1. Containerized Model Deployment with Docker


    Containerized model deployment typically involves packaging your machine learning model and the application or service that serves predictions using the model into a container. Docker is the most widely used tool for creating and managing containers since it provides a consistent environment for the application to run, regardless of where the Docker container is deployed.

    To deploy a containerized model using Pulumi and Docker, you will need to:

    1. Define your application: This involves writing the application which will serve the model predictions, usually through a web service interface like a REST API.
    2. Dockerize your application: Create a Dockerfile that defines the steps to package the application and its dependencies into a Docker image.
    3. Build the Docker image: Use Pulumi's Docker provider to build the Docker image using the Dockerfile.
    4. Run the container: Deploy a container instance from the built image to your local machine or any Docker-compatible cloud service.

    The following is a step-by-step guide to deploying a dockerized model, including a Pulumi program written in Python which demonstrates steps 3 and 4.

    import pulumi import pulumi_docker as docker # This program assumes that you have a Dockerfile in the same directory where the Pulumi program is executed. # The Dockerfile should be prepared to copy your model and application code, install any dependencies, # and set up the application's entry point. app_name = "model-serving-app" # Build and publish the Docker image using the local Dockerfile and context. # Replace `./app` with the path to your application's directory containing the Dockerfile and source code. image = docker.Image(app_name, build=docker.DockerBuild(context="./app"), image_name=f"{app_name}:v1.0.0", skip_push=True) # Set to `False` if you want to push the image to a remote registry. # Run the Docker container using the built image. container = docker.Container("model-serving-container", image=image.base_image_name, ports=[docker.ContainerPortArgs( internal=8080, # The port that your application listens on inside the container. external=8080 # The port that you want to expose on your host machine. )]) # Export the Docker container ID and the public URL at which the application can be accessed. pulumi.export("container_id", container.id) pulumi.export("url", f"http://localhost:8080")


    1. We import the necessary Pulumi and Pulumi Docker packages.
    2. We define app_name, which will be used for naming the Docker image and container.
    3. We use docker.Image to define a Docker image resource. The build parameter points to the application directory (./app) which contains the Dockerfile and source code.
      • image_name is the tag for the built image.
      • skip_push is set to True to prevent pushing the image to a remote registry (set this to False and provide proper registry credentials if you wish to push it).
    4. Next, we create a Docker container using docker.Container. We reference the built image and specify the application's internal and external ports.
    5. Finally, we export the container's ID and the URL where the application will be accessible. This URL assumes that the application is running on localhost, which would be the case if deploying to your local machine.

    Please ensure you have Docker installed on your local machine and that the Pulumi CLI is set up and configured. If you need to deploy to a cloud environment, ensure you're logged into the respective cloud provider's CLI and that you've set the appropriate Pulumi stack configuration.