Amazon SageMaker Studio Lab
Learn and experiment with ML using a no-setup, free development environment
Free machine learning development environment that provides the compute, storage, and security to learn and experiment with ML
Get started with a valid email address—no need to configure infrastructure or manage identity and access or even sign up for an AWS account
GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security—all at no cost—for anyone to learn and experiment with ML. All you need to get started is a valid email address—you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later.
How it works
No AWS Account Needed
To get started with SageMaker Studio Lab, use your email address to register for an account on studiolab.sagemaker.aws. Your SageMaker Studio Lab account is separate from an AWS account and doesn’t require a credit card.
Choose compute power
SageMaker Studio Lab offers either CPU or GPU sessions for your project. You can choose to run notebooks with a 12-hour CPU session for complex algorithms, or a 4-hour GPU session for deep learning (DL) architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). There’s no limit to the number of compute sessions you can run—after a session ends, you can start a new one.
SageMaker Studio Lab provides persistent sessions with 15 GB of free long-term storage, so you can save your work and pick up where you left off. When a session ends, your work is automatically saved in dedicated storage.
Prepackaged ML frameworks
Choose the best Python package manager for your project, such as Pip, Conda, or Mamba. By default, SageMaker Studio Lab supports the Terminal and Git command lines and GitHub integration for collaboration. Setup is fast and easy, with no configuration required to run a Jupyter Notebook.