Notebooks as Controllers

Last updated on 2025-03-10 | Edit this page

Overview

Questions

  • How do you set up and use SageMaker notebooks for machine learning tasks?
  • How can you manage compute resources efficiently using SageMaker’s controller notebook approach?

Objectives

  • Describe how to use SageMaker notebooks for ML workflows.
  • Set up a Jupyter notebook instance as a controller to manage compute tasks.
  • Use SageMaker SDK to launch training and tuning jobs on scalable instances.

Setting up our notebook environment

Amazon SageMaker provides a managed environment to simplify the process of building, training, and deploying machine learning models. In this episode, we’ll set up a SageMaker notebook instance—a Jupyter notebook hosted on AWS for managing SageMaker workflows.

Using the notebook as a controller

In this setup, the notebook instance functions as a controller to manage more resource-intensive compute tasks. By selecting a minimal instance (e.g., ml.t3.medium), you can perform lightweight operations while leveraging the SageMaker Python SDK to launch scalable compute instances for model training, batch processing, and hyperparameter tuning. This approach minimizes costs while accessing the full power of SageMaker for demanding tasks.

We’ll follow these steps to create our first “SageMaker notebook instance”.

  • In the AWS Console, search for SageMaker.
  • Recommended: Select the star icon next to Amazon SageMaker AI to save SageMaker as a bookmark in your AWS toolbar
  • Select Amazon SageMaker AI

2. Create a new notebook instance

  • In the SageMaker left-side menu, click on Notebooks, then click Create notebook instance.
  • Notebook name: To easily track this resource in our shared account, please use the following naming convention: “YourName-ExploreSageMaker”. For example, “DoeJohn-ExploreSageMaker”. Can include hyphens, but not spaces.
  • Instance type: SageMaker notebooks run on AWS EC2 instances. The instance type determines the compute resources allocated to the notebook. Since our notebook will act as a low-resource “controller”, we’ll select a small instance such as ml.t3.medium.
    • This keeps costs low while allowing us to launch separate training/tuning jobs on more powerful instances when needed.
    • For guidance on common instances for ML procedures, refer to our supplemental Instances for ML webpage.
  • Platform identifier: This is an internal AWS setting related to the environment version and underlying platform. You can leave this as the default.
  • Permissions and encryption:
    • IAM role: For this workshop, we have pre-configured the “ml-sagemmaker-use” role to enable access to AWS services like SageMaker, with some restrictions to prevent overuse/misuse of resources. Select the “ml-sagemmaker-use” role. Outside of the workshop, you create/select a role that includes the AmazonSageMakerFullAccess policy.
    • Root access: Determines whether the user can run administrative commands within the notebook instance. You should Enable root access to allow installing additional packages if/when needed.
    • Encryption key (skip): While we won’t use this feature for the workshop, it is possible to specify a KMS key for encrypting data at rest if needed.
  • Network (skip): Networking settings are optional. Configure them if you’re working within a specific VPC or need network customization.
  • Git repositories configuration (skip): You don’t need to complete this configuration. Instead, we’ll run a clone command from our notebook later to get our repo setup. This approach is a common strategy (allowing some flexiblity in which repo you use for the notebook).
  • Tags (NOT OPTIONAL): Adding tags helps track and organize resources for billing and management. This is particularly useful when you need to break down expenses by project, task, or team. To help track costs on our shared account, please use the tags found in the below image.
Tag Setup Example
Tag Setup Example
  • Click Create notebook instance. It may take a few minutes for the instance to start. Once its status is InService, you can open the notebook instance and start coding.

Managing training and tuning with the controller notebook

In the next couple expisodes, we’ll use the SageMaker Python SDK within the notebook to launch compute-heavy tasks on more powerful instances as needed. Examples of tasks to launch include:

  • Training a model: Use the SDK to submit a training job, specifying a higher-powered instance (e.g., ml.p2.xlarge or ml.m5.4xlarge) based on your model’s resource requirements.
  • Hyperparameter tuning: Configure and launch tuning jobs, allowing SageMaker to automatically manage multiple powerful instances for optimal tuning.

This setup allows you to control costs by keeping the notebook instance minimal and only incurring costs for larger instances when they are actively training or tuning models. Detailed guidance on training, tuning, and batch processing will follow in later episodes.

For more details, refer to the SageMaker Python SDK documentation for example code on launching and managing remote training jobs.

Key Points

  • Use a minimal SageMaker notebook instance as a controller to manage larger, resource-intensive tasks.
  • Launch training and tuning jobs on scalable instances using the SageMaker SDK.
  • Tags can help track costs effectively, especially in multi-project or team settings.
  • Use the SageMaker SDK documentation to explore additional options for managing compute resources in AWS.