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Phase out notice

Nebari is evolving. The original Nebari project is now called Nebari Classic, and is in maintenance mode.

We'll continue to provide security updates and key bug fixes; however, no new features are planned at this time. We're using the lessons learned from Nebari Classic to build a new Nebari ecosystem of tools, on a more robust foundation with a modular architecture. Nebari Classic's maintenance window will remain open until the new architecture reaches stability and feature parity, and current users are able to migrate, expected through the end of 2026.

See the current Nebari documentation for details on the new platform.

Nebari Deployment Python Environment Setup

To configure and deploy the Nebari platform, you'll first need to set up your python environment.

Nebari configuration and deployment is highly dependent on the version of the Nebari CLI that you have active when you run nebari commands. A such, we highly recommend installing Conda for managing isolated Python environments, especially when working on more than one Nebari deployment from the same machine or using Nebari extensions.

Once installed, you can use manage environments in different ways.

This is a simple way to set up a new Conda environment named nebari0 with a specific Nebari version and exactly one extension installed. This nebari0 environment will store all of our packages, including those installed with pip because when pip is installed into an active conda environment, packages which are then installed with pip will not be saved in the global namespace. This especially critical when working with extensions, because nebari will detect and apply any extensions installed in your Python environment when it runs.

conda create -n nebari0
conda activate nebari0
conda install python==3.12 pip # or python version of your choice
pip install nebari==2024.1.1
# Example plugin that can also be installed
pip install nebari-plugin-self-registration==0.0.9