base

Online Prediction with xgboost

Overview

In this tutorial, we go over the steps to deploy a model trained with xgboost to AI-Platform. We will then use the deployed model to make some predictions. Optionally, we will delete the resources created in this sample.

Prerequisites

• Complete the training tutorial.
• Change the directory to this sample and run python setup.py install.

Sample Structure

• scripts directory: command-line scripts to train the model locally or on AI Platform.
  • deploy.sh creates a model resource, and a model version for the newly trained model.
  • cleanup.sh deletes all the resources created in this tutorial.
• prediction contains the Python sample code to invoke the model for prediction.
  • predict.py invokes the model for making predictions.
• setup.py: installs all the required Python packages for this tutorial.

Running the Sample

After you go over the steps in the prerequisites section, you are ready to run the sample code. Here are the steps you need to take:

1. Run source ./scripts/deploy.sh. This script will create a Model Resource in AI-Platform. It will then deploy the model object which was created in the training sample as a new model with an assigned model version.

2. Once the model is deployed, you may use it to make predictions - run python ./prediction/predict.py. This sample code sends a prediction request for two instances and prints the result to the console.

3. You may want to delete the resources you created in this tutorial -- run source ./scripts/cleanup.sh which deletes the model version and resouce, and also the model object from GCS.