PetSpotR

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PetSpotR allows you to use advanced AI models to report and find lost pets. It is a sample application that uses Azure Machine Learning to train a model to detect pets in images.

It also leverages popular open-source projects such as Dapr and Keda to provide a scalable and resilient architecture.

Featured technologies

• Bicep - Infrastructure as code
• Bicep extensibility Kubernetes provider - Model Kubernetes resources in Bicep
• Azure Kubernetes Service
• Azure Blob Storage
• Azure Cosmos DB
• Azure Service Bus
• Azure Machine Learning
• Hugging Face - AI model community
• Azure Load Testing - Application load testing
• Blazor - Frontend web application
• Python Flask - Backend server
• Dapr - Microservice building blocks
• KEDA - Kubernetes event-driven autoscaling

Architecture

Note: This application is a demo app which is not intended to be used in production. It is intended to demonstrate how to use Azure Machine Learning and other Azure services to build a scalable and resilient application. Use at your own risk.

Local architecture

Cloud architecture

Moving from local to cloud is as simple as swapping out the local Dapr components with their cloud equivalents:

Microservices

PetSpotR is a microservices application that uses Azure Machine Learning to train a model to detect pets in images. It also uses Azure Blob Storage to store images and Azure Cosmos DB to store metadata, leveraging Dapr bindings and state management to abstract away the underlying infrastructure.

The frontend is a .NET Blazor application that allows users to upload images and view the results. The backend is a Python Flask application that uses Azure Machine Learning to train and score the model.

AI model training

The application uses Azure Machine Learning to train a model to detect pets in images. The model is sourced via Hugging Face, which is a community of AI researchers and developers. The model is trained using the Azure Machine Learning Compute Instance, which is a managed compute environment for data scientists and AI developers.

State management

The application uses the Dapr state management API to abstract away the underlying infrastructure. When the application is running locally, the state is stored in a lightweight Redis container. When the application is running in the cloud, the state is stored in Azure Cosmos DB.

Publish & subscribe messaging

The application uses the Dapr pub/sub API to abstract away the underlying infrastructure. When the application is running locally, the messages are sent via a lightweight Redis container. When the application is running in the cloud, the messages are sent via Azure Service Bus.

Bindings

The application uses the Dapr bindings API to abstract away the underlying infrastructure. When the application is running locally, the images are stored in the local filesystem. When the application is running in the cloud, the bindings are stored in Azure Blob Storage.

Scaling

The application scales using KEDA, which allows you to scale based on the number of messages in a queue. The application uses Azure Service Bus to queue messages for the backend to process, leveraging Dapr pub/sub to abstract away the underlying infrastructure.

Load testing

Azure Load Testing is used to simulate a large number of users uploading images to the application, which allows us to test the application's scalability.

Running PetSpotR

Prerequisites

• Azure subscription
  • Note: This application will create Azure resources that will incur costs. You will also need to manually request quota for Standard NCSv3 Family Cluster Dedicated vCPUs for Azure ML workspaces, as the default quota is 0.
• Azure CLI
• Dapr CLI
• Helm
• Docker
• Python 3.8
• Dotnet SDK
• Bicep extensibility

Run in a Codespace

You can run this application in a GitHub Codespace. This is useful for development and testing. The ML training and scoring will run within an Azure Machine Learning workspace in your Azure subscription.

1. Click the Code button and select Open with Codespaces
2. Wait for the Codespace to be created
3. Hit 'F5' or open the Run and Debug tab and select ✅ Debug with Dapr

Run locally

The services in this application can be run locally using the Dapr CLI. This is useful for development and testing. The ML training and scoring will run within an Azure Machine Learning workspace in your Azure subscription.

1. Install the Dapr CLI
2. Initialize Dapr

   dapr init

3. Configure your Dapr images component for Windows or Mac
   1. Open ./iac/dapr/local/images.yaml
   2. Uncomment the appropriate section for your OS, and comment out the other section
4. Deploy the required Azure resources

   az deployment group create --resource-group myrg --template-file ./iac/infra.bicep --parameters mode=dev

5. Run the backend

   cd src/backend
   dapr run --app-id backend --app-port 6002 --components-path ../../iac/dapr/local -- python app.py

6. Run the frontend

   cd src/frontend/PetSpotR
   dapr run --app-id frontend --app-port 5114 --components-path ../../../iac/dapr/local -- dotnet watch

7. Navigate to http://localhost:5114

Deploy to Azure with the CLI

1. Ensure you have access to an Azure subscription and the Azure CLI installed

   az login
   az account set --subscription "My Subscription"

2. Clone this repository

   git clone https://github.com/azure-samples/petspotr.git
   cd petspotr

3. Deploy the infrastructure

   az deployment group deployment create --resource-group myrg --template-file ./iac/infra.json

4. Deploy the configuration

   az deployment group deployment create --resource-group myrg --template-file ./iac/config.json

5. Get AKS credentials

   az aks get-credentials --resource-group myrg --name petspotr

6. Install Helm Charts

   helm repo add dapr https://dapr.github.io/helm-charts/
   helm repo add kedacore https://kedacore.github.io/charts
   helm repo update
   helm upgrade dapr dapr/dapr --install --version=1.10 --namespace dapr-system --create-namespace --wait
   helm upgrade keda kedacore/keda --install --version=2.9.4 --namespace keda --create-namespace --wait

7. Log into Azure Container Registry You can get your registry name from your resource group in the Azure Portal

   az acr login --name myacr

8. Build and push containers

   docker build -t myacr.azurecr.io/backend:latest ./src/backend
   docker build -t myacr.azurecr.io/frontend:latest ./src/frontend
   docker push myacr.azurecr.io/petspotr:latest
   docker push myacr.azurecr.io/frontend:latest

9. Deploy the application

   az deployment group deployment create --resource-group myrg --template-file ./iac/app.json

10. Get your frontend URL

    kubectl get svc

11. Navigate to your frontend URL