Py Packages

This tool manages Python installations and dependencies. It implements PEP 582 -- Python local packages directory and Pep 518 -- Specifying Minimum Build System Requirements for Python Projects

It keeps the latter isolated in the project directory, and runs Python in an environment which uses this directory. Per PEP 582, dependencies are stored in the project directory → __pypackages__ → 3.7(etc) → lib.

Goal: Make using and publishing Python projects as simple as possible. Understanding Python environments shoudn't be required to use dependencies safely.

Only works with Python ≥ 3.4. You don't need Python installed to use it; it will install the specified version of Python if not already installed.

Installation

There are 2 ways to install:

• Download a binary from the releases page. Installers are available for Debian/Ubuntu, and Windows. On Debian or Ubuntu, download and run this deb. On Windows, download and run this installer. Alternatively, download the appropriate binary (ie pypackage.exe or pypackage) and place it somewhere accessible by the system path. For example, /usr/bin in linux, or ~\AppData\Local\Programs\Python\Python37\bin in Windows.

• If you have Rust installed, the most convenient way is to run cargo install pypackage.

Quickstart

• (Optional) Run pypackage init in an existing project folder, or pypackage new projname to create a new project folder. init imports data from requirements.txt or Pipfile; new creates a folder with the basics

• Run pypackage install to set up Python, and sync dependencies with pyproject.toml, or add dependencies to it

• Run pypackage python to run Python

Why add another Python manager?

Pipenv and Poetry both address part of this problem. Some reasons why this tool is different:

• Its dependency resolution and locking is faster due to using a cached database of dependencies, vice downloading and checking each package, or relying on the incomplete data available on the pypi warehouse.

• By not using Python to install or run, it remains intallation-agnostic and environment-agnostic. This is important for making setup and use as simple and decison-free as possible. It's especially important on Linux, where there may be several versions of Python installed, with different versions and access levels. This avoids complications, especially for new users. It's common for Python-based CLI tools to not run properly when installed from pip due to the PATH not being configured in the expected way.

• It manages Python installations - lets you choose which Python version (≥ 3.4) to use. If one's installed, it uses that. If not, it downloads a binary, stores it in ~/python-installs, and uses that. It lets the user select which Python version to use in pyproject.toml, then automatically uses that version, installing as required.

• It keeps dependencies in the project directory, in __pypackages__, and doesn't modify outside files (Other than new Python installs). This is subtle, but reinforces the idea that there's no hidden state to be concerned with.

• It will always use the specified version of Python. This is a notable problem with Poetry; it may pick the wrong installation (eg Python2 vice Python3), with no obvious way to change it.

• Multiple versions of a dependency can be installed, allowing resolution of conflicting sub-dependencies. (ie: Your package requires Dep A>=1.0 and Dep B. Dep B requires Dep A==0.9) There are many cases where Poetry and Pipenv will fail to resolve dependencies, but we're able to by doing this. Try it for yourself with a few random dependencies from pypi; there's a good change you'll hit this problem using Poetry or Pipenv. Limitations: This will not work for some compiled dependencies, and attempting to package something using this will trigger an error.

Virtual environments are easy. What's the point of this?

Hopefully we're not replacing one problem with another.

Some people like the virtual-environment workflow - it requires only tools included with Python, and uses few console commands to create, and activate and environments. However, it may be tedius depending on workflow: The commands may be long depending on the path of virtual envs and projects, and it requires modifying the state of the terminal for each project, each time you use it, which you may find inconvenient or inelegant.

If you're satisified with an existing flow, there may be no reason to change, but I think we can do better. This is especially relevant for new Python users who haven't groked venvs, or are unaware of the hazards of working with a system Python.

Pipenv improves the workflow by automating environment use, and allowing reproducable dependency resolution. Poetry improves upon Pipenv's API, speed, and dependency resolution, as well as improving the packaging and distributing process by using a consolidating project config. Both are sensitive to the Python environment used to run them. This tool attempts to improve upon both in the areas listed in the section above. Its goal is to be as intuitive as possible.

Conda addresses these problems elegantly, but maintains a separate repository of binaries from PyPi. If all packages you need are available on Conda, it may be the best solution. If not, it requires falling back to Pip, which means using two separate package managers.

When building and deploying packages, a set of degenerate files are traditionally used: setup.py, setup.cfg, requirements.txt and MANIFEST.in. We use pyproject.toml as the single-source of project info required to build and publish.

A thoroughly biased feature table

(Please PR anything here that's innacurate, incomplete, or misleading)

These tools have different scopes and purposes:

Use

• Create a pyproject.toml file in your project directory. Note that running init, new, or install creates this file automatically. See PEP 518 for details.

Example contents:

[tool.pypackage]
py_version = "3.7"
name = "runcible"
version = "0.1.0"
author = "John Hackworth"


[tool.pypackage.dependencies]
numpy = "^1.16.4"
diffeqpy = "1.1.0"

The [tool.pypackage] section is used for metadata, and isn't required unless building and distributing a package. The [tool.pypyackage.dependencies] section contains all dependencies, and is an analog to requirements.txt.

You can specify extra dependencies, which will only be installed when passing explicit flags to pypackage install, or when included in another project with the appropriate flag enabled. Ie packages requiring this one can enable with pip install -e etc.

[tool.pypackage.extras]
test = ["pytest", "nose"]
secure = ["crypto"]

If you'd like to an install a dependency with extras, use syntax like this:

[tool.pypackage.dependencies]
ipython = { version = "^7.7.0", extras = ["qtconsole"] }

For details on how to specify dependencies in this Cargo.toml-inspired semvar format, reference this guide.

We also attempt to parse metadata and dependencies from tool.poetry sections of pyproject.toml, so there's no need to modify the format if you're using that.

What you can do

Managing dependencies:

• pypackage install - Install all packages in pyproject.toml, and remove ones not (recursively) specified
• pypackage install toolz - If you specify one or more packages after install, those packages will be added to pyproject.toml and installed.
• pypackage install numpy==1.16.4 matplotlib>=3.1. - Example with multiple dependencies, and specified versions
• pypackage uninstall toolz - Remove one or more dependencies

Running REPL and Python files in the environment:

• pypackage python - Run a Python REPL
• pypackage python main.py - Run a python file
• pypackage ipython, pypackage black etc - Run a CLI script like ipython. This can either have been installed by a dependency, or specified under [tool.pypackage], scripts
• pypackage run ipython - alternate syntax

Building and publishing:

• pypackage package - Package for distribution (uses setuptools internally, and builds both source and wheel if applicable.)
• pypackage package --features "test all" - Package for distribution with features enabled, as defined in pyproject.toml
• pypackage publish - Upload to PyPi (Repo specified in pyproject.toml. Uses Twine internally.)

Misc:

• pypackage list - Display all installed packages and console scripts

• pypackage new projname - Create a directory containing the basics for a project: a readme, pyproject.toml, .gitignore, and directory for code

• pypackage init - Create a pyproject.toml file in an existing project directory. Pull info from requirements.text and Pipfile as required.

• pypackage reset - Remove the environment, and uninstall all packages

• pypackage clear - Clear the global cache of downloaded packages, in ~/python-installs/dependency-cache

• pypackage -V - Get the current version of this tool

• pypackage help Get help, including a list of available commands

How installation and locking work

Running pypackage install syncs the project's installed dependencies with those specified in pyproject.toml. It generates pypackage.lock, which on subsequent runs, keeps dependencies each package a fixed version, as long as it continues to meet the constraints specified in pyproject.toml. Adding a package name via the CLI, eg pypackage install matplotlib simply adds that requirement before proceeding. pypackage.lock isn't meant to be edited directly.

Each dependency listed in pyproject.toml is checked for a compatible match in pypackage.lock If a constraint is met by something in the lock file, the version we'll sync will match that listed in the lock file. If not met, a new entry is added to the lock file, containing the highest version allowed by pyproject.toml. Once complete, packages are installed and removed in order to exactly meet those listed in the updated lock file.

This tool downloads and unpacks wheels from pypi, or builds wheels from source if none are availabile. It verifies the integrity of the downloaded file against that listed on pypi using SHA256, and the exact versions used are stored in a lock file.

When a dependency is removed from pyproject.toml, it, and its subdependencies not also required by other packages are removed from the __pypackages__ folder.

How dependencies are resolved

Compatible versions of dependencies are determined using info from the PyPi Warehouse (available versions, and hash info), and the pydeps database. We use pydeps, which is built specifically for this project, due to inconsistent dependency information stored on pypi. A dependency graph is built using this cached database. We attempt to use the newest compatible version of each package.

If all packages are either only specified once, or specified multiple times with the same newest-compatible version, we're done resolving, and ready to install and sync.

If a package is included more than once with different newest-compatible versions, but one of those newest-compatible is compatible with all requirements, we install that one. If not, we search all versions to find one that's compatible.

If still unable to find a version of a package that satisfies all requirements, we install multiple versions of it as-required, store them in separate directories, and modify their parents' imports as required.

Note that it may be possible to resolve dependencies in cases not listed above, instead of installing multiple versions. Ie we could try different combinations of top-level packages, check for resolutions, then vary children as-required down the hierarchy. We don't do this because it's slow, has no guarantee of success, and involves installing older versions of packages.

Not-yet-implemented

• Installing from sources other than pypi (eg repos)

• Installing multiple versions of a dependency may not work if it uses compiles code

• The lock file is missing some info like hashes

• Adding a dependency via the CLI with a specific version constraint, or extras.

• Developer requirements

• Packaging and publishing projects that use compiled extensions

• Download and install a Python version you specify, if not already installed

Building and uploading your project to PyPi

In order to build and publish your project, additional info is needed in pyproject.toml, that mimics what would be in setup.py. Example:

[tool.pypackage]
name = "everythingkiller"
py_version = "3.6"
version = "0.1.0"
author = "Fraa Erasmas"
author_email = "raz@edhar.math"
description = "Small, but packs a punch!"
homepage = "https://everything.math"
repository = "https://github.com/raz/everythingkiller"
license = "MIT"
classifiers = [
    "Topic :: System :: Hardware",
    "Topic :: Scientific/Engineering :: Human Machine Interfaces",
]
scripts = { activate = "jeejah:activate" }


[tool.pypackage.dependencies]
numpy = "^1.16.4"
manim = "0.1.8"
ipython = {version = "^7.7.0", extras=["qtconsole"]}

Building this from source

If you’d like to build from source, download and install Rust, clone the repo, and in the repo directory, run cargo build --release.

Ie on Linux:

curl https://sh.rustup.rs -sSf | sh
git clone https://github.com/david-oconnor/pypackage.git
cd pypackage
cargo build --release

Updating

If installed via Cargo, run cargo install pypackage --force.

Contributing

If you notice unexpected behavior or missing features, please post an issue, or submit a PR. If you see unexpected behavior, it's probably a bug! Post an issue listing the dependencies that did not install correctly.

Dependency cache repo:

• Github Example API calls: https://pydeps.herokuapp.com/requests, https://pydeps.herokuapp.com/requests/2.21.0. This pulls all top-level dependencies for the requests package, and the dependencies for version 2.21.0 respectively. The first time this command is run for a package/version combo, it may be slow. Subsequent calls, by anyone, should be fast. This is due to having to download and install each package on the server to properly determine dependencies, due to unreliable information on the pypi warehouse.

Gotchas

• Make sure the pypackage binary is accessible in your path. If installing via a deb, msi, or Cargo, this should be set up automatically.
• Make sure __pypackages__ and .venv are in your .gitignore file.

References

• PEP 582 - Python local packages directory
• PEP 518 - pyproject.toml
• Semantic versioning
• PEP 440 -- Version Identification and Dependency Specification
• Specifying dependencies in Cargo
• Predictable dependency management blog entry
• Blog on why Pyhon dependencies are hard to determine