If you’d like a set of custom packages to be installed and ready to go when you (or anyone else) launches a session in your project, you can take advantage of Renku’s code based environments.

With Renku code based environments, you can point Renku to a code repository that contains an environment definition file, such as a environment.yml, requirements.txt, or pyproject.toml, and Renku will build a custom environment for your session for you!

This guide has 2 parts:

• First, we will walk through what kinds of files you can use to define code based environments in RenkuLab.
• Second, we’ll show you how to create a code-based environment for your project.

What kinds of environment definitions are supported?

RenkuLab’s code-based environments currently supports creating Python environments. Support for more languages is coming soon!

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Do you need to install R packages in your Renku session? See R.

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Defining a Python Environment

There are multiple ways you can define a python environment for your Renku session:

• Miniconda (environment.yml) (recommended)
• Pip (requirements.txt)
• Poetry (pyproject.toml)

See below for more details on how to use each of these systems.

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If you’d like to learn more about the system Renku uses to create python environments, check out https://paketo.io/docs/howto/python/#use-a-package-manager.

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Miniconda (environment.yml) (recommended)

Include an environment.yml file located at the root (top level) of the code repository.

• Here’s an example environment.yml: