Python packaging, dependency management and isolation

General

• Least nerdview guide: Vicki Boykis, Alice in Python projectland.

• Simplest readable guide is python-packaging

• PyPI Quick and Dirty, includes some good tips such as using twine to make it automaticker.

• Open-sourcing a python project the right way.

• Official docs are no longer awful but are slightly stale, and especially perfunctory for compilation

• Kenneth Reitz shows rather than tells with a heavily documented setup.py

• Try Zed Shaw’s signature aggressively cynical and reasonably practical explanation of project structure, with bonus explication of how you should expect much time wasting yak shaving like this if you want to do software.

  • Or copy pyskel.
  • Or generate a project structure with cookiecutter.

primordial time

In the past-times there were other python packageing systems. Distutils and what-not. AFAICT, unless I am migrating extremely old code I should ignore everything about this.

pip

The default python package installer. It is best spelled

python -m pip install package_name

To snapshot dependencies:

python -m pip freeze > requirements.txt

To restore dependencies:

python -m pip install -r requirements.txt

Pro tip: pipx:

pip is a general-purpose package installer for both libraries and apps with no environment isolation. pipx is made specifically for application installation, as it adds isolation yet still makes the apps available in your shell: pipx creates an isolated environment for each application and its associated packages.

That is, pipx is a global application that installs global applications for you. (There is a bootstrapping problem here; How to install pipx itself.)

pip has a heavy cache overhead. If disk space is at a premium, I invoke it as pip --no-cache-dir.

Anaconda

The distribution you use if you want to teach a course in numerical python without dicking around with a 5 hour install process. Many things that are (or were?) painful to install by pip are painless via conda.

bash Miniconda3-latest-Linux-x86_64.sh
# login/logout here
# or do something like `exec bash -` if you are fancy
# Less aggressive conda
conda config --set auto_activate_base false
# conda for fish users
conda init fish

curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh
bash Mambaforge-$(uname)-$(uname -m).sh

conda install pytables=3.2
conda install pyqt=4

source (conda info --root)/etc/fish/conf.d/conda.fish

conda init fish

conda install nb_conda_kernels

conda env export > environment.yml

conda env create --file environment.yml

conda clean --all --yes

conda config --add pkgs_dirs /SOME/OTHER/PATH/.conda

chmod a-rwx ~/.conda

conda create -n pynomkl python nomkl

conda config --set env_prompt '({name})'
conda env create --prefix ./env/myenv --file environment_linux.yml
conda activate ./env/myenv

conda config --set env_prompt '\({name}\)'

env_prompt: ({name})

Mamba

Mamba is a fully compatible drop-in replacement for conda. It was started in 2019 by Wolf Vollprecht. It can be installed as a conda package with the command conda install mamba -c conda-forge.

The introductory blog post is an enlightening read, which also explains conda better than conda explains itself.

See mamba-org/mamba for more.

It explicitly targets package installation for R, and vscode development environments.

Provide a convenient way to install developer tools in VSCode workspaces from conda-forge with micromamba. Get NodeJS, Go, Rust, Python or JupyterLab installed by running a single command.

It also inherits some of the debilities of conda, e.g. that dependencies are platform- and architecture- specific.

venv

venv is now a built-in python virtual environment system in python 3. It doesn’t support python 2 but fixes various problems, e.g. it supports framework python on macOS which is important for GUIs, and is covered by the python docs in the python virtual environment introduction.

# Create venv
python3 -m venv ~/.virtualenvs/learning_gamelan_keras_2
# Use venv from fish OR
source ~/.virtualenvs/learning_gamelan_keras_2/bin/activate.fish
# Use venv from bash
source ~/.virtualenvs/learning_gamelan_keras_2/bin/activate

pyenv

pyenv. which eases and automates switching between all the other pythons and python environments created by virtualenv, python.org python, os python, anaconda python etc. Manages python and thus implicitly can be used as a manager for all the other managers. The new new hipness, at least for platforms other than windows.

BUT WHO MANAGES THE VIRTUALENV MANAGER MANAGER? Also, what is going on? Logan Jones explains:

• pyenv manages multiple versions of Python itself.
• virtualenv/venv manages virtual environments for a specific Python version.
• pyenv-virtualenv manages virtual environments for across varying versions of Python.

Anyway, pyenv compiles a custom version of python and as such is extremely isolated from everything else. Here is an introduction with emphasis on my area: Intro to Pyenv for Machine Learning.

Of course, because this is a python packaging solution, it immediately becomes complicated and confusing when you try to interact with the rest of the ecosystem, e.g.,

pyenv-virtualenvwrapper is different from pyenv-virtualenv, which provides extended commands like pyenv virtualenv 3.4.1 project_name to directly help out with managing virtualenvs. pyenv-virtualenvwrapper helps in interacting with virtualenvwrapper, but pyenv-virtualenv provides more convenient commands, where virtualenvs are first-class pyenv versions, that can be (de)activated. That’s to say, pyenv and virtualenvwrapper are still separated while pyenv-virtualenv is a nice combination.

Huh. I am already too bored to think. However, I did nut out a command which installed a pyenv tensorflow with an isolated virtualenv:

brew install pyenv pyenv-virtualenv
pyenv install 3.8.6
pyenv virtualenv 3.8.6 tf2.4
pyenv activate tf2.4
pip install --upgrade pip wheel
pip install 'tensorflow-probability>=0.12' 'tensorflow<2.5' jupyter

For fish shell you need to add some special lines to config.fish:

set -x PYENV_ROOT $HOME/.pyenv
set -x PATH $PYENV_ROOT/bin $PATH
## fish <3.1
# status --is-interactive; and . (pyenv init -|psub)
# status --is-interactive; and . (pyenv virtualenv-init -|psub)
## fish >=3.1
status --is-interactive; and pyenv init - | source
status --is-interactive; and pyenv virtualenv-init - | source

Poetry

No! wait! The new new new hipness is poetry. All the other previous hipnesses were not the real eternal ultimate hipness that transcends time. I know we said this every previous time, but this time its real and our love will last forever.

Poetry is a tool for dependency management and packaging in Python. It allows you to declare the libraries your project depends on and it will manage (install/update) them for you.

From the introduction:

Packaging systems and dependency management in Python are rather convoluted and hard to understand for newcomers. Even for seasoned developers it might be cumbersome at times to create all files needed in a Python project: setup.py, requirements.txt, setup.cfg, MANIFEST.in and the newly added Pipfile.

So I wanted a tool that would limit everything to a single configuration file to do: dependency management, packaging and publishing.

It takes inspiration in tools that exist in other languages, like composer (PHP) or cargo (Rust).

And, finally, I started poetry to bring another exhaustive dependency resolver to the Python community apart from Conda’s.

What about Pipenv?

In short: I do not like the CLI it provides, or some of the decisions made, and I think we can make a better and more intuitive one.

curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py | python -

OK, so poetry could be regarded as a similar thing to pipenv, in that it (per default, but not necessarily) manages the local dependencies in a local venv. I list it separately because it has a much more full-service approach. For example, it has its own dependency resolver, which makes use of modern dependency metadata but also will work with previous dependency specifications by brute force if needed. It separates specified dependencies from the ones that it actually resolves in practice, which means that the dependencies seem to transport much better than conda. In practice the many small conveniences and very thoughtful workflow are helpful. For example, it will set up the current package for developing per default so that imports work as similarly as possible across this local environment and when it is distributed to users.

Generic dependency managers

i.e. non-python-specific. See dependency managers.