This project provides set of quickstarts aimed at accelerating GenAI integration and personalization within of live game enviroments using Google Cloud products and open source gaming solutions.
In today's gaming industry, providing a personalized and engaging experience for players is crucial. This project offers game developers a set of quickstart resources to help them integrate GenAI capabilities into their live game environments. By leveraging Google Cloud products and open-source gaming solutions, you can enhance player engagement, unlock new use cases with Generative AI, and create memorable gaming experiences.
NOTE: This is a rapidly evolving repo and is being adapted for a variety of use cases. If you would like to contribute or notice any bugs, please open an issue and/or feel free to submit a PR for review.
If you’re using this project, please ★Star this repository to show your interest!
| Folder | Description |
|---|---|
| terraform | Infrastructure deployment scripts based on Terraform |
| examples | Individual quickstarts that can be tested and deployed based on your use case |
| src | Core source code that is used as part of our quickstarts |
The following steps below will walk you through the setup guide for GenAI Quickstart. The process will walk through enabling the proper Google Cloud APIs, creating the resources via Terraform, and deployment of the Kubernetes manifests needed to run the project.
Note: These steps assume you already have a running project in Google Cloud for which you have IAM permissions to deploy resources into.
git clone https://github.com/googleforgames/GenAI-quickstart.git
cd GenAI-quickstart
Set your unique Project ID for Google Cloud
# To just use your current project
export PROJECT_ID=$(gcloud config list --format 'value(core.project)' 2>/dev/null)
# Otherwise set it to the project you wish to use.
Set default location for Google Cloud
export LOCATION=us-central1
To better follow along with this quickstart guide, set CUR_DIR env variable
export CUR_DIR=$(pwd)
gcloud auth list
Check if your authentication is ok and your PROJECT_ID is valid.
gcloud projects describe ${PROJECT_ID:?}
You should see the your PROJECT_ID listed with an ACTIVE state.
gcloud services enable --project ${PROJECT_ID:?} \
aiplatform.googleapis.com \
artifactregistry.googleapis.com \
cloudbuild.googleapis.com \
cloudresourcemanager.googleapis.com \
compute.googleapis.com \
container.googleapis.com \
containerfilesystem.googleapis.com \
containerregistry.googleapis.com \
iam.googleapis.com \
servicecontrol.googleapis.com \
spanner.googleapis.com
cd ${CUR_DIR:?}/terraform
cat terraform.example.tfvars | sed -e "s:your-unique-project-id:${PROJECT_ID:?}:g" > terraform.tfvars
terraform init
terraform plan
terraform apply
The deployment of cloud resources can take between 5 - 10 minutes. For a detailed view of the resources deployed see README in terraform directory.
After cloud resources have successfully been deployed with Terraform, get newly created GKE cluster credentials.
gcloud container clusters get-credentials genai-quickstart --region us-central1 --project ${PROJECT_ID:?}
Test your Kubernetes client credentials.
kubectl get nodes
Switch to the genai directory and render common templates that use your unique project id.
# Find all files named .template.yaml, replace `your-unique-project-id` with PROJECT_ID, and output to .yaml.
cd ${CUR_DIR:?}/genai && find common -type f -name "*.template.yaml" -exec \
bash -c "template_path={}; sed \"s:your-unique-project-id:${PROJECT_ID:?}:g\" < \${template_path} > \${template_path/%.template.yaml/.yaml} " \;
Build and run GenAI workloads with Skaffold
gcloud auth configure-docker ${LOCATION:?}-docker.pkg.dev
export SKAFFOLD_DEFAULT_REPO=${LOCATION:?}-docker.pkg.dev/${PROJECT_ID:?}/repo-genai-quickstart
cd ${CUR_DIR:?}/genai
# To run all apis and models (requires a GPU node for stable-diffusion)
skaffold run --build-concurrency=0
After workloads are deployed, you can swap to using GPU deployments instead:
# Scale up a 2xL4 Mixtral 8x7B Deployment:
kubectl scale -n genai deployment huggingface-tgi-mixtral-small --replicas=1
# Or scale up a 8xL4 Mixtral 8x7B Deployment:
kubectl scale -n genai deployment huggingface-tgi-mixtral-big --replicas=1
# Scale down CPU Deployment:
kubectl scale -n genai deployment huggingface-tgi-mistral-cpu --replicas=0
# Note that the `huggingface-tgi-api` Service matches all of the huggingface-tgi-*
# Deployments, so if you have multiple replicas running, it will load balance
# between them.
You can also run the individual backends in isolation:
# To run only stable-diffusion (requires a GPU node)
#skaffold run --module stable-diffusion-api-cfg,stable-diffusion-endpt-cfg
# To run only Vertex chat (Vertex AI is required)
#skaffold run --module vertex-chat-api-cfg
Access the API - You can test the application and all the APIs from here :)
The cluster creates an internal passthrough Network Load Balancer (ILB). To access the APIs run:
kubectl port-forward svc/genai-api -n genai 8080:80
then in another window run:
export EXT_IP=localhost:8080
echo "Browse to http://${EXT_IP}/genai_docs to try out the GenAI APIs!"
and then navigate to the URL in your browser.
Test the API using curl:
curl -X 'POST' "http://${EXT_IP}/genai/text" \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{"prompt": "Who are the founders of Google?"}'
Or test the API using the api-caller container inside the cluster:
# See available service endpoints. The `genai` endpoint wraps them all.
kubectl get svc -ngenai
# Start `api-caller` pod interactively
kubectl run -it -ngenai --rm --restart=Never api-caller --image=${SKAFFOLD_DEFAULT_REPO}/api-caller:latest
# Examples:
# See available example scripts
root@api-caller:/app# ls
embeddings.py genai_api.py huggingface_tgi.py npc_chat_api.py stable_diffusion_api.py vertex_chat_api.py vertex_code_api.py vertex_gemini_api.py vertex_image_api.py vertex_text_api.py
# The genai_api script works for text prompts
root@api-caller:/app# python3 genai_api.py --endpoint=http://genai-api/genai/text --prompt "Describe a wombat"
INFO:root:Status Code: 200
INFO:root:Response: "A wombat is a marsupial native to Australia. [...]"
# To try the Smart NPC, first reset the world data:
root@api-caller:/app# python3 npc_chat_api.py --endpoint http://genai-api/genai/npc_chat/reset_world_data --empty
INFO:root:Status Code: 200
INFO:root:Response: {"status":"ok"}
# Then you can use the interactive chat:
root@api-caller:/app# python3 npc_chat_api.py --endpoint http://genai-api/genai/npc_chat --chat
>>> hey, how are you?
<<< I am doing my best here at the distribution center. It's a tough situation, but I am staying focused on helping those in need. How about you? How are you holding up?
# You can also interact with the services underneath, e.g.: Hugging Face TGI supports an interactive chat
root@api-caller:/app# python3 huggingface_tgi.py --endpoint=http://huggingface-tgi-api:8080/v1
>>> hello!
INFO:httpx:HTTP Request: POST http://huggingface-tgi-api:8080/v1/chat/completions "HTTP/1.1 200 OK"
<<< Hello! How can I help you today? If you have any questions or need assistance with something, feel free to ask and I'll do my best to help. If you just want to chat, we can talk about pretty much anything. What's on your mind?
In genai directory
cd ${CUR_DIR:?}/genai
skaffold delete
In terraform directory
cd ${CUR_DIR:?}/terraform
terraform destroy
If you are not running the above project in Google Cloud shell, make sure you are logged in and authenticated with your desired project:
gcloud auth application-default login
gcloud config set project ${PROJECT_ID:?}
and follow the authentication flow.
The entire repo can be cloned and used as-is, or in many cases you may choose to fork this repo and keep the code base that is most useful and relevant for your use case. If you'd like to contribute, then more info can be found witin our contributing guide.