Fully Managed D-Wave / Google QBoost on AWS SageMaker

This example shows how to package a QBoost algorithm for use with SageMaker. I have chosen a simple QBoost implementation made by D-Wave, based on the algorithm developed by Google, to illustrate the procedure.

SageMaker supports two execution modes: training where the algorithm uses input data to train a new model and serving where the algorithm accepts HTTP requests and uses the previously trained model to do an inference (also called "scoring", "prediction", or "transformation").

The algorithm that we have built here supports both training and scoring in SageMaker with the same container image. It is perfectly reasonable to build an algorithm that supports only training or scoring as well as to build an algorithm that has separate container images for training and scoring.

In order to build a production grade inference server into the container, we use the following stack to make the implementer's job simple:

1. nginx is a light-weight layer that handles the incoming HTTP requests and manages the I/O in and out of the container efficiently.
2. gunicorn is a WSGI pre-forking worker server that runs multiple copies of your application and load balances between them.
3. flask is a simple web framework used in the inference app that you write. It lets you respond to call on the /ping and /invocations endpoints without having to write much code.

The Structure of the Sample Code

The components are as follows:

• Dockerfile: The Dockerfile describes how the image is built and what it contains. It is a recipe for your container and gives you tremendous flexibility to construct almost any execution environment you can imagine. Here. we use the Dockerfile to describe a pretty standard python science stack and the simple scripts that we're going to add to it. See the Dockerfile reference for what's possible here.

• build_and_push.sh: The script to build the Docker image (using the Dockerfile above) and push it to the Amazon EC2 Container Registry (ECR) so that it can be deployed to SageMaker. Specify the name of the image as the argument to this script. The script will generate a full name for the repository in your account and your configured AWS region. If this ECR repository doesn't exist, the script will create it.

• qboost: The directory that contains the application to run in the container. See the next session for details about each of the files.

The application run inside the container

When SageMaker starts a container, it will invoke the container with an argument of either train or serve. We have set this container up so that the argument in treated as the command that the container executes. When training, it will run the train program included and, when serving, it will run the serve program.

• train: The main program for training the model. When you build your own algorithm, you'll edit this to include your training code. In this example we will connect to the D-Wave machine to perform the training.
• serve: The wrapper that starts the inference server. In most cases, you can use this file as-is.
• wsgi.py: The start up shell for the individual server workers. This only needs to be changed if you changed where predictor.py is located or is named.
• predictor.py: The algorithm-specific inference server. This is the file that you modify with your own algorithm's code. It will perform the inference of your quantumply trained model in a classical environment.
• nginx.conf: The configuration for the nginx master server that manages the multiple workers.

Environment variables

When you create an inference server, you can control some of Gunicorn's options via environment variables. These can be supplied as part of the CreateModel API call.

Parameter                Environment Variable              Default Value
---------                --------------------              -------------
number of workers        MODEL_SERVER_WORKERS              the number of CPU cores
timeout                  MODEL_SERVER_TIMEOUT              60 seconds

Usage

1. Run the script: create_wisc_datasets.py: this will create the folder data in your local machine, and will generate inside it two files, one for training and one for testing
2. Please get your AWS credentials, AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, and export them as environment variables
3. Run the following commands to build the container and push the image in Amazon ECR: chmod +wrx build_and_push.sh && ./build_and_push.sh qboost-sagemaker-example
4. Once it's done, please open AWS SageMaker and start a Jupyter Notebook instance
5. Follow the instruction in qboost-on-sagemaker.ipynb in this repository

Disclamier

This example is inspired by the official AWS SageMaker tutorial, which explains how to sagemakerize scikit-learn models.