5/6/2023 0 Comments Jupyterlab vs jupyterhub![]() ![]() The screenshot below shows a managed notebook instance. The Vertex AI Workbench notebook executor feature allows you to schedule notebook runs and save the output to Google Cloud Storage, where it can be shared. You can control compute on a per-notebook level and configure automated shutdown for idle instances. The fully managed Vertex AI Workbench option offers convenient, built-in integrations with Google Cloud Storage and Google Cloud BigQuery, and is easy to integrate with GitHub, all from within your JupyterLab environment. The fully managed option includes extra functionality and integrations. The user-managed Vertex AI Workbench is a simple JupyterLab instance with a choice of kernels. ![]() Vertex AI Workbench is the enterprise edition, which can be either user-managed or fully managed. Google’s Jupyter offering is Vertex AI, a suite of machine learning functionality that includes feature stores, training pipelines, model registries, and endpoints, all available within the Google Cloud Platform (GCP). You will need to set up integrations with version control systems for notebook sharing & collaboration.You will need to manage connections to external datasets.You will need to do some low-level set up and maintenance, including security and authentication.You have full control over configuration and can add any integrations or add-ons you want.You can monitor costs closely and keep costs down by avoiding the extra costs associated with managed notebook services.You are not locked into any particular cloud vendor.JupyterHub supports integrating with any OAuth identity providers such as GitHub, GitLab, and Google and even supports LDAP and Active Directory authentication. One concern you may have about self-hosting JupyterHub is security: You will need to set up HTTPS and authentication (JupyterHub uses PAM authentication by default). Both options can be used with the most common cloud providers or as bare-metal installations. There are two JupyterHub distribution options: The Littlest JupyterHub for small-scale JupyterHub instances and a Kubernetes-based deployment for larger-scale deployments with a hundred or more users. ![]() JupyterHub provides a multi-user platform for Jupyter that your team members can log in to and run notebooks on. Alternatively, if you are willing to host Jupyter in the cloud but you want full control over the configuration, or if you want to ensure that your solution is cloud-agnostic, setting up your own Jupyter installation on cloud infrastructure may be right for you. In some contexts, bare-metal is the right solution: If you work with large scientific datasets that are stored on local infrastructure that has custom software to access it, hosting Jupyter locally may be your best option. Here we take a look at three enterprise notebook options, ranging from an entirely self-managed JupyterHub to fully vendor-managed cloud solutions like AWS Sagemaker & GCP Vertex AI.ĭoing it yourself can mean anything from running Jupyter locally on bare-metal servers to using cloud-based Kubernetes engines. Your professional data science or machine learning team needs a more powerful Jupyter platform, one that is scalable, integrated with your data pipelines, and includes access control.
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