{0xc00044b600 0xc0004cf0c0} Launch a SageMaker notebook instance :: Distributed training with Amazon SageMaker / Amazon EKS Workshop

Launch a SageMaker notebook instance

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.

Launch an Amazon SageMaker notebook instance

  • 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. SageMaker Console

  • Click on Notebook Instances Launch notebook instance 1

  • From the Amazon SageMaker > Notebook instances page, select Create notebook instance. Launch notebook instance 2

  • In the Notebook instance name text box, enter a name for the notebook instance.

    • For this workshop select “tfworld2019” as the instance name
    • Choose ml.c5.xlarge. We’ll only be using this instance to launch jobs. The training job themselves will run either on a SageMaker managed cluster or an Amazon EKS cluster
    • Volume size 50 - this is only needed for building docker containers. During training data is copied directly from Amazon S3 to the training cluster when using SageMaker. When using Amazon EKS, we’ll setup an FSx for lustre file system that worker nodes will use to get access to training data. Fill 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. iam

  • 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.