Our mission at AWS is to make machine learning (ML) more accessible. Through many conversations over the past years, I learned about barriers that many ML beginners face. Existing ML environments are often too complex for beginners, or too limited to support modern ML experimentation. Beginners want to quickly start learning and not worry about […]
Our mission at AWS is to make machine learning (ML) more accessible. Through many conversations over the past years, I learned about barriers that many ML beginners face. Existing ML environments are often too complex for beginners, or too limited to support modern ML experimentation. Beginners want to quickly start learning and not worry about spinning up infrastructure, configuring services, or implementing billing alarms to avoid going over budget. This emphasizes another barrier for many people: the need to provide billing and credit card information at sign-up.
What if you could have a predictable and controlled environment for hosting your Jupyter notebooks in which you can’t accidentally run up a big bill? One that doesn’t require billing and credit card information at all at sign-up?
Today, I am extremely happy to announce the public preview of Amazon SageMaker Studio Lab, a free service that enables anyone to learn and experiment with ML without needing an AWS account, credit card, or cloud configuration knowledge.
At AWS, we believe technology has the power to solve the world’s most pressing issues. And, we proudly support the new and innovative ways that our customers are using these technologies to deliver social impacts.
This is why I am also excited to announce the launch of the AWS Disaster Response Hackathon using Amazon SageMaker Studio Lab. The hackathon, starting today and running through February 7, 2022, is a great way to start learning ML while doing good in the world. I will share more details on how to get involved at the end of the post.
Getting Started with Amazon SageMaker Studio Lab
Studio Lab is based on open-source JupyterLab and gives you free access to AWS compute resources to quickly start learning and experimenting with ML. Studio Lab is also simple to set up. In fact, the only configuration you have to do is one click to choose whether you need a CPU or GPU instance for your project. Let me show you.
The first step is to request a free Studio Lab account here.
When your account request is approved, you will receive an email with a link to the Studio Lab account registration page. You can now create your account with your approved email address and set a password and your username. This account is separate from an AWS account and doesn’t require you to provide any billing information.
Once you have created your account and verified your email address, you can sign in to Studio Lab. Now, you can select the compute type for your project. You can choose between 12 hours of CPU or 4 hours of GPU per user session, with an unlimited number of user sessions available to you. Furthermore, you get a minimum of 15 GB of persistent storage per project. When your session expires, Studio Lab will take a snapshot of your environment. This enables you to pick up right where you left off. Let’s select CPU for this demo, and choose Start runtime.
Once the instance is running, select Open project to go to your free Studio Lab environment and start building. No additional configuration is required.
Customize your environment
Studio Lab comes with a Python base image to get you started. The image only has a few libraries pre-installed to save the available space for the frameworks and libraries that you actually need.
You can customize the Conda environment and install additional packages using the
%conda install <package> or
%pip install <package> command right from within your notebook. You can also create entirely new, custom Conda environments, or install open-source JupyterLab and Jupyter Server extensions. For detailed instructions, see the Studio Lab documentation.
Studio Lab is tightly integrated with GitHub and offers full support for the Git command line. This lets you easily clone, copy, and save your projects. Moreover, you can add an Open in Studio Lab badge to the
README.md file or notebooks in your public GitHub repo to share your work with others.
This will let everyone open and view the notebook in Studio Lab. If they have a Studio Lab account, then they can also run the notebook. Add the following markdown to the top of your
README.md file or notebook to add the Open in Studio Lab badge:
[![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/org/repo/blob/master/path/to/notebook.ipynb)
path and the notebook filename with those for your repo. Then, when you click the Open in Studio Lab badge, it will preview the notebook in Studio Lab. If your repo is private within a GitHub account or organization and you would like other people to use it, then you must additionally install the Amazon SageMaker GitHub App at the GitHub account or organization level.
If you have a Studio Lab account, you can click Copy to project and choose to either copy just the notebook or to clone the entire repo into your Studio Lab account. Moreover, Studio Lab can check if the repository contains a Conda environment file and build the custom Conda environment for you.
Learn the Fundamentals of ML
If you are new to ML, then Studio Lab provides access to free, educational content to get you started. Dive into Deep Learning (D2L) is a free interactive book that teaches the ideas, the math, and the code behind ML and DL. The AWS Machine Learning University (MLU) gives you access to the same ML courses used to train Amazon’s own developers on ML. Hugging Face is a large open source community and a hub for pre-trained deep learning (DL) models. This is mainly aimed at natural language processing. In just a few clicks, you can import the relevant notebooks from D2L, MLU, and Hugging Face into your Studio Lab environment.
Join the AWS Disaster Response Hackathon using Amazon SageMaker Studio Lab
The frequency and severity of natural disasters are increasing. This year alone, we have seen significant wildfires across the Western United States and in countries like Greece and Turkey; major floods across Europe; and Hurricane Ida’s impact to the coast of Louisiana. In response, governments, businesses, nonprofits, and international organizations are placing more emphasis on disaster preparedness and response than ever before.
Through the AWS Disaster Response Hackathon offering a total of $54,000 USD in prices, we hope to simulate ways of applying ML to solve pressing challenges in natural disaster preparedness and response.
Join the hackathon today, start building, and don’t forget to submit your project before February 7, 2022. This hackathon is also an attempt to set the Guinness World Record for the “largest machine learning competition.”
Join the Preview
You can request a free Amazon SageMaker Studio Lab account starting today. The number of new account registrations will be limited to ensure a high quality of experience for all customers. You can find sample notebooks in the Studio Lab GitHub repository. Give it a try and let us know your feedback.