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GCP Cloud Run Deployment Example

This guide demonstrates how to deploy a simple reward function to Google Cloud Run using the reward-kit CLI. The example uses a basic hello_world_reward function found in examples/gcp_cloud_run_deployment_example/dummy_rewards.py.

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Overview

Deploying a reward function to GCP Cloud Run allows you to host it as a scalable, serverless HTTP endpoint. The reward-kit deploy command automates much of this process, including containerization and service configuration.

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Files in the Example Directory

Located in examples/gcp_cloud_run_deployment_example/:

• dummy_rewards.py: Contains a basic hello_world_reward function used for this deployment example.
• rewardkit.example.yaml: An example configuration file for reward-kit. This shows the structure for rewardkit.yaml if you choose to use one for GCP settings.

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Prerequisites

1. Google Cloud Platform (GCP) Account: Active GCP account with billing enabled.
2. gcloud CLI: Google Cloud CLI installed and authenticated (gcloud auth login, gcloud auth application-default login).
3. APIs Enabled: Ensure the following APIs are enabled in your GCP project:
   • Cloud Build API
   • Artifact Registry API
   • Cloud Run Admin API
   • Secret Manager API
4. Permissions: The authenticated user/service account for gcloud needs sufficient permissions (e.g., roles like “Cloud Build Editor”, “Artifact Registry Administrator”, “Cloud Run Admin”, “Secret Manager Admin”).
5. reward-kit installed: Ensure reward-kit is installed in your Python environment (e.g., pip install reward-kit).

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Setup

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rewardkit.yaml Configuration (Optional but Recommended)

The reward-kit CLI can pick up GCP settings from a rewardkit.yaml file located in the directory from which you run the reward-kit deploy command.

1. Create rewardkit.yaml: You can copy the examples/gcp_cloud_run_deployment_example/rewardkit.example.yaml to the directory where you intend to run reward-kit deploy (this could be the example directory itself, or your project root). Rename it to rewardkit.yaml.

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   # If in examples/gcp_cloud_run_deployment_example/
   cp rewardkit.example.yaml rewardkit.yaml
   

2. Customize rewardkit.yaml: Open rewardkit.yaml and replace placeholders with your actual GCP Project ID and desired region. Example rewardkit.yaml:

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   gcp_cloud_run:
     project_id: "my-actual-gcp-project-123"
     region: "us-west1"
     # artifact_registry_repository: "my-custom-eval-repo" # Optional
     # default_auth_mode: "api-key" # Optional, defaults to api-key
   evaluator_endpoint_keys: {} # Managed by reward-kit for API key auth
   

Note: If you choose not to use a rewardkit.yaml file, you must provide all necessary GCP parameters (like --gcp-project YOUR_PROJECT_ID, --gcp-region YOUR_REGION) directly in the reward-kit deploy command.

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Deployment Command

It’s recommended to run the deployment command from the directory containing the reward function script (dummy_rewards.py) and your rewardkit.yaml (if used), for example, from examples/gcp_cloud_run_deployment_example/.

1. Ensure your virtual environment is active:
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   source .venv/bin/activate
   

2. Run the deployment command:
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   reward-kit deploy \
       --id my-dummy-gcp-evaluator \
       --target gcp-cloud-run \
       --function-ref dummy_rewards.hello_world_reward \
       --gcp-auth-mode api-key \
       --verbose
       # --force # Add if overwriting an existing evaluator
       # If not using rewardkit.yaml, add required GCP params:
       # --gcp-project YOUR_PROJECT_ID --gcp-region YOUR_REGION
   

Command Explanation:

• --id my-dummy-gcp-evaluator: A unique ID for your evaluator on the Fireworks AI platform.
• --target gcp-cloud-run: Specifies deployment to GCP Cloud Run.
• --function-ref dummy_rewards.hello_world_reward: The Python import path to your reward function. If dummy_rewards.py is in the current directory, this reference works.
• --gcp-auth-mode api-key: Configures the Cloud Run service with API key authentication. reward-kit will generate a key, store it in GCP Secret Manager, and configure the service. The key is also saved to your local rewardkit.yaml under evaluator_endpoint_keys. This is the default if not specified.
• --verbose: Shows detailed output, including gcloud commands being executed.
• --force: (Optional) If an evaluator with the same --id already exists, this flag will delete the existing one before creating the new one.

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Expected Outcome

If successful, reward-kit will:

1. Create an Artifact Registry repository (default: reward-kit-evaluators, or as specified in rewardkit.yaml).
2. Build a Docker container with your reward function and push it to Artifact Registry.
3. If api-key auth is used, create a GCP Secret to store the generated API key.
4. Deploy the container to Cloud Run, configured for the chosen authentication mode.
5. Register the deployed Cloud Run service URL as a remote evaluator with the Fireworks AI platform.

The output will include the Cloud Run service URL and the API key (if newly generated).

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Testing the Deployed Endpoint

You can test the deployed endpoint using curl or reward-kit preview --remote-url <your-cloud-run-url>.

If using curl with API key authentication:

1. Retrieve the API key. It’s printed during deployment and saved in rewardkit.yaml (if one is used in the command’s directory) under evaluator_endpoint_keys: { "my-dummy-gcp-evaluator": "YOUR_KEY" }.
2. Get your Cloud Run service URL from the deployment output.

API_KEY="your_generated_api_key"
SERVICE_URL="your_cloud_run_service_url"

curl -X POST "$SERVICE_URL/evaluate" \
     -H "Content-Type: application/json" \
     -H "X-Api-Key: $API_KEY" \
     -d '{
           "messages": [{"role": "user", "content": "Test"}],
           "kwargs": {}
         }'

This should return a JSON response from your hello_world_reward function.