Plese see the latest BentoML documentation on OCI-container based deployment workflow: https://docs.bentoml.com/

Google Compute Engine offers you a secure and costomizable compute service that lets you create and run virutal machines on Google's infrastructure. You can choose from a wide range of CPU, GPU and memory configurations to meet the needs of your models. With the combination of BentoML and bentoctl, you can easily deploy models created with your favourite ML frameworks and easily manage the infrastructure via terraform.

Note: This operator is compatible with BentoML version 1.0.0 and above. For older versions, please switch to the branch pre-v1.0 and follow the instructions in the README.md.

• Quickstart with bentoctl
• Configuration Options

This quickstart will walk you through deploying a bento into Google Compute Engine. Make sure to go through the prerequisites section and follow the instructions to set everything up.

1. Google cloud CLI tool - Install instruction: https://cloud.google.com/sdk/docs/install and make sure all your gcloud components are up to date. Run gcloud components update to update
2. Terraform - Terraform is a tool for building, configuring, and managing infrastructure. Installation instruction: www.terraform.io/downloads
3. Docker - Install instruction: https://docs.docker.com/install
4. A working bento - for this guide, we will use the iris-classifier bento from the BentoML quickstart guide.

1. Install bentoctl via pip

   pip install --pre bentoctl

2. Install the operator

   Bentoctl will install the official Google Compute Engine operator and its dependencies. The Operator contains the Terraform templates and sets up the registries reqired to deploy to GCP.

   bentoctl operator install google-compute-engine

3. Initialize deployment with bentoctl

   Follow the interactive guide to initialize the deployment project.

   $ bentoctl init
   
   Bentoctl Interactive Deployment Config Builder
   
   Welcome! You are now in interactive mode.
   
   This mode will help you set up the deployment_config.yaml file required for
   deployment. Fill out the appropriate values for the fields.
   
   (deployment config will be saved to: ./deployment_config.yaml)
   
   api_version: v1
   name: quickstart
   operator: google-compute-engine
   template: terraform
   spec:
       project_id: bentoml-316710
       zone: us-central1-a
       machine_type: n1-standard-1
   filename for deployment_config [deployment_config.yaml]:
   deployment config generated to: deployment_config.yaml
   ✨ generated template files.
     - ./main.tf
     - ./bentoctl.tfvars

   This will also run the bentoctl generate command for you and will generate the main.tf terraform file, which specifies the resources to be created and the bentoctl.tfvars file which contains the values for the variables used in the main.tf file.

4. Build and push docker image into Google Container Registry.

   bentoctl build -b iris_classifier:latest -f deployment_config.yaml

   The iris-classifier service is now built and pushed into the container registry and the required terraform files have been created. Now we can use terraform to perform the deployment.

5. Apply Deployment with Terraform

   1. Initialize terraform project. This installs the Google Cloud provider and sets up the terraform folders.

      terraform init

   2. Apply terraform project to create compute engine deployment

      terraform apply -var-file=bentoctl.tfvars -auto-approve

6. Test deployed endpoint

   The iris_classifier uses the /classify endpoint for receiving requests so the full URL for the classifier will be in the form {EndpointUrl}/classify.

   URL=$(terraform output -json | jq -r .Endpoint.value)/classify
   curl -i \
     --header "Content-Type: application/json" \
     --request POST \
     --data '[5.1, 3.5, 1.4, 0.2]' \
     $URL

7. Delete deployment Use the bentoctl destroy command to remove the registry and the deployment

   bentoctl destroy -f deployment_config.yaml

• project_id: The project ID for the GCP project you want to deploy to. Make sure the VM intances API is activated.
• zone: The zone to which you want to deploy to. To get the complete list of available zones run gcloud compute zones list
• machine_type: This specifies the machine type you want to use. You can use different machine types based on the resource requirments of your model. To get a list of all the machine types available run gcloud compute machine-types list