Google Cloud Workbench Notebooks Extension Connects VS Code to Google Cloud's Jupyter Notebooks

Google released a VS Code extension that connects the local IDE to managed Jupyter notebook environments on Google Cloud, enabling developers to run ML/AI workflows without switching between browser-based notebooks and local tools. The open-source extension integrates with BigQuery, Vertex AI, and Cloud Storage, and is available on the Visual Studio Marketplace.

The Google Cloud Workbench Notebooks https://developers.googleblog.com/ml-development-in-vs-code-with-google-cloud-power-workbench-extension-now-available/ extension for VS Code is a new tool that enables developers to connect their local IDE directly to managed Jupyter notebook environments on Google Cloud. Google says the extension is ideal for both data scientists and developers, making experimentation, developing, and scaling ML/AI workflows more seamless. It combines "the familiarity of a local IDE with the heavy-lifting capabilities of the cloud", bringing more fluidity to the overall experience of managing code and cloud-based notebooks by removing the need to switch between browser-based notebooks and the local environment: This integration is specifically designed to streamline the ML lifecycle. By eliminating context switching, developers can move from local experimentation to high-performance cloud compute without disruption. After installing the extension and authenticating with Google Cloud, users can open a .ipynb file, select a project, and run the notebook on a remote Worbench instance directly from the IDE. Image courtesy of Google Google Cloud Workbench Notebooks are managed, cloud-hosted Jupyter notebook environments https://www.infoq.com/jupyter notebooks/ running on Google Cloud that help build, run, and scale data science and machine learning workflows. Besides the infrastructure, Google manages setup and updates and pre-installs common libraries for machine learning, data science, and artificial intelligence. Notebooks are also deeply integrated with other Google Cloud services like BigQuery, Vertex AI https://www.infoq.com/news/2021/06/google-vortex-ai/ , and Cloud Storage. In addition to Google Cloud Workbench Notebooks, developers seeking simple integration of interactive coding and cloud compute can consider alternative offerings from Databricks https://www.databricks.com/product/collaborative-notebooks , DeepNote https://deepnote.com , Kaggle Notebooks https://www.kaggle.com/code , and others. An alternative for developers and data scientists needing a fully- managed, scalable platform for ML/AI development is Amazon SageMaker https://www.infoq.com/news/2022/12/aws-sagemaker-jumpstart/ . It includes several additional components besides remote notebooks, and supports the full ML lifecycle, from data preparation to training, deployment, and monitoring. This makes SageMaker more complex than remote notebooks but also more suited for large-scale production systems. Microsoft Azure also provides the capability of running notebooks on managed compute https://visualstudio.microsoft.com/vs/features/notebooks-at-microsoft/ as well as the more comprehensive Azure Machine Learning service https://azure.microsoft.com/en-us/products/machine-learning . The Google Cloud Workbench Notebooks Extension is open source https://github.com/GoogleCloudPlatform/colab-enterprise-vscode and can be installed from the Visual Studio Marketplace https://marketplace.visualstudio.com/items?itemName=GoogleCloudTools.workbench-notebooks&ssr=false overview .