Amazon SageMaker
Developer Guide

Create a Notebook Instance

To create a notebook instance, use either the Amazon SageMaker console or the CreateNotebookInstance API. For an example of using the Amazon SageMaker console to create a notebook instance, see Step 1: Create an Amazon SageMaker Notebook Instance.

After receiving the request, Amazon SageMaker does the following:

  • Creates a network interface—If you choose the optional VPC configuration, it creates the network interface in your VPC. It uses the subnet ID that you provide in the request to determine which Availability Zone to create the subnet in. Amazon SageMaker associates the security group that you provide in the request with the subnet. For more information, see Notebook Instance Security.

  • Launches an ML compute instance—Amazon SageMaker launches an ML compute instance in an Amazon SageMaker VPC. Amazon SageMaker performs the configuration tasks that allow it to manage your notebook instance, and if you specified your VPC, it enables traffic between your VPC and the notebook instance.

  • Installs Anaconda packages and libraries for common deep learning platforms—Amazon SageMaker installs all of the Anaconda packages that are included in the installer. For more information, see Anaconda package list. In addition, Amazon SageMaker installs the TensorFlow and Apache MXNet deep learning libraries.

  • Attaches an ML storage volume—Amazon SageMaker attaches an ML storage volume to the ML compute instance. You can use the volume to clean up the training dataset or to temporarily store other data to work with. Choose any size between 5 GB and 16384 GB, in 1 GB increments, for the volume. The default is 5 GB.


    Only files and data saved within the /home/ec2-user/SageMaker folder persist between notebook instance sessions. Files and data that are saved outside this directory are overwritten when the notebook instance stops and restarts.


    Each notebook instance's /tmp directory provides a minimum of 10 GB of storage in an instant store. An instance store is temporary, block-level storage that isn't persistent. When the instance is stopped or restarted, Amazon SageMaker deletes the directory's contents. This temporary storage is part of the root volume of the notebook instance.

  • Copies example Jupyter notebooks— These Python code examples illustrate model training and hosting exercises using various algorithms and training datasets.