automatic-model-tuning-ex.md

Example: Hyperparameter Tuning Job

This example shows how to create a new notebook for configuring and launching a hyperparameter tuning job. The tuning job uses the XGBoost Algorithm to train a model to predict whether a customer will enroll for a term deposit at a bank after being contacted by phone.

You use the low-level SDK for Python (Boto3) to configure and launch the hyperparameter tuning job, and the AWS Management Console to monitor the status of hyperparameter tuning jobs. You can also use the Amazon SageMaker high-level Amazon SageMaker Python SDK to configure, run, monitor, and analyze hyperparameter tuning jobs. For more information, see https://github.com/aws/sagemaker-python-sdk.

Prerequisites

To run the code in this example, you need

• An AWS account and an administrator user
• An Amazon S3 bucket for storing your training dataset and the model artifacts created during training
• A running SageMaker notebook instance

Topics

• Prerequisites
• Create a Notebook Instance
• Get the Amazon SageMaker Boto 3 Client
• Get the SageMaker Execution Role
• Specify a S3 Bucket to Upload Training Datasets and Store Output Data
• Download, Prepare, and Upload Training Data
• Configure and Launch a Hyperparameter Tuning Job
• Clean up