Install a software package
In Nuvolos, almost all applications are equipped with conda environments and we also let our users load user-written packages and extensions.

Basic provisions

All Nuvolos applications come with a set of useful *nix applications pre-installed:
    build-essential (includes make)
    xterm for GUI based applications for terminal emulation
    Nuvolos data connectors for supported languages

The conda environment

Except for R, all Nuvolos applications come equipped with the package manager conda, and more recent applications alias conda to a faster drop-in replacement called mamba. Conda is a non-language specific package manager which lets you install language-specific packages and system libraries as a non-root user. For the Python language, most packages available via pip can also be installed via conda.
Always try to install software with conda first and keep pip as a last option.
We also recommend passing the '--freeze-installed' flag when installing with conda, to ensure the minimal possible changes to the conda environment.
If you cannot self-service your packages, contact us at [email protected] and we will help you.
As an example, suppose you want to install imagemagick and gifsicle for mass editing gifs. The following command will install this to the conda environment of your application:
conda install --freeze-installed gifsicle imagemagick
When distributing and snapshotting an application, the contents of the conda environment are also impacted. This is a key feature for reproducibility.

Tips and tricks

Single-purpose applications

We strongly suggest creating single-purpose applications.
This practice has the following benefits:
    Conda or R package environments remain monolithic and fairly lightweight.
    Distribution and snapshotting takes less storage and resources and conclude faster.

Create a new application instead of upgrading

If you are contemplating doing a major version update on your application, we suggest creating a new app in the same instance and starting there from scratch.
This practice has the following benefits:
    Conda environments can break after major updates.
    The reproducibility of your work may suffer - however it is trivial to maintain two monolithic and separate application structures in parallel, even in the same instance!
    Distribution is based on filesystem-differences and after-upgrade distributions may become less stable due to the massive number of changes occurring on the filesystem.

Known issues

There are no current known issues. Please do not hesitate to reach out to us if you see anything out of the ordinary.
Last modified 7mo ago