Amazon SageMaker makes it easy and accessible to build machine learning models using the familiar Jupyter Notebook interface. Given the prominence of Jupyter Notebooks in the data science and machine learning workflow, Jupyter Lab is the next generation user-face of Jupyter notebook files. All notebook files are fully supported within the Lab interface. This tutorial will detail how to access Jupyter Lab on the Amazon SageMaker platform. However, note that Jupyter Lab is currently in beta so more features (such as real-time collaboration) and improvements are still under development.
Accessing Jupyter Lab in Amazon SageMaker
Jupyter Lab is easily accessible on the Amazon SageMaker platform. First, start your SageMaker instance on the management console. The platform is currently only available in Tokyo, North Virginia, Ohio, Ireland, and Oregon (as of June 2018) so you will have to change your region to one of the available regions. After some time, open the notebook interface inside your browser; you will see the familiar Jupyter notebook interface start up on the home directory. It only takes one step to change the user interface – simply replace the ‘tree’ at the end of the URL with ‘lab’.
3 Reasons to Use Jupyter Lab:
Below we list three of our favourite features of Jupyter Lab in no particular order. For more details and features, visit the user documentation at http://jupyterlab.readthedocs.io/en/latest/.
A dark theme is now built into the interface. We also expect custom themes and extensions from the community in the near future.
Multiple Notebooks and Windows
Having multiple notebooks snapped to a bash terminal or python console facilitates a modular environment that is entirely customisable to the user or task.
Live Markdown Editor
A live markdown editor is included in Jupyter Lab, enabling users to preview their edits in real time.