I need to point out a misunderstanding in your question. There seems to be a conceptual confusion here - Google Cloud Run and Kubernetes (which is what Helm charts are used for) are different deployment platforms. You'll need to choose one or the other: 1. If you want to use Google Cloud Run: - Cloud Run is a serverless platform that doesn't use Helm charts - You would deploy the worker directly as a Cloud Run service - Follow the Prefect GCP integration guide 2. If you want to use Helm charts: - You'll need Google Kubernetes Engine (GKE) instead of Cloud Run - Use the official Prefect worker Helm charts - Follow the Kubernetes deployment guide Would you like me to provide specific instructions for either: 1. Deploying a worker on Cloud Run 2. Deploying a worker on GKE using Helm charts Just let me know which approach you'd prefer, and I'll provide detailed steps for that specific platform.

To configure a Cloud Run work pool to run inside your VPC on Google Cloud, you'll need to use a VPC Connector. Here's how to set it up: 1. First, create a VPC Connector:

bash
   gcloud compute networks vpc-access connectors create [CONNECTOR_NAME] \
       --region=[REGION] \
       --range=[IP_RANGE] \
       --network=[VPC_NAME]

2. Configure the Work Pool: - In the Prefect UI: Create a new Cloud Run work pool - Or via CLI:

bash
   prefect work-pool create my-cloud-run-pool --type cloud-run

3. Add VPC Configuration: - In the work pool settings, go to the "Advanced" tab - Update the job configuration JSON to include the VPC connector in the correct location:

json
   {
     "job_configuration": {
       "command": "{{ command }}",
       "env": "{{ env }}",
       "labels": "{{ labels }}",
       "name": "{{ name }}",
       "region": "{{ region }}",
       "credentials": "{{ credentials }}",
       "job_body": {
         "apiVersion": "<http://run.googleapis.com/v1|run.googleapis.com/v1>",
         "kind": "Job",
         "metadata": {
           "name": "{{ name }}",
           "annotations": {
             "<http://run.googleapis.com/launch-stage|run.googleapis.com/launch-stage>": "BETA"
           }
         },
         "spec": {
           "template": {
             "metadata": {
               "annotations": {
                 "<http://run.googleapis.com/vpc-access-connector|run.googleapis.com/vpc-access-connector>": "{{ vpc_connector_name }}"
               }
             }
           }
         }
       }
     }
   }

4. Set the VPC Connector Name: - In the work pool configuration UI, you can set the VPC connector name either: - In the standard configuration menu - Or in the advanced configuration options Important Notes: - Make sure your VPC connector is in the same region as your Cloud Run service - The VPC connector name should be in the format:

projects/[PROJECT_ID]/locations/[REGION]/connectors/[CONNECTOR_NAME]

- Your service account needs appropriate IAM permissions to use the VPC connector - The VPC connector allows your Cloud Run jobs to access resources in your VPC, such as databases or other internal services This configuration will ensure your Cloud Run workers can securely access resources within your VPC network while maintaining the serverless benefits of Cloud Run.

Yes, you're absolutely right to ask this question. When creating a Cloud Run work pool, you'll need to specify the VPC connector details at creation time. Here's the process: 1. During Work Pool Creation: - In the UI: When creating a new Cloud Run work pool, look for the "Job Configuration" section - There will be a field for

vpc_connector_name

- This should be specified in the format:

projects/[PROJECT_ID]/locations/[REGION]/connectors/[CONNECTOR_NAME]

2. Via CLI:

bash
   prefect work-pool create my-cloud-run-pool \
     --type cloud-run \
     --vpc-connector-name "projects/[PROJECT_ID]/locations/[REGION]/connectors/[CONNECTOR_NAME]"

3. Or using the Base Job Configuration: You can also set this in the base job configuration during creation:

json
   {
     "vpc_connector_name": "projects/[PROJECT_ID]/locations/[REGION]/connectors/[CONNECTOR_NAME]"
   }

Important Notes: - The VPC connector must exist before you create the work pool - The connector must be in the same region where your Cloud Run jobs will run - You can modify the VPC connector settings after creation by updating the work pool configuration - Make sure your service account has the necessary permissions to use the VPC connector Would you like me to provide more details about any of these configuration methods or about the required permissions?

Here's how to modify the Cloud Run work pool configuration to ensure VPC connectivity and restrict to internal IP usage only. You'll need to update the job configuration in the "Advanced" tab of your work pool settings:

json
{
  "job_configuration": {
    "command": "{{ command }}",
    "env": "{{ env }}",
    "labels": "{{ labels }}",
    "name": "{{ name }}",
    "region": "{{ region }}",
    "credentials": "{{ credentials }}",
    "job_body": {
      "apiVersion": "<http://run.googleapis.com/v1|run.googleapis.com/v1>",
      "kind": "Job",
      "metadata": {
        "name": "{{ name }}",
        "annotations": {
          "<http://run.googleapis.com/launch-stage|run.googleapis.com/launch-stage>": "BETA"
        }
      },
      "spec": {
        "template": {
          "metadata": {
            "annotations": {
              "<http://run.googleapis.com/vpc-access-connector|run.googleapis.com/vpc-access-connector>": "{{ vpc_connector_name }}",
              "<http://run.googleapis.com/vpc-access-egress|run.googleapis.com/vpc-access-egress>": "all-traffic",
              "<http://run.googleapis.com/ingress|run.googleapis.com/ingress>": "internal"
            }
          },
          "spec": {
            "containers": [{
              "image": "{{ image }}",
              "env": "{{ env }}",
              "resources": {
                "limits": {
                  "cpu": "{{ cpu }}",
                  "memory": "{{ memory }}"
                }
              }
            }]
          }
        }
      }
    }
  }
}

Key changes and annotations explained: 1.

"<http://run.googleapis.com/vpc-access-connector|run.googleapis.com/vpc-access-connector>": "{{ vpc_connector_name }}"

- Connects the service to your VPC using the specified connector 2.

"<http://run.googleapis.com/vpc-access-egress|run.googleapis.com/vpc-access-egress>": "all-traffic"

- Routes all egress traffic through the VPC - This ensures no direct internet access 3.

"<http://run.googleapis.com/ingress|run.googleapis.com/ingress>": "internal"

- Restricts the service to internal access only - Prevents external IP assignment Important Notes: - Make sure your VPC connector is properly configured before deploying - The service account used needs appropriate IAM permissions - You'll need to configure Private Service Connect or Internal Load Balancing if you need to access this service from within your VPC - Remember to set the

vpc_connector_name

in your work pool configuration using the format:

projects/[PROJECT_ID]/locations/[REGION]/connectors/[CONNECTOR_NAME]

This configuration ensures that your Cloud Run jobs: - Run within your VPC network - Have no external IP addresses - Route all traffic through your VPC - Can only be accessed internally Would you like me to explain any of these settings in more detail or provide information about the required IAM permissions?

Yes, for a self-hosted Prefect server with basic auth, you'll need to set specific environment variables in your Cloud Run worker configuration. Here's how to do it: 1. Required Environment Variables:

bash
PREFECT_API_URL=<https://your-prefect-server-url/api>
PREFECT_API_USERNAME=your-username
PREFECT_API_PASSWORD=your-password

2. Add these to your Work Pool Configuration: In the work pool settings, you can add these environment variables in one of two ways: a. Through the UI: - Go to your work pool settings - Add the environment variables in the "Base Job Configuration" section b. Or in the Advanced configuration:

json
   {
     "job_configuration": {
       "env": [
         {
           "name": "PREFECT_API_URL",
           "value": "<https://your-prefect-server-url/api>"
         },
         {
           "name": "PREFECT_API_USERNAME",
           "value": "your-username"
         },
         {
           "name": "PREFECT_API_PASSWORD",
           "value": "your-password"
         }
       ]
     }
   }

Important Security Notes: - Consider using Google Cloud Secret Manager to store sensitive credentials - If using Secret Manager, you can reference secrets in your environment variables:

json
  {
    "name": "PREFECT_API_PASSWORD",
    "valueFrom": {
      "secretKeyRef": {
        "name": "prefect-password",
        "key": "latest"
      }
    }
  }

- Ensure your VPC and network settings are properly configured to allow the worker to reach your self-hosted Prefect server - Make sure the URL includes the

/api

suffix in the

PREFECT_API_URL

Would you like me to provide more details about setting up Secret Manager integration or any other aspects of the configuration?

Ah, if you're using the standard HTTP Basic Auth format, it's even simpler. You only need to set the

PREFECT_API_URL

with the credentials embedded in the URL format:

bash
PREFECT_API_URL=<https://username:password@your-prefect-server-url/api>

So in your Cloud Run work pool configuration, you would just need:

json
{
  "job_configuration": {
    "env": [
      {
        "name": "PREFECT_API_URL",
        "value": "<https://username:password@your-prefect-server-url/api>"
      }
    ]
  }
}

That's all you need - no separate username and password environment variables required. The basic auth credentials are passed directly in the URL as per HTTP Basic Auth standards. Remember to: - URL encode any special characters in your username or password - Ensure the

/api

suffix is included in the URL - Consider using Secret Manager for the entire URL if you want to keep the credentials more secure

Ah, I see - you're looking to replicate the Helm chart's basic auth configuration for Cloud Run. In this case, you'll need to set the basic auth string directly as an environment variable. Here's how to configure it in your Cloud Run work pool:

json
{
  "job_configuration": {
    "env": [
      {
        "name": "PREFECT_API_URL",
        "value": "<https://your-prefect-server-url/api>"
      },
      {
        "name": "PREFECT_API_AUTH_CREDENTIALS",
        "value": "username:password"
      }
    ]
  }
}

This exactly mirrors the Helm chart's behavior where: -

enabled: true

is handled by setting the auth credentials -

authString: "username:password"

is directly set in

PREFECT_API_AUTH_CREDENTIALS

The worker will use these credentials to authenticate with your self-hosted Prefect server, just like it would in the Kubernetes deployment. Note: Make sure to replace "username:password" with your actual credentials. As before, you might want to consider using Secret Manager for storing these credentials securely.

Ah yes, you're absolutely right! I apologize for the confusion. Since you're using

PREFECT_SERVER_API_AUTH_STRING

on the server side, you should use the same environment variable name for consistency on the worker side. Here's the correct configuration for your Cloud Run work pool:

json
{
  "job_configuration": {
    "env": [
      {
        "name": "PREFECT_API_URL",
        "value": "<https://your-prefect-server-url/api>"
      },
      {
        "name": "PREFECT_SERVER_API_AUTH_STRING",
        "value": "username:password"
      }
    ]
  }
}

This matches exactly with your server configuration, using

PREFECT_SERVER_API_AUTH_STRING

for both server and worker. The worker will now use the same authentication mechanism as configured on your server.