You can access the debugger by simply choosing the debugger icon on the notebook toolbar.Īs of this writing, the debugger is available for our newly launched Base Python 2.0 and Data Science 2.0 images, which are Python 3.8 based images, in SageMaker Studio and amazonei_pytorch_latest_p37, pytorch_p38, and tensorflow2_p38 kernels in SageMaker Notebook Instance, with plans to support more in the near future. With the integrated debugger, you can inspect variables and step through breakpoints while you interactively build your data science and ML code.
#Jupyterlab variable inspector install#
The ability to install extensions through pip, Conda, and Mamba.A table of contents panel to more easily navigate notebooks.An integrated debugger with support for breakpoints and variable inspection.The new notebooks come with several features out of the box that improve the SageMaker developer experience, including the following: In this post, we showcase some of the exciting new features built into SageMaker notebooks and call attention to some of our favorite open-source extensions that improve the developer experience when using SageMaker to build, train, and deploy your ML models. AWS is a major contributor to the Jupyter open-source community and we’re happy to bring the latest Jupyter capabilities to our customers.
#Jupyterlab variable inspector code#
The new notebooks provide data scientists and developers a modern IDE complete with developer productivity tools for code authoring, refactoring and debugging, and support for the latest open-source Jupyter extensions. Today, we’re excited to announce that SageMaker Studio and SageMaker Notebook Instance now come with JupyterLab 3 notebooks. The second option is Amazon SageMaker Notebook Instance – a single, fully managed ML compute instance running notebooks in cloud, offering customers more control on their notebook configurations. In addition to creating notebooks, you can perform all the ML development steps to build, train, debug, track, deploy, and monitor your models in a single pane of glass in Studio. You can quickly launch notebooks in Studio, easily dial up or down the underlying compute resources without interrupting your work, and even share your notebook as a link in few simple clicks.
The first option is fast start, collaborative notebooks accessible within Amazon SageMaker Studio – a fully integrated development environment (IDE) for machine learning. Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models.
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