AWS DeepLens
Developer Guide

Set Up the Project Data Store in Amazon S3

For this project, we will use Amazon SageMaker to train the model. To do so, we need to prepare an S3 bucket and four subfolders to store the input needed for the training job and the output produced by the training job.

We will create an Amazon S3 bucket and name it deeplens-sagemaker-models-<my-name>. Fill in your name or some ID in the <my-name> placeholder to make the bucket name unique. In this bucket, we create a headpose folder to hold data for training the model specific for head pose detection.

We then create the following four subfolders under the headpose folder:

  • deeplens-sagemaker-models-<my-name>/headpose/TFartifacts: to store training output, i.e., the trained model artifacts.

  • deeplens-sagemaker-models-<my-name>/headpose/customTFcodes

  • deeplens-sagemaker-models-<my-name>/headpose/datasets: to store training input, the preprocessed images with known head poses.

  • deeplens-sagemaker-models-<my-name>/headpose/testIMs

Follow the steps below to create the bucket and folders using the Amazon S3 console:

  1. Open the Amazon S3 console at

  2. If the Amazon S3 bucket does not exist yet, choose + Create bucket and then type deeplens-sagemaker-models-<my-name> in Bucket name, use the default values for other options, and choose Create. Otherwise, double click the existing bucket name to choose the bucket.

  3. Choose + Create folder, type the headpose as the folder name, and then choose Save.

  4. Choose the headpose folder just created, and then choose + Create folder to create the four subfolders (named TFartifacts, customTFcodes, datasets, testIMs) under headpose, one at time.

                Image: Amazon S3 bucket and folders for the head pose detection tutorial

Next, we need to prepared the input data for training in Amazon SageMaker.