Implement a Custom Vision model as a Docker container

Before we delve into the implementation process, it’s important to understand the key components involved.

• Azure Custom Vision: This component of Azure Cognitive Services enables developers to build and improve Custom Vision models for specific image recognition tasks.
• Docker: Docker is a platform used to develop, ship, and run applications inside containers. A Docker container encapsulates an application along with all its dependencies to ensure it will work uniformly across different environments.

Steps to Implement a Custom Vision model as a Docker Container

1. Train a Custom Vision Model

The first step is to train a Custom Vision model using the Azure Custom Vision service. Azure offers robust tools and APIs that simplify the training process, even if you’re working with complex image categories.

This process involves uploading and tagging images, training the model with these images, and then testing the model to gauge its accuracy.

2. Export the Model

After the model has been trained and tested, the next step is to export it. Azure Custom Vision supports a variety of export formats but for our purpose, we’ll choose the Dockerfile (Linux) format.

3. Build the Docker Image

With the Dockerfile in place, you can now build a Docker image of the model. This is accomplished using the Docker CLI command:

docker build -t mymodel:version .

Replace ‘mymodel:version’ with the name and version you prefer for your image. The ‘.’ at the end signifies that Docker should look for the Dockerfile in the current directory.

4. Run the Docker Container

Once the Docker image has been built, you can run it as a Docker container. The following command can be used:

docker run -p 8080:80 -d mymodel:version

The default port that the model listens on inside the container is 80. The ‘-p 8080:80’ forward requests made to the host at port 8080 to the container at port 80.

Once the container is running, you can make predictions by sending a POST request to ‘http://localhost:8080/image’. The image data should be included in the body of the request, and the content type should be set to ‘application/octet-stream’.

Final Thoughts

Running a Custom Vision model as a Docker container allows for flexibility and portability, and thanks to the power and simplicity of Azure and Docker, it is a relatively straightforward process. Just train your model, export it, build a Docker image, and run it as a container. At that point, your AI solutions can tap into your Custom Vision model, wherever they’re run, thanks to the portability of Docker.

Whether you’re preparing for the AI-102 Designing and Implementing a Microsoft Azure AI Solution exam, or you’re a developer looking to add advanced image recognition capabilities to your applications, these steps should help you get your Custom Vision models running in Docker containers.

Practice Test

True or False: Custom vision models can’t be exported and used as Docker containers.

• True
• False

Answer: False

Explanation: Custom vision models can be exported and used as Docker containers. Microsoft Azure provides an option to export the model for Docker.

Multiple Select: What are the steps needed to implement a custom vision model as a Docker container?

• A. Creating a general internet usage model
• B. Training the Custom Vision model
• C. Exporting the model for Docker
• D. Running the model as a Docker container

Answer: B, C, D

Explanation: To implement a custom vision model as a Docker container, one must train the Custom Vision model, then export the model for Docker, and finally, run the model as a Docker container.

True or False: Docker isn’t compatible with Microsoft Azure.

• True
• False

Answer: False

Explanation: Docker is indeed compatible with Microsoft Azure. Docker containers can be used to host applications, including Custom Vision models on Azure.

Single Select: What language is used to write Dockerfile?

• A. Python
• B. C++
• C. Ruby
• D. None of the above

Answer: D. None of the above

Explanation: Dockerfiles aren’t written in Python, C++ or Ruby. They are written in Docker’s own DSL (Domain Specific Language).

True or False: Implementing a Custom Vision model as a Docker container requires an Azure account.

• True
• False

Answer: True

Explanation: Training and exporting a Custom Vision model requires an Azure account where the model is trained and stored before exporting for Docker.

Multiple Select: In AI-102 exam, what topics related to Docker and Custom Vision might be covered?

• A. How to create Dockerfile
• B. How to train Custom Vision Model
• C. How to use Docker for other cloud platforms
• D. How to implement Custom Vision model as Docker container

Answer: A, B, D

Explanation: AI-102 exam covers Docker and Custom Vision including topics like creating Dockerfiles, training a Custom Vision Model, and implementing it as a Docker container.

True or False: Once you export a Custom Vision model for Docker, it cannot be updated.

• True
• False

Answer: False

Explanation: The Custom Vision model can be updated by retraining the model in the Azure Custom Vision portal and exporting a new Docker image.

Single Select: What is the primary use of Docker in the context of Custom Vision models?

• A. Debugging
• B. Data storage
• C. Training the models
• D. Deploying the models

Answer: D. Deploying the models

Explanation: While Docker may be used in various contexts, in relation to Custom Vision models, Docker containers are primarily used to deploy/export the models.

True or False: Running Custom Vision models in Docker containers can improve the speed and performance.

• True
• False

Answer: True

Explanation: Docker containers can help improve the runtime speed and performance of your application, making your Custom Vision models more efficient.

Single Select: In Azure AI, why might you choose to use Docker to deploy a Custom Vision model?

• A. To allow for easy scaling and distributed computing
• B. For advanced debugging purposes
• C. To reduce the cost of data storage
• D. All of the above

Answer: A. To allow for easy scaling and distributed computing

Explanation: Docker allows for independent and isolated environment for each application, which can greatly benefit distributed computing and scaling. The other options aren’t relevant to deploying a Custom Vision model via Docker.

Interview Questions

What is Azure Custom Vision model?

Azure Custom Vision is an AI service under Azure Cognitive Services that allows developers to easily train custom AI models for image classification and object detection. Custom Vision enables to build, improve, and deploy AI models quickly, even with limited machine learning expertise.

What is a Docker container?

A Docker container is a lightweight, standalone, and executable package that includes everything needed to run a piece of software, including code, runtime, system libraries, and system settings. Containers are isolated from each other and bundle their own software, libraries and configuration files.

Why would you implement a Custom Vision model as a Docker container?

Implementing a Custom Vision model as a Docker container has several advantages. For instance, it offers flexibility of deployment, where the model can be hosted either locally or in the cloud. It also ensures the consistent application behavior across different environments and significantly simplifies the model environment setup process.

How can you export a Custom Vision model for use as a Docker container?

After training the Custom Vision model, it can be exported in the Docker format by selecting the “Export” option from the Custom Vision portal and choosing Docker. After exporting, a compressed file is downloaded which contains the Dockerfile and related scripts to set up the Docker image.

What type of Custom Vision models can be exported?

Both Image Classification and Object Detection Custom Vision models can be exported to be run in a Docker container. However, only compact domain types are exportable.

What does the Dockerfile contain?

The Dockerfile is a text file that contains a set of instructions used to create a Docker image. In the context of Custom Vision model, it includes instructions to create the image with the necessary dependencies, copy the required scripts and model files, and start the application/service.

What are the primary steps to run the Custom Vision model in a Docker container?

The primary steps involve building the Docker image using the provided Dockerfile, then running the Docker container using the created image. This is accomplished using Docker commands like

docker build

and

docker run

.

What are the prerequisites for running Azure Custom Vision model in a Docker container?

The key prerequisites are Docker installed on the host machine and the exported Docker files for the Custom Vision model.

How can users interact with the Custom Vision model running as a Docker container?

Users can interact with the Custom Vision model by sending POST requests to the scoring endpoint exposed by the application running inside the Docker container.

How can you monitor the logs of a Docker container running a Custom Vision model?

You can monitor the logs by using the

docker logs

command followed by the container ID or name.

Can you scale the Custom Vision model running in a Docker container?

Yes, you can scale it using container orchestration platforms like Kubernetes.

What versions of Docker are compatible with Custom Vision exported models?

Docker version 19.03 or later is compatible with the exported Custom Vision models.

Can you update a Custom Vision model running in a Docker container?

Yes, to update, you would need to retrain the model on the Custom Vision portal, re-export, rebuild the Docker image, and then redeploy the container.

Can you run the Custom Vision Docker container on any platform?

Yes, the container can be run on any platform where Docker can be installed. This could be a local machine, a server, or a cloud-based virtual machine.

Can we use GPU acceleration with Docker containers running Custom Vision models?

Yes, we can use GPU acceleration with the compatible Docker containers if the host machine has the necessary GPU capabilities and corresponding drivers are installed. However, the use of GPU must be specified when starting the Docker container using the

--gpus

flag.