Hello @Marvin. when setting up prefect-ray and go to deploy my flow, I get the following error:

13:43:38.672 | INFO    | prefect.engine - Created flow run 'qualified-groundhog' for flow 'training-pipeline'
13:43:38.672 | INFO    | Flow run 'qualified-groundhog' - View at <https://1723c8f0a74698b29db0b96f82ec23ca.dv-kubeflow.com/flow-runs/flow-run/a780d274-6693-4025-8d27-40b63cef70c5>
13:43:38.672 | INFO    | prefect.task_runner.ray - Connecting to an existing Ray instance at <http://raycluster-kuberay-head-svc.kuberay.svc.cluster.local:8265>
2024-08-16 13:43:38,672 WARNING worker.py:1446 -- SIGTERM handler is not set because current thread is not the main thread.
13:43:38.672 | ERROR   | Flow run 'qualified-groundhog' - Crash detected! Execution was interrupted by an unexpected exception: AssertionError: Module: http does not have ClientBuilder.

Obviously if I am deploying from a machine outside of my k8 cluster, it wouldn't be able to access the rayCluster deployed. Question is, how can I remedy this?

It seems the exact error you are encountering isn't directly addressed in the Prefect documentation or reported GitHub issues as of now. However, based on typical scenarios, here are some steps you can follow to diagnose and possibly fix the issue: 1. Check Ray Setup: Ensure your Ray cluster is correctly set up and accessible from your deployment environment. This might involve checking the network setup or permissions to access the Ray cluster from outside the Kubernetes cluster. 2. Prefect-Ray Compatibility: Make sure you are using compatible versions of Prefect, Ray, and

prefect-ray

. An incompatibility could lead to errors during runtime. 3. Set up SIGTERM Handler: The warning indicates that the SIGTERM signal handler isn't set because the thread is not the main one. This could be benign, but in some cases, it might cause issues. Setting up a proper signal handler could help. 4. Ray ClientBuilder: The

AssertionError: Module: http does not have ClientBuilder

suggests that there might be a missing or incorrect configuration in your Ray or Prefect-Ray setup. Verifying module imports and ensuring all necessary components are correctly installed would be essential. 5. Networking Issues: As indicated by your own observation, deploying from outside the k8 cluster might result in network accessibility issues. Ensure your Ray cluster endpoint is accessible from your deployment environment. One way to test this is to try accessing the Ray dashboard or running a basic Ray script directly from the deployment environment. 6. Update Dependencies: Make sure all dependencies are up to date. Sometimes updating to the latest versions can resolve unidentified issues. Relevant Documentation: - Prefect Installation: Installation Guide - Prefect-Ray Task Runners: Task Runners in Prefect-Ray If these steps don't resolve your issue, you may consider raising a new issue in the Prefect repository or seeking further assistance on the Prefect Slack community.