 3. Validation Errors: The error message indicating a validation error for

EngineContext

suggests that something might not be correctly set up or is missing in your configuration. To resolve these issues, you can take the following steps: - Verify Configuration: Double-check your storage configuration. Make sure you have defined and registered a Storage Block for MinIO within Prefect. Ensure all necessary authentication details and URLs are correctly configured. - Documentation Check: You can review the Prefect 3.x documentation on result persistence and storage configuration for guidelines on setting up and troubleshooting storage configurations. - Testing Connection: Try connecting to your MinIO instance separately to ensure there are no connectivity or permission issues that could be causing this problem. If you've tried these steps and are still facing issues, you might want to explore Prefect's support channels or look into related GitHub issues to see if others have faced similar problems. If you have further questions or need more help, feel free to ask!

@Marvin give me the url of the gitlab issues

It seems there are no specific GitHub issues related to "result_storage with remote-file-system or MinIO and validation errors" in the

prefecthq/prefect

repository. If you're still experiencing difficulties or think this might be a bug or undocumented behavior, you could consider creating a new issue in the Prefect GitHub repository with detailed information about your configuration and the errors you're encountering. This way, the Prefect community or developers can provide more targeted help or fix potential issues. If you have any other questions or need further assistance, feel free to ask!

@Marvin distributed.comm.core.CommClosedError: in <TCP (closed) Scheduler connection to worker local=tcp://127.0.0.1:51846 remote=tcp://127.0.0.1:51868>: Stream is closed

The

CommClosedError

you're encountering suggests that there might be a problem with the network communication between the scheduler and workers in your distributed environment. Here are a few steps you can take to troubleshoot and resolve this issue: 1. Check Network Configuration: Ensure that there are no firewall rules or network policies blocking communication between different components in your Prefect setup. All necessary ports must be open for traffic both on the machine running the scheduler and the workers. 2. Resource Limits: Make sure that your system has sufficient resources (CPU, RAM, etc.) allocated for running your Prefect flows. Sometimes, resource constraints can lead to unexpected disconnections. 3. Consistent Software Versions: Ensure that all components involved (scheduler, workers, etc.) are using compatible versions of Prefect and any other dependency packages. Different versions might have protocol changes that lead to these kinds of issues. 4. Retry Mechanisms: Check if your flow configuration includes retry mechanisms for tasks that may fail due to transient network issues. Prefect allows you to set retry policies to handle temporary network glitches. 5. Logs and More Details: Examine the worker and scheduler logs for more detailed error messages that might give clues about the cause of the closed connection. Logs often provide the specific context needed to diagnose such issues. 6. Running Locally: If this is occurring in a development or local setup with everything on

localhost

, it might help to restart your Prefect server, scheduler, and workers to clear any lingering state that might be causing the issue. If the issue persists after trying these troubleshooting steps, consider reaching out to Prefect's community or opening an issue on their GitHub repository to get more tailored support. Feel free to ask if you have more questions or need further assistance!