Almost every kind of software needs some form of installation instructions that tell users how to load your code onto their machine.

You might be used to software that is time-consuming and tedious to install. Perhaps you’ve created such software yourself. But we will assert: in the modern technology ecosystem, every piece of software can and should be easy to install. In most cases, your installation instructions can be just a few sentences, and need not live on their own page.

The catch is that making your software easy to install might require a fair amount of effort on your part — especially if you’re not familiar with modern software packaging systems. We contend, however, that investing time in mastering these systems is one of the best ways you can spend your time as a developer. If you find yourself writing a long, complex set of installation instructions, that’s a sign that you need to set off on a side quest: there’s some kind of engineering work that has been left undone.

Example: Basic Python Package

If your software is a typical Python package, your installation instructions could be as short as these from ionize:

Installation

One-line install using pip:

pip install ionize

In many cases, there’s no need to write anything more than this. You could consider mentioning whether your package is also available via conda or other tools, but you can expect that anyone who needs it will try running conda install ionize whether you write it down explicitly or not.

Virtually every modern language (even Fortran!) has a preferred package manager and registry supporting comparably easy installs.

Package Manager Integration

If you want to be able to provide your users with one-line installation instructions, you need to do some work on the backend to integrate your software with the appropriate package manager and registry. This can be a bit of a journey if it’s your first time undertaking such integration.

It’s beyond the scope of One Good Tutorial to tell you how to do this for your particular software. But no matter what language you’re using, these workflows are generally thoroughly documented and any problems you run into are overwhelmingly likely to have Google-able answers.

Python Hassles

Python packaging can be a bit of a pain because while there is a single consensus Python package registry (PyPI), there isn’t a single Python project management tool: different projects might use setuptools, scikit-build, poetry, pdm, uv, or even something else. All of these have their own particular workflows. Fortunately, while these tools lead to diversity on the developer side (making packages and uploading them to the registry), things are always consistent on the user side: pip install will work.

If you need to choose which one of these Python project management tools to use, there is unlikely to be a single best answer. As of this writing (2025), uv seems to be the darling of the moment, but that could easily change. If you have the time to comparison-shop, you might as well see if one particular tool strikes your fancy the most. If not, there’s nothing wrong with sticking with what you know, or what a colleague knows, or what’s used by another piece of software that you’re familiar with.

Example: Conda Package with Conda-Forge

The package managers that we’ve been talking about are generally strongly tied to a single programming language. Some software might be built using multiple languages, or need to be installed into software environments that rely on multiple languages. In these “polyglot” cases, language-specific package managers might not be able to accomplish what you need.

These situations used to be tricky to handle. Not anymore. These days, the Conda package ecosystem has established itself the dominant framework for easily installing polyglot software, enabling one-line install instructions even for very complex applications:

Installation

The easiest way to install {Your Software} is through the Conda package manager with conda-forge:

conda install -c conda-forge {your-package-name}

To make your software installable with Conda, someone needs to package it into Conda’s specific file format and publish those packages. Conda-forge is an impressive, fully open-source project that automates the building and publishing of tens of thousands such packages. It is almost surely the most convenient way to get your software into the Conda ecosystem, unless a more field-specific framework like Bioconda would be appropriate.

Learning how to make Conda packages might feel like a bit of a pain, but it remains extremely worthwhile if a language-specific package manager won’t meet your needs.

Case Study: daschlab

There’s a risk if you tell users to go ahead and conda install mypackage: if your package is a complex application with many dependencies, and they’re installing your software into some kind of global kitchen-sink environment, there might be conflicts that make your package uninstallable, or even break their environment altogether.

The daschlab visualization and analysis package integrates tightly with JupyterLab, which is often finicky to install. So its installation instructions guide users towards installing it into its own environment, using a web-hosted requirements.txt file to avoid a download step:

We strongly recommend installing daschlab and its dependencies using the popular Conda package management system in conjunction with the conda-forge package suite. If you need help setting up Conda or conda-forge, see the official conda installation instructions and how to start using conda-forge packages. There are numerous other tutorials available on the general internet as well.

Once these systems are set up on your computer, you can install daschlab by following running the following command in a terminal:

conda create --name daschlab --file https://github.com/pkgw/daschlab/releases/latest/download/daschlab-conda-requirements.txt

This command will produce a fair amount of output, but should not report any severe errors. Once the installation is complete, you can launch a new daschlab session as follows:

1. If needed, activate the daschlab environment with the command

   conda activate daschlab
   

2. Launch JupyterLab with the command

   jupyter lab
   

Applications with pixi

Somewhat annoyingly, the Conda ecosystem has suffered a bit of the fragmentation seen in Python packaging: you could also recommend that people install your software using mamba, or pixi. As in the Python case, these all pull from the same registries, but the different tools have different strengths.

If in doubt, you can’t go wrong recommending good old conda. But the pixi tool is worth calling out because it has very good isolation features not available in the other tools. (It’s also very easy to install and quite fast.)

Instead of the named environments as used by conda (e.g., --name daschlab as seen above), Pixi is workspace-based: each installed environment is associated with a directory on disk containing a pixi.toml file. All of Pixi’s environment configuration is declarative, so that you could, for instance, manage this file using version control, and anyone tracking your changes would have their installed environment automatically evolve to track exactly what your file specifies.

Pixi also provides a pack command that can even generate self-extracting shell scripts that set up an entire, self-contained, conda-based environment from a single source file. This could be useful for airgapped deployment scenarios.