What is jupyterhub and what is it used for
Jupyterhub is a web application to share and manage data science kernels. It allows for simultaneous work on the same project by many users from different computers. Jupyterlab is the next generation of the Jupyter Notebook, and it aims to provide an interactive data science environment. Jupyterlab introduces a new approach to building GUIs for data analysis – the kind of GUI you think of when you hear the word “browser”. It is a web application built on top of a lightweight, persistent, in-memory database. This means Jupyterlab behaves more like a browser than most other GUIs. For example, because it uses web storage instead of cookies, it should remain usable even when you are offline. Jupyterlab also has additional features not available in Jupyterhub. For example, users can work in separate terminal sessions simultaneously by using a password to secure the connection between sessions.
You can install jupyterhub and jupyterlab on Linux (as well as other Unix systems). On Windows you can use the JupyterHub or JupyterLab installer programs, which provide a simpler user interface than installing a full-fledged web server. To use a Linux machine as a machine for running Jupyterlab, you will need to install some additional software. You can install a package called “jupyter” that has a few useful programs like jupyter-notebook and jupyter-web-server . You can also install the package called “jupyterlab”, which provides the web server needed to run Jupyterlab. The version of jupyterlab currently in testing on Fedora 27 is 0.31.0 . You can check on the latest version available for the systems you want to use it on, by checking for updates at the URL https://pypi.python.org/pypi/jupyterlab .
What is jupyterlab and what are its features
Jupyterlab is the next generation of the Jupyter Notebook, and it aims to provide an interactive data science environment. Jupyterlab is a web application built on top of a lightweight, persistent, in-memory database. This means Jupyterlab behaves more like a browser than most other GUIs. For example, because it uses web storage instead of cookies, it should remain usable even when you are offline. Jupyterlab is written in python and uses the web browser as the main user interface.
With Jupyterlab, you can interact with a notebook by running it locally. When you do so, you can see and modify its contents from the local browser window. The notebook is then saved to disk in a format that persists even when your local machine is not connected to the internet. Although the notebook is saved in a persistent way, it is not persistent across local network disconnections. When Jupyterlab is running on your desktop machine, its contents persist through local network disconnections and reconnection.
Jupyterlab works well with an IPython notebook because they share a common codebase and user interface. However you can also run Jupyterlab locally on any system that supports Python or IPython.
Providing a persistent, simple data science platform for Jupyter is an important goal for us. Running Jupyter on an interactive data science environment with persistent data is a core design goal of the project. To achieve this goal, it was necessary to start from scratch and develop a more minimal and focused platform. For example:
How are they different
The main difference between the two tools is the architecture. Jupyterhub acts as a server that runs a number of separate kernels for each user. In practice, this means multiple users can be using different kernels at the same time over a single Jupyterhub server. Jupyter lab, however, is designed to be run locally or remotely on a single machine. It is not multi-user. That said, there are ways to use sklearn clusters with Jupyter lab.
Another difference is how they are packaged and deployed. Jupyterhub can be run as a Docker container, while Jupyter lab is typically run as a Python package.
When it comes to the user interface, there again is a difference in presentations of the notebook documents themselves. Jupyterlab has a tabbed notebook document. Jupyterhub has the notebook documents in a single frame.
Here’s a more detailed list of differences:
Which one should you use
For most people, Jupyterhub is a better choice. Jupyterlab is more expensive than Jupyterhub and does not allow for simultaneous use of multiple kernels. In addition, the configuration required to make use of more than one Jupyter lab configuration file (which allows multiple users to share the same notebook) is cumbersome. Jupyterhub, on the other hand, is a much cheaper and simpler to use.
This is not to say that Jupyterlab has no use. For example, if you are a researcher who needs to run code for multiple projects at once, then Jupyterlab with multiple independent sessions may be a good fit. If you have a big computing task which takes a long time and needs to be split among several machines (e.g. a parallel lilypond performance analysis), then Jupyterlab may be a good fit.
To determine which is most appropriate for your situation, you need to look at the features that each system provides. Both have full IPython functionality: printing, running code in different kernels, running jupyterlab functions from the notebook and so on. In addition, both systems allow for unlimited number of users to work on their own JupyterLab sessions. In JupyterLab, users can also upload and share their own files. Both systems allow for multiple concurrent JupyterLab sessions. Both systems (with some configuration) allow for sharing notebooks between multiple users (JupyterLab call this “Team Mode” and “Network Mode”, respectively). However, there are three notable differences:
How to set up jupyterhub and how to set up jupyterlab
Jupyterhub requires Python 3.5 or later, and Jupyterlab requires Python 3.4 or later. If you use Linux or Mac OS X, installation is simple: just follow the installation instructions in this tutorial . If you are using Windows or have any questions about installation, please refer to this page for more details.
Jupyterlab is a web interface to create, share, and run Python code. Jupyterlab allows you to log into your account on the Jupyterhub server and use the functions available there.
Jupyterhub is a web frontend for interacting with Jupyter Notebooks.
When you set up Jupyterhub you also set up Jupyterlab, so you can access your notebook creations. Most people will find that they only need to enter their username, password and password recovery email once and never have to enter these details again.
The user interface for Jupyterlab is not the same as what is available in a browser, however. It will look different from a browser-based notebook or terminal session. Please see the official documentation for more details.
In Jupyterlab, you can create new notebooks, open existing notebooks that are stored on the system, import new libraries and run code. Jupyterlab is a browser-based application, so you do not need to install any software to use it.