Azure Functions are a great option if you want to deploy your model in a serverless manner and forget about autoscaling and want to keep costs to a minimum (per-second billing and a free tier of 1 Million requests). The only drawbacks are that the first request to your endpoint will take more time but consecutive requests will be fast. This is due to the cold start issue and you can read the official docs to learn more about it and possible fixes.

Using BentoML and bentoctl, you can enjoy the flexibility of Azure Functions with your favourite ML frameworks and easily manage your 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
• Configuration Options

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

1. Azure CLI - An active Azure account configured on the machine with Azure CLI installed and configured
   • Install instruction: https://docs.microsoft.com/en-us/cli/azure/install-azure-cli (Version >= 2.6.0)
   • Configure Azure account instruction: https://docs.microsoft.com/en-us/cli/azure/authenticate-azure-cli
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 Azure Function operator and its dependencies. The Operator contains the Terraform templates and sets up the registries reqired to deploy to Azure.

   bentoctl operator install azure-functions

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: azure-functions
   template: terraform
   spec:
       resource_group: quickstart
       acr_name: quickstartjj
       min_instances: 1
       max_burst: 2
       premium_plan_sku: P1v2
       
   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 Azure provider and sets up the terraform folders.

      terraform init

   2. Apply terraform project to create Azure Function 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 .url.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

• resrouce_group: Resource group into which the resources have to be created.
• acr_name: The name of Azure Container Registry to use to store images.
• min_instances: The number of workers for the app.
• max_burst: The maximum number of elastic workers for the app
• premium_plan_sku: The SKU of the app service plan. Allowed values: P1v2, P2v2, P3v2. See the link for more info: https://docs.microsoft.com/en-us/azure/azure-functions/functions-premium-plan