venv-cli - A CLI tool to create and manage virtual python environments.

Overview

venv-cli is a CLI tool to help create and manage virtual python environments. It is built on pip and python -m venv, and so only requires packages that are already part of the core python installation; no third-party python packages required. This alleviates the bootstrapping problem of needing to install a python package using your system python and pip before you are able to create virtual environments.

You also don't need conda, pyenv, pythonz etc. to manage your python versions. Just make sure the correct version of python is installed on your system, then reference that specific version when creating the virtual environment, and everything just works. No shims, no path hacks, just the official python build.

Installation

Clone this repository, then run the install.sh script:

$ ./install.sh

This will install the venv source file, along with an uninstall script, in /usr/local/share/venv/, and add a line in the appropriate shell rc-file (e.g. ~/.bashrc) sourcing the venv source script.

The default shell is bash. To install for a different shell, specify the shell name, e.g.

$ ./install.sh zsh

The installation makes the venv command available in your terminal. To check if it works, restart the terminal and run

$ venv --version
venv-cli 1.0.0

Uninstall

To uninstall venv and remove all files, run the uninstall script placed at /usr/local/share/venv/:

$ bash /usr/local/share/venv/uninstall.sh

The script should be run by the user that ran the install script to correctly remove the sourcing lines from the rc-file of that user. However, since it also cleans up the files in /usr/share/local/venv/, it will ask for sudo access.

Usage

To see the help menu, along with a list of available commands, run venv -h/--help.

In the following sections it is assumed that the working directory is the folder ~/project.

Create virtual environment

To create a virtual environment, use the command

$ venv create <python-version>

e.g.

$ venv create 3.9

This creates a virtual environment using the python3.9 executable on $PATH. The name of the virtual environment will be the name of the current folder (in this case, project ), unless you specify a name when creating the environment:

$ venv create 3.9 venv-name

If you don't have the specific version of python installed yet, you can get it by running

$ sudo apt install python<version>

e.g.

$ sudo apt install python3.10

(or in the case of Debian-based distributions, like Ubuntu, sudo apt install python3.10-venv. The -venv part is necessary to be able to use the system python to create virtual environments.)

Activating and deactivating the virtual environment

To activate the virtual environment, place yourself in the folder containing the .venv folder, then run

$ venv activate

To deactivate it again, run

$ venv deactivate

Install/uninstall packages and requirements

To install a single package, simply run

$ venv install <package>

This will install the <package> in the current environment. However, it does more than that. A main design philosophy of venv-cli is to always keep the current environment in a reproducible state. For this reason, venv-cli aims to always keep a requirements file up to date with that state.

This means that when running venv install <package>, the package is first added (or appended) to a requirements.txt file in the current folder, and then the command venv install -r requirements.txt is run, which clears the entire environment and reinstalls it from scratch using the requirements specified in requirements.txt.

Unlike pip install <package>, which leaves no trace, this ensures that the requirements.txt keeps a record of the packages that have been manually installed.

In the same spirit, venv uninstall <package> first removes the package from requirements.txt, then runs venv install -r requirements.txt to reinstall the environment from scratch. Unlike pip uninstall <package>, this ensures that the uninstall does not leave any "orphaned" packages in the current environment (packages that were installed as secondary dependencies, but are no longer needed since the primary dependency has been uninstalled).

Requirements files

To specify a different requirements file to install to/uninstall from, use -r <requirements> :

$ venv install numpy 'pandas >= 2.0' -r core.txt

This will add numpy and pandas >= 2.0 as requirements in core.txt, then install from that file. Similarly,

$ venv uninstall pandas -r core.txt

will remove the pandas >= 2.0 requirement from core.txt again, then reinstall the environment using the updated core.txt.

Lock files

When installing or uninstalling packages, the resulting environment is locked into a corresponding .lock-file, e.g. running venv install -r requirements.txt will lock the installed packages into requirements.lock¹.

This file is useful if a reproducible install is needed, e.g. when deploying a project to a different machine, or when running a colleagues project. Where requrements.txt is used to specify the packages and version your project needs (and nothing more), installing from requirements.lock makes sure that you get the exact version of every package.

Additional requirements

If you have both production and development package requirements, keep them in separate requirements-files, e.g. requirements.txt for production requirements and test.txt for requirements needed when running tests. An example of these could be:

# requirements.txt
numpy
pandas ~= 1.5


# test.txt
-r requirements.txt
pytest
pytest-cov

You can then use either

$ venv install -r requirements.txt

To install production requirements only, or

$ venv install -r test.txt

to install both production and test requirements. The -r requirements.txt in test.txt is what makes sure that installing test requirements also installs the requirements from requirements.txt.

Clearing the environment

If you want to manually clear the environment, you can run

$ venv clear

This will uninstall all installed packages from the environment. This is useful if you have installed development packages, and then need to get back to a production environment, e.g.

$ venv install dev-requirements.txt

# Later
$ venv clear
$ venv install requirements.txt

Deleting the environment

To completely delete the virtual environment and everything in it, run

$ venv delete

(this will not delete any requirement or .lock-files). This will ask for confirmation before deleting the virtual environment. To give immediate confirmation, pass the -y flag:

$ venv delete -y

Contributing

Before creating a pull request, please open an issue first to discuss what you would like to change.

To contribute, clone the repo and create a branch, create a virtual environment and install dev-requirements.txt. When you are done with your changes, run the test suite with

$ pytest .

then create a pull request for the develop branch.

Every (public) subcommand has its own test file tests/test_venv_<command>.py Please make sure to add/update tests as appropriate.

Git Flow

This project follows the Git Flow branching model. The default development branch is accordingly named develop, and the branch main is reserved for tagged releases and hotfixes. Other branches should be named according to their purpose:

feature/<branch name>
bugfix/<branch name>
release/<branch name>
hotfix/<branch name>
support/<branch name>

Releases are made by creating a branch release/vX.X.X from develop, where X.X.X represents the release version number, following SemVer. This freezes the version number of this release, and no more features from develop should be merged into this release (only bugfix/). When the release/ branch is ready for release, merge it into main and tag the commit with the release version, then merge the release/ branch back into develop (resolving any merge conflicts) to get the bugfixes included there as well.

License

MIT

Footnotes

1. A current limitation of using pip freeze under the hood is that installing packages from a version control system (VCS) URL that requires authentication, e.g. private_package @ git+https://USERNAME:[email protected]/my-user/private-package, the authentication is not locked (see https://github.com/pypa/pip/issues/12365). These credentials can either be inserted manually into the generated .lock-file, or the credentials can instead be stored in a .netrc file, which pip install will then reference when running pip install: https://pip.pypa.io/en/stable/topics/authentication/#netrc-support ↩