Deploying .NET Machine Learning Models With ML.NET, ASP.NET Core, Docker and Azure Container Instances
This is a sample solution of how machine learning models built with ML.NET framework can be exposed to clients via an ASP.NET Core Web API that has been packaged into a Docker container and deployed to Azure Container Instances service. A more detailed walk-through can be found in this blog post at the following link
Requirements
Docker
Azure CLI
.NET Core 2.0
Docker Hub Account
Project Instructions
- Build The Project
- Create Model
- Test Web API Locally
- Package Web API
- Test Web API Docker Image Locally
- Push Docker Image to Docker Hub
- Deploy to Azure Container Instances
- Test Deployed Application
1. Build The Solution
Clone Repository
git clone https://github.com/lqdev/mlnetacidemo.git
Build Solution
cd mlnetacidemo
dotnet restore
dotnet build
2. Create Model
Before we start our API, we need to create our machine learning model. This is done via the model
project.
dotnet run -p model/model.csproj
3. Test Web API Locally
The model
project should have created a file called model.zip
inside of its directory. Copy model.zip
file inside the model
project directory to the api
project directory.
Start Web API
dotnet run -p api/api.csproj
Send Test Request
Use POSTMAN or Insomnia REST Clients to send a POST
request to http://localhost:5000/api/predict
that includes the following body
{
"SepalLength": 3.3,
"SepalWidth": 1.6,
"PetalLength": 0.2,
"PetalWidth": 5.1,
}
4. Package Web API
Using the Docker CLI
tool, and your own Docker Hub username and image name of your choice, the following command will create a Docker image of the application using the Dockerfile
in the root solution directory.
docker build -t <DOCKERUSERNAME>/<IMAGENAME>:latest .
5. Test Web API Docker Image Locally
Start Docker Image
Using your DockerHub username and recently created image name from the last example, enter the following command to start the Docker image locally and bind it to port 5000.
docker run -p 5000:80 <DOCKERUSERNAME>/<IMAGENAME>:latest
Send Test Request
Use POSTMAN or Insomnia REST Clients to send a POST
request to http://localhost:5000/api/predict
that includes the following body
{
"SepalLength": 3.3,
"SepalWidth": 1.6,
"PetalLength": 0.2,
"PetalWidth": 5.1,
}
To stop the container, use Ctrl + C
6. Push Docker Image to Docker Hub
To make our Docker image accessible to everyone else and the Azure Container Instance service, we need to push it to Docker Hub. This can be done with the following command
docker login
docker push <DOCKERUSERNAME>/<IMAGENAME>:latest
7. Deploy to Azure Container Instances
Prepare Manifest File
The azuredeploy.json
file helps define the deployment configurations for the application. However, in order to specify your Docker image as the one that will be deployed, the containerimage
property needs to be replaced with your Docker Hub username and name of the image you just pushed to Docker Hub.
Deploy Application
Login
az login
Create Resource Group
az group create --name mlnetacidemogroup --location eastus
Deploy
az group deployment create --resource-group mlnetacidemogroup --template-file azuredeploy.json
8. Test Deployed Application
Give it a few minutes for your deployment to initialize. If the deployment was successful, you should see some output on the command line. Look for the ContainerIPv4Address property. This is the IP Address where your container is accessible. In POSTMAN or Insomnia, replace the URL to which you previously made a POST
request to with http://<ContainerIPv4Address>/api/predict
where ContainerIPv4Address is the value that was returned to the command line after the deployment. If successful, the response should be just like previous requests Iris-virginica.
Once you’re finished, you can clean up resources with the following command:
az group delete --name mlnetacidemogroup