NotebookCreation

Creating a Notebook Instance

We'll start by creating an Amazon S3 bucket that may be used in certain modules of the workshop. Next, we'll create a SageMaker notebook instance for running the Jupyter notebooks used in this workshop.

1. Create a S3 Bucket

SageMaker typically uses S3 as storage for data and model artifacts. In this step you'll create a S3 bucket for this purpose. To begin, sign into the AWS Management Console, https://console.aws.amazon.com/.

High-Level Instructions

Use the console or AWS CLI to create an Amazon S3 bucket (see step-by-step instructions below if you are unfamiliar with this process). Keep in mind that your bucket's name must be globally unique across all regions and customers. We recommend using a name like smworkshop-firstname-lastname. If you get an error that your bucket name already exists, try adding additional numbers or characters until you find an unused name.

Step-by-step instructions (expand for details)

1. In the AWS Management Console, choose Services then select S3 under Storage.

2. Choose +Create Bucket

3. Provide a globally unique name for your bucket such as smworkshop-firstname-lastname.

4. Select the Region you've chosen to use for this workshop from the dropdown.

5. Choose Create in the lower left of the dialog without selecting a bucket to copy settings from.

2. Launching the Notebook Instance

1. Make sure you are on the AWS Management Console home page. As shown below, in the Search for services search box, type SageMaker. The search result list will populate with Amazon SageMaker, which you should now click. This will bring you to the Amazon SageMaker console homepage.

2. In the upper-right corner of the AWS Management Console, confirm you are in the desired AWS region. Select N. Virginia, Oregon, Ohio, or Ireland (or any other region where SageMaker is available).

3. To create a new notebook instance, click the Notebook instances link on the left side, and click the Create notebook instance button in the upper right corner of the browser window.

4. Type smworkshop-[First Name]-[Last Name] into the Notebook instance name text box, and select ml.m5.xlarge for the Notebook instance type.

5. In the Permissions and encryption section, choose Create a new role in the IAM role drop down menu. Leave the defaults in the pop-up modal, as shown below. Click Create role.

6. As shown below, go to the Git repositories section, click the Repository drop down menu, select Clone a Git repository to this notebook instance only, and enter the following for Git repository URL: https://github.com/awslabs/amazon-sagemaker-workshop.git

7. Click Create notebook instance at the bottom.

3. Accessing the Notebook Instance

1. Wait for the server status to change to InService. This will take several minutes, possibly up to ten but likely much less.

2. Click Open Jupyter. You will now see the Jupyter homepage for your notebook instance.