Implement a Custom Vision model as a Docker container

Azure Cognitive Services provides a variety of pre-trained AI models that can be easily incorporated into your applications. However, in some cases, you may want to use a custom model trained specifically for your application’s requirements. Azure Custom Vision allows you to create and train your own vision models tailored to your specific use case. In this article, we will learn how to implement a Custom Vision model as a Docker container.

Prerequisites

To get started, you’ll need an Azure subscription and the Docker runtime installed. You can download and install Docker from the official Docker website (https://docs.docker.com/get-docker/).

Step 1: Create a Custom Vision project

Start by creating a Custom Vision project in the Azure portal. Follow these steps:

1. Sign in to the Azure portal.
2. Click on “Create a resource”.
3. Search for “Custom Vision” and select “Custom Vision” from the results.
4. Click on “Create” to start creating a new Custom Vision project.
5. Provide a unique name for your project.
6. Select the appropriate resource group or create a new one.
7. Choose the project type based on your use case: Classification, Object Detection, or Instance Segmentation.
8. Click on “Create” to create the project.

Step 2: Upload and label training images

Next, upload a dataset of images to train your Custom Vision model. The dataset should contain labeled examples of the objects you want the model to recognize. You can upload images using the web interface or programmatically using the Custom Vision Python SDK.

Step 3: Train your Custom Vision model

Once your training data is ready, train your Custom Vision model by following these steps:

1. In the Azure portal, navigate to your Custom Vision project.
2. Click on the “Train” button to start the training process.
3. Wait for the training to complete. The process may take some time depending on the size of your dataset and the complexity of your model.

Step 4: Export your Custom Vision model

After training, export your Custom Vision model by following these steps:

1. In the Azure portal, navigate to your Custom Vision project.
2. Click on the “Export” button to export your model.
3. Select “Dockerfile” as the export platform.
4. Provide a unique name for your Docker image.
5. Click on “Export” to generate the Dockerfile necessary for containerizing your model.

Step 5: Build and run the Docker container

Now, let’s build the Docker container. Open your terminal or command prompt and navigate to the directory containing the generated Dockerfile. Use the following command to build the container image:

docker build -t .


Replace <image-name> with the name you provided during the export step. This will build the Docker image with your Custom Vision model included.

Once the image is built, use the following command to run the Docker container:

docker run -p 80:80 --name -d


Replace <container-name> with a name for your container. This will run the container locally and map port 80 to the container’s port 80.

Step 6: Test your Custom Vision model

Now that your Docker container is running, it’s time to test the model. You can send HTTP requests to the container to get predictions using the REST API.

If you’re using a tool like cURL, you can execute the following command to test a sample image:

curl -X POST -H "Content-Type: application/json" -d '{"url": ""}' http://localhost:80/v1.0/predictions


Replace <image-url> with the URL of an image you want to test. The REST API will return the predictions made by your Custom Vision model.

Step 7: Deploy your Docker container

Once you’re satisfied with the performance of your Custom Vision model, you can deploy the Docker container to a cloud environment like Azure Kubernetes Service (AKS) or Azure Container Instances (ACI). This will allow your model to scale and handle production workloads.

In the case of AKS, you can use Kubernetes deployment YAML files to deploy your container to a cluster. For ACI, you can use the Azure CLI or Azure portal to deploy your container to a container instance.

That’s it! You’ve successfully implemented your Custom Vision model as a Docker container. You can now leverage the power of Azure AI to recognize and classify objects in your custom applications.

Remember, Azure Custom Vision allows you to continuously improve your model’s performance by retraining it with new data. So make sure to periodically update and retrain your Custom Vision model for accurate results.

Start exploring the possibilities of Azure Cognitive Services and unleash the potential of AI in your applications!