Based on the research and my experience, this error typically occurs due to an architecture mismatch in your container environment. Here are the most likely causes and solutions: 1. Architecture Mismatch: The most common cause is that your Docker image architecture doesn't match the architecture of the VertexAI infrastructure. This happens when: - You're using an ARM-based image on an x86_64 infrastructure (or vice versa) - The Prefect binary in your container was compiled for a different architecture 2. Solution Steps: a. Check Your Dockerfile: Ensure you're using the correct base image for the target architecture. For VertexAI, you typically want to use x86_64/amd64 architecture. Add this to your Dockerfile:

dockerfile
   FROM python:3.9-slim  # or whatever version you're using, but make sure it's x86_64

b. Verify Image Architecture: You can check your local image architecture with:

bash
   docker inspect your-image-name | grep "Architecture"

c. Build for the Correct Platform: If you're building on an ARM machine (like M1/M2 Mac), explicitly specify the platform:

bash
   docker build --platform linux/amd64 -t your-image-name .

3. Additional Checks: - Ensure the Prefect installation in your container is working correctly - Verify permissions on the

/usr/local/bin/prefect

file - Make sure your Python environment is properly set up in the container 4. Debug Steps: - Try running

file /usr/local/bin/prefect

inside your container to check the binary type - Check if all dependencies are properly installed - Verify that your container has the necessary permissions to execute files Would you like me to help you troubleshoot any specific part of this issue? It would be helpful if you could share: 1. The contents of your Dockerfile 2. The platform you're building your container on 3. The exact VertexAI work pool configuration you're using

This is a particularly common issue when: 1. Building on Apple Silicon (M1/M2) Macs and deploying to cloud services 2. Working with cloud services that use x86_64/amd64 architecture (which is most of them) For future reference, here's a quick cheat sheet for avoiding this issue:

bash
# When building your Docker image, always specify the platform:
docker build --platform linux/amd64 -t your-image-name .

# Or if you're using docker-compose:
services:
  your-service:
    platform: linux/amd64
    build: .

And in your Dockerfile, you can also be explicit about the platform:

dockerfile
FROM --platform=linux/amd64 python:3.9-slim  # or your preferred base image

Is there anything else you'd like to know about handling architecture differences in your Prefect deployments on VertexAI?

Based on the research and given that you're using Cloud Run with VPC networking, here are several potential causes and solutions for the WebSocket connection issue: 1. Cloud Run WebSocket Configuration - Cloud Run needs specific configuration to support WebSocket connections - Ensure your Cloud Run service is configured with:

--use-http2
     --allow-unauthenticated (if applicable)

2. URL Configuration - The Prefect API URL must be correctly formatted and accessible - For Cloud Run, it should look like:

<https://your-service-name-hash.a.run.app/api>

- Check your PREFECT_API_URL environment variable in your VertexAI job configuration:

bash
     PREFECT_API_URL=<https://your-cloudrun-service-url/api>

3. VPC Network Configuration - Even with VPC network configured, Cloud Run requires proper ingress settings - Check that: - The VPC connector is properly configured for both services - The VPC has the necessary firewall rules to allow traffic - The Cloud Run service is configured to use the VPC connector - Internal traffic is allowed between services 4. Authentication - Verify your PREFECT_API_KEY is correctly set in your VertexAI job - Ensure the API key has the necessary permissions 5. Debugging Steps:

bash
   # Test connectivity from within your job
   curl -v ${PREFECT_API_URL}

   # Check if the WebSocket endpoint is accessible
   curl -v "${PREFECT_API_URL}/ws/api"

6. Cloud Run Service Configuration: - Make sure your Cloud Run service is configured to accept all traffic - Check if you need to enable HTTP/2 and WebSocket support - Verify the service URL is correct and accessible To help debug this further, could you please provide: 1. The exact PREFECT_API_URL you're using (with sensitive parts redacted) 2. The Cloud Run service configuration (especially networking settings) 3. Any error messages from the Prefect server logs in Cloud Run Also, as a quick test, you could try temporarily making the Cloud Run service public (if it isn't already) to verify if it's specifically a VPC networking issue or a more general configuration problem.

Based on my knowledge and research: No, VertexAI custom jobs do NOT require HTTP/2 to communicate with the Prefect server. The WebSocket connection from VertexAI to Prefect server works fine with HTTP/1.1. The key requirements for VertexAI to Prefect server communication are: 1. WebSocket Support: The connection uses WebSocket protocol, which works over both HTTP/1.1 and HTTP/2 2. Proper URL Configuration: Correct PREFECT_API_URL pointing to your Cloud Run service 3. Network Access: Appropriate networking (VPC/firewall rules) allowing the connection 4. Authentication: Valid PREFECT_API_KEY The HTTP/2 setting is more relevant for the Cloud Run side when you want to: - Handle multiple concurrent connections more efficiently - Use server push features - Optimize header compression Since you're seeing a 404 error, this suggests the issue is more likely related to: 1. Incorrect URL configuration 2. Missing or incorrect API endpoint 3. Routing/networking issues Would you like me to help you troubleshoot the specific 404 error? It would be helpful to see: 1. The PREFECT_API_URL you're using (with sensitive parts redacted) 2. The Cloud Run service URL structure