Note: In this workshop, we’ll be using an Amazon SageMaker notebook instance for simplicity and convenience. You can use any local client to perform steps detailed in this and subsequent sections. You’ll just need to make sure you have the right privileges to access AWS services such as SageMaker, EKS, S3, ECR and others from your clinet. You’ll also need to install AWS Command Line Interface (AWS CLI), python, boto3 and SageMaker SDK installed. The SageMaker Jupyter notebook on the other hand is preconfigured and ready to use.
Open the AWS Management Console
Note: This workshop has been tested on the US West (Oregon) (us-west-2) region. Make sure that you see Oregon on the top right hand corner of your AWS Management Console. If you see a different region, click the dropdown menu and select US West (Oregon)
In the AWS Console search bar, type SageMaker and select Amazon SageMaker to open the service console.
Click on Notebook Instances
From the Amazon SageMaker > Notebook instances page, select Create notebook instance.
In the Notebook instance name text box, enter a name for the notebook instance.
To create an IAM role, from the IAM role drop-down list, select Create a new role. In the Create an IAM role dialog box, select Any S3 bucket. After that select Select Create role. Amazon SageMaker creates the AmazonSageMaker-ExecutionRole-* ** role.
Keep the default settings for the other options and click Create notebook instance. On the Notebook instances section you should see the status change from Pending -> InService
While the notebook instance spins up, continue to work on the next section, and we’ll come back and launch the instance when it’s ready.