jupyterhub vs jupyterlab: What are the differences?

Introduction

JupyterHub and JupyterLab are both popular tools in the Jupyter ecosystem, providing interactive computing environments for data science and computational research. While they share a similar purpose, there are several key differences between the two.

1. JupyterHub: Scalable Multi-User Environment

JupyterHub is a multi-user server that allows multiple users to access Jupyter notebooks simultaneously. It provides a central hub where users can log in and create their own isolated notebook servers. Each user gets a separate environment with the ability to install and run their own packages and libraries. JupyterHub is designed for collaborative work and can scale to support hundreds or even thousands of users.

2. JupyterLab: Next-Generation User Interface

JupyterLab, on the other hand, is a next-generation user interface for Jupyter notebooks. It provides a more modern and versatile environment compared to the classic Jupyter Notebook interface. JupyterLab allows users to arrange multiple notebooks, code consoles, and other components in a flexible and customizable layout. It also supports a wide range of file formats and offers features like drag-and-drop functionality, code and file editing, and rich media display.

3. JupyterHub: User Authentication and Authorization

JupyterHub offers built-in user authentication and authorization mechanisms. It integrates with popular authentication providers like GitHub, Google, and LDAP, allowing users to log in using their existing credentials. JupyterHub provides fine-grained access control, allowing administrators to define user permissions and restrict access to specific resources. This makes it suitable for organizations and institutions that require secure and controlled access to Jupyter notebooks.

4. JupyterLab: Enhanced Code Editing and Navigation

JupyterLab provides an enhanced code editing and navigation experience compared to JupyterHub. It offers features like code completion, syntax highlighting, and cell-based editing, making it easier to write and edit code. JupyterLab also supports advanced navigation and search capabilities, allowing users to quickly navigate through large notebooks and find specific code snippets or sections.

5. JupyterHub: Manage and Monitor Multiple Servers

JupyterHub allows administrators to manage and monitor multiple notebook servers from a central dashboard. They can start, stop, and restart servers, view resource usage, and monitor server activity. JupyterHub also provides tools for managing user accounts, groups, and permissions, making it easier to manage a large number of users and notebooks.

6. JupyterLab: Extensibility and Customization

JupyterLab is designed to be highly extensible and customizable. It offers a flexible plugin system that allows users to add new functionality and customize the user interface to suit their needs. Users can develop and share their own JupyterLab extensions, which can range from simple UI customizations to complex data processing and visualization tools. This extensibility makes JupyterLab a versatile platform that can be tailored to different use cases and workflows.

In summary, JupyterHub is a scalable multi-user environment with built-in authentication and access control, while JupyterLab is a next-generation user interface with enhanced code editing and extensibility. JupyterHub focuses on collaborative work and server management, while JupyterLab offers a flexible and customizable environment for individual users.