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Python Enhancement Proposals

PEP 735 – Dependency Groups in pyproject.toml

Author:
Stephen Rosen <sirosen0 at gmail.com>
Sponsor:
Brett Cannon <brett at python.org>
PEP-Delegate:
Paul Moore <p.f.moore at gmail.com>
Discussions-To:
Discourse thread
Status:
Draft
Type:
Standards Track
Topic:
Packaging
Created:
20-Nov-2023
Post-History:
14-Nov-2023, 20-Nov-2023
Table of Contents

Abstract

This PEP specifies a mechanism for storing package requirements in pyproject.toml files such that they are not included in any built distribution of the project.

This is suitable for creating named groups of dependencies, similar to requirements.txt files, which launchers, IDEs, and other tools can find and identify by name.

The feature defined here is referred to as “Dependency Groups”.

Motivation

There are two major use cases for which the Python community has no standardized answer:

  • How should development dependencies be defined for packages?
  • How should dependencies be defined for projects which do not build distributions (non-package projects)?

In support of these two needs, there are two common solutions which are similar to this proposal:

  • requirements.txt files
  • package extras

Both requirements.txt files and extras have limitations which this standard seeks to overcome.

Note that the two use cases above describe two different types of projects which this PEP seeks to support:

  • Python packages, such as libraries
  • non-package projects, such as data science projects

Limitations of requirements.txt files

Many projects may define one or more requirements.txt iles, and may arrange them either at the project root (e.g. requirements.txt and test-requirements.txt) or else in a directory (e.g. requirements/base.txt and requirements/test.txt). However, there are major issues with the use of requirements files in this way:

  • There is no standardized naming convention such that tools can discover or use these files by name.
  • requirements.txt files are not standardized, but instead provide options to pip.

As a result, it is difficult to define tool behaviors based on requirements.txt files. They are not trivial to discover or identify by name, and their contents may contain a mix of package specifiers and additional pip options.

The lack of a standard for requirements.txt contents also means they are not portable to any alternative tools which wish to process them other than pip.

Additionally, requirements.txt files require a file per dependency list. For some use-cases, this makes the marginal cost of dependency groupings high, relative to their benefit. A terser declaration is beneficial to projects with a number of small groups of dependencies.

In contrast with this, Dependency Groups are defined at a well known location in pyproject.toml with fully standardized contents. Not only will they have immediate utility, but they will also serve as a starting point for future standards.

Limitations of extras

extras are additional package metadata declared in the [project.optional-dependencies] table. They provide names for lists of package specifiers which are published as part of a package’s metadata, and which a user can request under that name, as in pip install 'foo[bar]' to install foo with the bar extra.

Because extras are package metadata, they are not usable when a project does not build a distribution (i.e., is not a package).

For projects which are packages, extras are a common solution for defining development dependencies, but even under these circumstances they have downsides:

  • Because an extra defines optional additional dependencies, it is not possible to install an extra without installing the current package and its dependencies.
  • Because they are user-installable, extras are part of the public interface for packages. Because extras are published, package developers often are concerned about ensuring that their development extras are not confused with user-facing extras.

Rationale

This PEP defines the storage of requirements data in lists within a [dependency-groups] table. This name was chosen to match the canonical name of the feature (“Dependency Groups”).

This format should be as simple and learnable as possible, having a format very similar to existing requirements.txt files for many cases. Each list in [dependency-groups] is defined as a list of package specifiers. For example:

[dependency-groups]
test = ["pytest>7", "coverage"]

There are a number of use cases for requirements.txt files which require data which cannot be expressed in PEP 508 dependency specifiers. Such fields are not valid in Dependency Groups. Including many of the data and fields which pip supports, such as index servers, hashes, and path dependencies, requires new standards. This standard leaves room for new standards and developments, but does not attempt to support all valid requirements.txt contents.

The only exception to this is the -r flag which requirements.txt files use to include one file in another. Dependency Groups support an “include” mechanism which is similar in meaning, allowing one dependency group to extend another.

Dependency Groups have two additional features which are similar to requirements.txt files:

  • they are not published as part of any built distribution
  • installation of a dependency group does not imply installation of a package’s dependencies or the package itself

Use Cases

The following use cases are considered important targets for this PEP. They are defined in greater detail in the Use Cases Appendix.

  • Web Applications deployed via a non-python-packaging build process
  • Libraries with unpublished dev dependency groups
  • Data science projects with groups of dependencies but no core package
  • Input data to lockfile generation (Dependency Groups should generally not be used as a location for locked dependency data)
  • Input data to an environment manager, such as tox, Nox, or Hatch
  • Configurable IDE discovery of test and linter requirements

Regarding Poetry and PDM Dependency Groups

The existing Poetry and PDM tools already offer a feature which each calls “Dependency Groups”. However, absent any standard for specifying collections of dependencies, each tool defines these in a tool-specific way, in the relevant sections of the [tool] table.

(PDM also uses extras for some Dependency Groups, and overlaps the notion heavily with extras.)

This PEP does not support all of the features of Poetry and PDM, which, like requirements.txt files for pip, support several non-standard extensions to common dependency specifiers.

It should be possible for such tools to use standardized Dependency Groups as extensions of their own Dependency Group mechanisms. However, defining a new data format which replaces the existing Poetry and PDM solutions is a non-goal. Doing so would require standardizing several additional features, such as path dependencies, which are supported by these tools.

Dependency Groups are not Hidden Extras

Dependency Groups are very similar to extras which go unpublished. However, there are two major features which distinguish them from extras further:

  • they support non-package projects
  • installation of a Dependency Group does not imply installation of a package’s dependencies (or the package itself)

Future Compatibility & Invalid Data

Dependency Groups are intended to be extensible in future PEPs. However, Dependency Groups should also be usable by multiple tools in a single Python project. With multiple tools using the same data, it is possible that one implements a future PEP which extends Dependency Groups, while another does not.

To support users in this case, this PEP defines and recommends validation behaviors in which tools only examine Dependency Groups which they are using. This allows multiple tools, using different versions of Dependency Groups data, to share a single table in pyproject.toml.

Specification

This PEP defines a new section (table) in pyproject.toml files named dependency-groups. The dependency-groups table contains an arbitrary number of user-defined keys, each of which has, as its value, a list of requirements (defined below). These keys must be valid non-normalized names, and must be normalized before comparisons.

Tools SHOULD prefer to present the original, non-normalized name to users by default. If duplicate names, after normalization, are encountered, tools SHOULD emit an error.

Requirement lists under dependency-groups may contain strings, tables (“dicts” in Python), or a mix of strings and tables.

Strings in requirement lists must be valid Dependency Specifiers, as defined in PEP 508.

Tables in requirement lists must be valid Dependency Object Specifiers, defined below.

Dependency Object Specifiers

Dependency Object Specifiers are tables which define zero or more dependencies.

This PEP standardizes only one type of Dependency Object Specifier, a “Dependency Group Include”. Other types may be added in future standards.

Dependency Group Include

A Dependency Group Include includes the dependencies of another Dependency Group in the current Dependency Group.

An include is defined as a table with exactly one key, "include", whose value is a string, the name of another Dependency Group.

For example, {include = "test"} is an include which expands to the contents of the test Dependency Group.

Includes are defined to be exactly equivalent to the contents of the named Dependency Group, inserted into the current group at the location of the include. For example, if foo = ["a", "b"] is one group, and bar = ["c", {include = "foo"}, "d"] is another, then bar should evaluate to ["c", "a", "b", "d"] when Dependency Group Includes are expanded.

Dependency Group Includes may specify the same package multiple times. Tools SHOULD NOT deduplicate or otherwise alter the list contents produced by the include. For example, given the following table:

[dependency-groups]
group-a = ["foo"]
group-b = ["foo>1.0"]
group-c = ["foo<1.0"]
all = ["foo", {include = "group-a"}, {include = "group-b"}, {include = "group-c"}]

The resolved value of all SHOULD be ["foo", "foo", "foo>1.0", "foo<1.0"]. Tools should handle such a list exactly as they would handle any other case in which they are asked to process the same requirement multiple times with different version constraints.

Dependency Group Includes may include lists containing Dependency Group Includes, in which case those includes should be expanded as well. Dependency Group Includes MUST NOT include cycles, and tools SHOULD report an error if they detect a cycle.

Example Dependency Groups Table

The following is an example of a partial pyproject.toml which uses this to define four Dependency Groups: test, docs, typing, and typing-test:

[dependency-groups]
test = ["pytest", "coverage"]
docs = ["sphinx", "sphinx-rtd-theme"]
typing = ["mypy", "types-requests"]
typing-test = [{include = "typing"}, {include = "test"}, "useful-types"]

Note that none of these Dependency Group declarations implicitly install the current package, its dependencies, or any optional dependencies. Use of a Dependency Group like test to test a package requires that the user’s configuration or toolchain also installs .. For example,

$TOOL install-dependency-group test
pip install -e .

could be used (supposing $TOOL is a tool which supports installing Dependency Groups) to build a testing environment.

This also allows for the docs dependency group to be used without installing the project as a package:

$TOOL install-dependency-group docs

Package Building

Build backends MUST NOT include Dependency Group data in built distributions as package metadata. This means that PKG-INFO in sdists and METADATA in wheels do not include any referencable fields containing Dependency Groups.

It is valid to use Dependency Groups in the evaluation of dynamic metadata, and pyproject.toml files included in sdists will naturally still contain the [dependency-groups] table. However, the table contents are not part of a published package’s interfaces.

Installing Dependency Groups

Tools which support Dependency Groups are expected to provide new options and interfaces to allow users to install from Dependency Groups.

No syntax is defined for expressing the Dependency Group of a package, for two reasons:

  • it would not be valid to refer to the Dependency Groups of a third-party package from PyPI (because the data is defined to be unpublished)
  • there is not guaranteed to be a current package for Dependency Groups – part of their purpose is to support non-package projects

For example, a possible pip interface for installing Dependency Groups would be:

pip install --dependency-groups=test,typing

Note that this is only an example. This PEP does not declare any requirements for how tools support the installation of Dependency Groups.

Validation and Compatibility

Tools supporting Dependency Groups may want to validate data before using it. However, tools implementing such validation behavior should be careful to allow for future expansions to this spec, so that they do not unnecessarily emit errors or warnings in the presence of new syntax.

Tools SHOULD error when evaluating or processing unrecognized data in Dependency Groups.

Tools SHOULD NOT eagerly validate the list contents of all Dependency Groups.

This means that in the presence of the following data, most tools will allow the foo group to be used, and will only error when the bar group is used:

[dependency-groups]
foo = ["pyparsing"]
bar = [{set-phasers-to = "stun"}]

Reference Implementation

The following Reference Implementation prints the contents of a Dependency Group to stdout, newline delimited. The output is therefore valid requirements.txt data.

import re
import sys
import tomllib
from collections import defaultdict

from packaging.requirements import Requirement


def _normalize_name(name: str) -> str:
    return re.sub(r"[-_.]+", "-", name).lower()


def _normalize_group_names(dependency_groups: dict) -> dict:
    original_names = defaultdict(list)
    normalized_groups = {}

    for group_name, value in dependency_groups.items():
        normed_group_name = _normalize_name(group_name)
        original_names[normed_group_name].append(group_name)
        normalized_groups[normed_group_name] = value

    errors = []
    for normed_name, names in original_names.items():
        if len(names) > 1:
            errors.append(f"{normed_name} ({', '.join(names)})")
    if errors:
        raise ValueError(f"Duplicate dependency group names: {', '.join(errors)}")

    return normalized_groups


def _resolve_dependency_group(
    dependency_groups: dict, group: str, past_groups: tuple[str] = ()
) -> list[str]:
    if group in past_groups:
        raise ValueError(f"Cyclic dependency group include: {group} -> {past_groups}")

    if group not in dependency_groups:
        raise LookupError(f"Dependency group '{group}' not found")

    raw_group = dependency_groups[group]
    if not isinstance(raw_group, list):
        raise ValueError(f"Dependency group '{group}' is not a list")

    realized_group = []
    for item in raw_group:
        if isinstance(item, str):
            # packaging.requirements.Requirement parsing ensures that this is a valid
            # PEP 508 Dependency Specifier
            # raises InvalidRequirement on failure
            Requirement(item)
            realized_group.append(item)
        elif isinstance(item, dict):
            if tuple(item.keys()) != ("include",):
                raise ValueError(f"Invalid dependency group item: {item}")

            include_group = _normalize_name(next(iter(item.values())))
            realized_group.extend(
                _resolve_dependency_group(
                    dependency_groups, include_group, past_groups + (group,)
                )
            )
        else:
            raise ValueError(f"Invalid dependency group item: {item}")

    return realized_group


def resolve(dependency_groups: dict, group: str) -> list[str]:
    if not isinstance(dependency_groups, dict):
        raise TypeError("Dependency Groups table is not a dict")
    if not isinstance(group, str):
        raise TypeError("Dependency group name is not a str")
    return _resolve_dependency_group(dependency_groups, group)


if __name__ == "__main__":
    with open("pyproject.toml", "rb") as fp:
        pyproject = tomllib.load(fp)

    dependency_groups_raw = pyproject["dependency-groups"]
    dependency_groups = _normalize_group_names(dependency_groups_raw)
    print("\n".join(resolve(pyproject["dependency-groups"], sys.argv[1])))

Backwards Compatibility

At time of writing, the dependency-groups namespace within a pyproject.toml file is unused. Since the top-level namespace is reserved for use only by standards specified at packaging.python.org, there should be no direct backwards compatibility concerns.

Security Implications

This PEP introduces new syntaxes and data formats for specifying dependency information in projects. However, it does not introduce newly specified mechanisms for handling or resolving dependencies.

It therefore does not carry security concerns other than those inherent in any tools which may already be used to install dependencies – i.e. malicious dependencies may be specified here, just as they may be specified in requirements.txt files.

How to Teach This

This feature should be referred to by its canonical name, “Dependency Groups”.

The basic form of usage should be taught as a variant on typical requirements.txt data. Standard dependency specifiers (PEP 508) can be added to a named list. Rather than asking pip to install from a requirements.txt file, either pip or a relevant workflow tool will install from a named Dependency Group.

For new Python users, they may be taught directly to create a section in pyproject.toml containing their Dependency Groups, similarly to how they are currently taught to use requirements.txt files. This also allows new Python users to learn about pyproject.toml files without needing to learn about package building. A pyproject.toml file with only [dependency-groups] and no other tables is valid.

For both new and experienced users, the Dependency Group Includes will need to be explained. For users with experience using requirements.txt, this can be described as an analogue for -r. For new users, they should be taught that an include allows one Dependency Group to extend another. Similar configuration interfaces and the Python list.extend method may be used to explain the idea by analogy.

Rejected Ideas

Why not define each Dependency Group as a table?

If our goal is to allow for future expansion, then defining each Dependency Group as a subtable, thus enabling us to attach future keys to each group, allows for the greatest future flexibility.

However, it also makes the structure nested more deeply, and therefore harder to teach and learn. One of the goals of this PEP is to be an easy replacement for many requirements.txt use-cases.

Why not define a special string syntax to extend Dependency Specifiers?

Earlier drafts of this specification defined syntactic forms for Dependency Group Includes and Path Dependencies.

However, there were three major issues with this approach:

  • it complicates the string syntax which must be taught, beyond PEP 508
  • the resulting strings would always need to be disambiguated from PEP 508 specifiers, complicating implementations

Why not allow for more non-PEP 508 dependency specifiers?

Several use cases surfaced during discussion which need more expressive specifiers than are possible with PEP 508.

“Path Dependencies”, referring to local paths, and references to [project.dependencies] were of particular interest.

However, there are no existing standards for these features (excepting the de-facto standard of pip’s implementation details).

As a result, attempting to include these features in this PEP results in a significant growth in scope, to attempt to standardize these various features and pip behaviors.

Special attention was devoted to attempting to standardize the expression of editable installations, as expressed by pip install -e and PEP 660. However, although the creation of editable installs is standardized for build backends, the behavior of editables is not standardized for installers. Inclusion of editables in this PEP requires that any supporting tool allows for the installation of editables.

Therefore, although Poetry and PDM provide syntaxes for some of these features, they are considered insufficiently standardized at present for inclusion in Dependency Groups.

Why is the table not named [run], [project.dependency-groups], …?

There are many possible names for this concept. It will have to live alongside the already existing [project.dependencies] and [project.optional-dependencies] tables, and possibly a new [external] dependency table as well (at time of writing, PEP 725, which defines the [external] table, is in progress).

[run] was a leading proposal in earlier discussions, but its proposed usage centered around a single set of runtime dependencies. This PEP explicitly outlines multiple groups of dependencies, which makes [run] a less appropriate fit – this is not just dependency data for a specific runtime context, but for multiple contexts.

[project.dependency-groups] would offer a nice parallel with [project.dependencies] and [project.optional-dependencies], but has major downsides for non-package projects. [project] requires several keys to be defined, such as name and version. Using this name would either require redefining the [project] table to allow for these keys to be absent, or else would impose a requirement on non-package projects to define and use these keys. By extension, it would effectively require any non-package project allow itself to be treated as a package.

Why is pip’s planned implementation of --only-deps not sufficient?

pip currently has a feature on the roadmap to add an –only-deps flag. This flag is intended to allow users to install package dependencies and extras without installing the current package.

It does not address the needs of non-package projects, nor does it allow for the installation of an extra without the package dependencies.

Why isn’t <environment manager> a solution?

Existing environment managers like tox, Nox, and Hatch already have the ability to list inlined dependencies as part of their configuration data. This meets many development dependency needs, and clearly associates dependency groups with relevant tasks which can be run. These mechanisms are good but they are not sufficient.

First, they do not address the needs of non-package projects.

Second, there is no standard for other tools to use to access these data. This has impacts on high-level tools like IDEs and Dependabot, which cannot support deep integration with these Dependency Groups. (For example, at time of writing Dependabot will not flag dependencies which are pinned in tox.ini files.)

Open Issues

Should it be possible for a Dependency Group to include [project.dependencies] or vice-versa?

A topic of debate is how – or if – Dependency Groups should interact with [project.dependencies] and [project.optional-dependencies].

An additional Dependency Object Specifier could be added for including [project.dependencies] or [project.optional-dependencies] data to a Dependency Group. However, it is a goal of this spec that Dependency Groups should always be resolvable to a list of packages without the use of a build backend. Therefore, an inclusion of [project.dependencies] or [project.optional-dependencies] would need to be defined carefully with respect to dynamic dependencies.

The inclusion running in the opposite direction – a [project.dependencies] list containing a Dependency Group reference, possibly re-using Dependency Group Include objects as the mechanism – is also possible but presents different challenges. Such an addition would introduce new syntax into the [project] table, which not all tools would support at first.

Appendix A: Prior Art in Non-Python Languages

This section is primarily informational and serves to document how other language ecosystems solve similar problems.

JavaScript and package.json

In the JavaScript community, packages contain a canonical configuration and data file, similar in scope to pyproject.toml, at package.json.

Two keys in package.json control dependency data: "dependencies" and "devDependencies". The role of "dependencies" is effectively the same as that of [project.dependencies] in pyproject.toml, declaring the direct dependencies of a package.

"dependencies" data

Dependency data is declared in package.json as a mapping from package names to version specifiers.

Version specifiers support a small grammar of possible versions, ranges, and other values, similar to Python’s PEP 440 version specifiers.

For example, here is a partial package.json file declaring a few dependencies:

{
    "dependencies": {
        "@angular/compiler": "^17.0.2",
        "camelcase": "8.0.0",
        "diff": ">=5.1.0 <6.0.0"
    }
}

The use of the @ symbol is a scope which declares the package owner, for organizationally owned packages. "@angular/compiler" therefore declares a package named compiler grouped under angular ownership.

Dependencies Referencing URLs and Local Paths

Dependency specifiers support a syntax for URLs and Git repositories, similar to the provisions in Python packaging.

URLs may be used in lieu of version numbers. When used, they implicitly refer to tarballs of package source code.

Git repositories may be similarly used, including support for committish specifiers.

Unlike PEP 440, NPM allows for the use of local paths to package source code directories for dependencies. When these data are added to package.json via the standard npm install --save command, the path is normalized to a relative path, from the directory containing package.json, and prefixed with file:. For example, the following partial package.json contains a reference to a sibling of the current directory:

{
    "dependencies": {
        "my-package": "file:../foo"
    }
}

The official NPM documentation states that local path dependencies “should not” be published to public package repositories, but makes no statement about the inherent validity or invalidity of such dependency data in published packages.

"devDependencies" data

package.json is permitted to contain a second section named "devDependencies", in the same format as "dependencies". The dependencies declared in "devDependencies" are not installed by default when a package is installed from the package repository (e.g. as part of a dependency being resolved) but are installed when npm install is run in the source tree containing package.json.

Just as "dependencies" supports URLs and local paths, so does "devDependencies".

"peerDependencies" and "optionalDependencies"

There are two additional, related sections in package.json which have relevance.

"peerDependencies" declares a list of dependencies in the same format as "dependencies", but with the meaning that these are a compatibility declaration. For example, the following data declares compatibility with package foo version 2:

{
    "peerDependencies": {
        "foo": "2.x"
    }
}

"optionalDependencies" declares a list of dependencies which should be installed if possible, but which should not be treated as failures if they are unavailable. It also uses the same mapping format as "dependencies".

"peerDependenciesMeta"

"peerDependenciesMeta" is a section which allows for additional control over how "peerDependencies" are treated.

Warnings about missing dependencies can be disabled by setting packages to optional in this section, as in the following sample:

{
    "peerDependencies": {
        "foo": "2.x"
    },
    "peerDependenciesMeta": {
        "foo": {
            "optional": true
        }
    }
}

--omit and --include

The npm install command supports two options, --omit and --include, which can control whether “prod”, “dev”, “optional”, or “peer” dependencies are installed.

The “prod” name refers to dependencies listed under "dependencies".

By default, all four groups are installed when npm install is executed against a source tree, but these options can be used to control installation behavior more precisely. Furthermore, these values can be declared in .npmrc files, allowing per-user and per-project configurations to control installation behaviors.

Ruby & Ruby Gems

Ruby projects may or may not be intended to produce packages (“gems”) in the Ruby ecosystem. In fact, the expectation is that most users of the language do not want to produce gems and have no interest in producing their own packages. Many tutorials do not touch on how to produce packages, and the toolchain never requires user code to be packaged for supported use-cases.

Ruby splits requirement specification into two separate files.

  • Gemfile: a dedicated file which only supports requirement data in the form of dependency groups
  • <package>.gemspec: a dedicated file for declaring package (gem) metadata

The bundler tool, providing the bundle command, is the primary interface for using Gemfile data.

The gem tool is responsible for building gems from .gemspec data, via the gem build command.

Gemfiles & bundle

A Gemfile is a Ruby file containing gem directives enclosed in any number of group declarations. gem directives may also be used outside of the group declaration, in which case they form an implicitly unnamed group of dependencies.

For example, the following Gemfile lists rails as a project dependency. All other dependencies are listed under groups:

source 'https://rubygems.org'

gem 'rails'

group :test do
  gem 'rspec'
end

group :lint do
  gem 'rubocop'
end

group :docs do
  gem 'kramdown'
  gem 'nokogiri'
end

If a user executes bundle install with these data, all groups are installed. Users can deselect groups by creating or modifying a bundler config in .bundle/config, either manually or via the CLI. For example, bundle config set --local without 'lint:docs'.

It is not possible, with the above data, to exclude the top-level use of the 'rails' gem or to refer to that implicit grouping by name.

gemspec and packaged dependency data

A gemspec file is a ruby file containing a Gem::Specification instance declaration.

Only two fields in a Gem::Specification pertain to package dependency data. These are add_development_dependency and add_runtime_dependency. A Gem::Specification object also provides methods for adding dependencies dynamically, including add_dependency (which adds a runtime dependency).

Here is a variant of the rails.gemspec file, with many fields removed or shortened to simplify:

version = '7.1.2'

Gem::Specification.new do |s|
  s.platform    = Gem::Platform::RUBY
  s.name        = "rails"
  s.version     = version
  s.summary     = "Full-stack web application framework."

  s.license = "MIT"
  s.author   = "David Heinemeier Hansson"

  s.files = ["README.md", "MIT-LICENSE"]

  # shortened from the real 'rails' project
  s.add_dependency "activesupport", version
  s.add_dependency "activerecord",  version
  s.add_dependency "actionmailer",  version
  s.add_dependency "activestorage", version
  s.add_dependency "railties",      version
end

Note that there is no use of add_development_dependency. Some other mainstream, major packages (e.g. rubocop) do not use development dependencies in their gems.

Other projects do use this feature. For example, kramdown makes use of development dependencies, containing the following specification in its Rakefile:

s.add_dependency "rexml"
s.add_development_dependency 'minitest', '~> 5.0'
s.add_development_dependency 'rouge', '~> 3.0', '>= 3.26.0'
s.add_development_dependency 'stringex', '~> 1.5.1'

The purpose of development dependencies is only to declare an implicit group, as part of the .gemspec, which can then be used by bundler.

For full details, see the gemspec directive in bundler's documentation on Gemfiles. However, the integration between .gemspec development dependencies and Gemfile/bundle usage is best understood via an example.

gemspec development dependency example

Consider the following simple project in the form of a Gemfile and .gemspec. The cool-gem.gemspec file:

Gem::Specification.new do |s|
  s.author = 'Stephen Rosen'
  s.name = 'cool-gem'
  s.version = '0.0.1'
  s.summary = 'A very cool gem that does cool stuff'
  s.license = 'MIT'

  s.files = []

  s.add_dependency 'rails'
  s.add_development_dependency 'kramdown'
end

and the Gemfile:

source 'https://rubygems.org'

gemspec

The gemspec directive in Gemfile declares a dependency on the local package, cool-gem, defined in the locally available cool-gem.gemspec file. It also implicitly adds all development dependencies to a dependency group named development.

Therefore, in this case, the gemspec directive is equivalent to the following Gemfile content:

gem 'cool-gem', :path => '.'

group :development do
  gem 'kramdown'
end

Appendix B: Prior Art in Python

In the absence of any prior standard for Dependency Groups, two known workflow tools, PDM and Poetry, have defined their own solutions.

This section will primarily focus on these two tools as cases of prior art regarding the definition and use of Dependency Groups in Python.

Projects are Packages

Both PDM and Poetry treat the projects they support as packages. This allows them to use and interact with standard pyproject.toml metadata for some of their needs, and allows them to support installation of the “current project” by doing a build and install using their build backends.

Effectively, this means that neither Poetry nor PDM supports non-package projects.

Non-Standard Dependency Specifiers

PDM and Poetry extend PEP 508 dependency specifiers with additional features which are not part of any shared standard. The two tools use slightly different approaches to these problems, however.

PDM supports specifying local paths, and editable installs, via a syntax which looks like a set of arguments to pip install. For example, the following dependency group includes a local package in editable mode:

[tool.pdm.dev-dependencies]
mygroup = ["-e file:///${PROJECT_ROOT}/foo"]

This declares a dependency group mygroup which includes a local editable install from the foo directory.

Poetry describes dependency groups as tables, mapping package names to specifiers. For example, the same configuration as the above mygroup example might appear as follows under Poetry:

[tool.poetry.group.mygroup]
foo = { path = "foo", editable = true }

PDM restricts itself to a string syntax, and Poetry introduces tables which describe dependencies.

Installing and Referring to Dependency Groups

Both PDM and Poetry have tool-specific support for installing dependency groups. Because both projects support their own lockfile formats, they also both have the capability to transparently use a dependency group name to refer to the locked dependency data for that group.

However, neither tool’s dependency groups can be referenced natively from other tools like tox, nox, or pip. Attempting to install a dependency group under tox, for example, requires an explicit call to PDM or Poetry to parse their dependency data and do the relevant installation step.

Appendix C: Use Cases

Web Applications

A web application (e.g. a Django or Flask app) often does not need to build a distribution, but bundles and ships its source to a deployment toolchain.

For example, a source code repository may define Python packaging metadata as well as containerization or other build pipeline metadata (Dockerfile, etc). The Python application is built by copying the entire repository into a build context, installing dependencies, and bundling the result as a machine image or container.

Such applications have dependency groups for the build, but also for linting, testing, etc. In practice, today, these applications often define themselves as packages to be able to use packaging tools and mechanisms like extras to manage their dependency groups. However, they are not conceptually packages, meant for distribution in sdist or wheel format.

Dependency Groups allow these applications to define their various dependencies without relying on packaging metadata, and without trying to express their needs in packaging terms.

Libraries

Libraries are Python packages which build distributions (sdist and wheel) and publish them to PyPI.

For libraries, Dependency Groups represent an alternative to extras for defining groups of development dependencies, with the important advantages noted above.

A library may define groups for test and typing which allow testing and type-checking, and therefore rely on the library’s own dependencies (as specified in [project.dependencies]).

Other development needs may not require installation of the package at all. For example, a lint Dependency Group may be valid and faster to install without the library, as it only installs tools like black, ruff, or flake8.

lint and test environments may also be valuable locations to hook in IDE or editor support. See the case below for a fuller description of such usage.

Here’s an example Dependency Groups table which might be suitable for a library:

[dependency-groups]
test = ["pytest<8", "coverage"]
typing = ["mypy==1.7.1", "types-requests"]
lint = ["black", "flake8"]
typing-test = [{include = "typing"}, "pytest<8"]

Note that none of these implicitly install the library itself. It is therefore the responsibility of any environment management toolchain to install the appropriate Dependency Groups along with the library when needed, as in the case of test.

Data Science Projects

Data Science Projects typically take the form of a logical collection of scripts and utilities for processing and analyzing data, using a common toolchain. Components may be defined in the Jupyter Notebook format (ipynb), but rely on the same common core set of utilities.

In such a project, there is no package to build or install. Therefore, pyproject.toml currently does not offer any solution for dependency management or declaration.

It is valuable for such a project to be able to define at least one major grouping of dependencies. For example:

[dependency-groups]
main = ["numpy", "pandas", "matplotlib"]

However, it may also be necessary for various scripts to have additional supporting tools. Projects may even have conflicting or incompatible tools or tool versions for different components, as they evolve over time.

Consider the following more elaborate configuration:

[dependency-groups]
main = ["numpy", "pandas", "matplotlib"]
scikit = [{include = "main"}, "scikit-learn==1.3.2"]
scikit-old = [{include = "main"}, "scikit-learn==0.24.2"]

This defines scikit and scikit-old as two similar variants of the common suite of dependencies, pulling in different versions of scikit-learn to suit different scripts.

This PEP only defines these data. It does not formalize any mechanism for a Data Science Project (or any other type of project) to install the dependencies into known environments or associate those environments with the various scripts. Such combinations of data are left as a problem for tool authors to solve, and perhaps eventually standardize.

Lockfile Generation

There are a number of tools which generate lockfiles in the Python ecosystem today. PDM and Poetry each use their own lockfile formats, and pip-tools generates requirements.txt files with version pins and hashes.

Dependency Groups are not an appropriate place to store lockfiles, as they lack many of the necessary features. Most notably, they cannot store hashes, which most lockfile users consider essential.

However, Dependency Groups are a valid input to tools which generate lockfiles. Furthermore, PDM and Poetry both allow a Dependency Group name (under their notions of Dependency Groups) to be used to refer to its locked variant.

Therefore, consider a tool which produces lockfiles, here called $TOOL. It might be used as follows:

$TOOL lock --dependency-group=test
$TOOL install --dependency-group=test --use-locked

All that such a tool needs to do is to ensure that its lockfile data records the name test in order to support such usage.

The mutual compatibility of Dependency Groups is not guaranteed. For example, the Data Science example above shows conflicting versions of scikit-learn. Therefore, installing multiple locked dependency groups in tandem may require that tools apply additional constraints or generate additional lockfile data. These problems are considered out of scope for this PEP.

As two examples of how combinations might be locked:

  • A tool might require that lockfile data be explicitly generated for any combination to be considered valid
  • Poetry implements the requirement that all Dependency Groups be mutually compatible, and generates only one locked version. (Meaning it finds a single solution, rather than a set or matrix of solutions.)

Environment Manager Inputs

A common usage in tox, Nox, and Hatch is to install a set of dependencies into a testing environment.

For example, under tox.ini, type checking dependencies may be defined inline:

[testenv:typing]
deps =
    pyright
    useful-types
commands = pyright src/

This combination provides a desirable developer experience within a limited context. Under the relevant environment manager, the dependencies which are needed for the test environment are declared alongside the commands which need those dependencies. They are not published in package metadata, as extras would be, and they are discoverable for the tool which needs them to build the relevant environment.

Dependency Groups apply to such usages by effectively “lifting” these requirements data from a tool-specific location into a more broadly available one. In the example above, only tox has access to the declared list of dependencies. Under an implementation supporting dependency groups, the same data might be available in a Dependency Group:

[dependency-groups]
typing = ["pyright", "useful-types"]

The data can then be used under multiple tools. For example, tox might implement support as dependency_groups = typing, replacing the deps usage above.

In order for Dependency Groups to be a viable alternative for users of environment managers, the environment managers will need to support processing Dependency Groups similarly to how they support inline dependency declaration.

IDE and Editor Use of Requirements Data

IDE and editor integrations may benefit from conventional or configurable name definitions for Dependency Groups which are used for integrations.

There are at least two known scenarios in which it is valuable for an editor or IDE to be capable of discovering the non-published dependencies of a project:

  • testing: IDEs such as VS Code support GUI interfaces for running particular tests
  • linting: editors and IDEs often support linting and autoformatting integrations which highlight or autocorrect errors

These cases could be handled by defining conventional group names like test, lint, and fix, or by defining configuration mechanisms which allow the selection of Dependency Groups.

For example, the following pyproject.toml declares the three aforementioned groups:

[dependency-groups]
test = ["pytest", "pytest-timeout"]
lint = ["flake8", "mypy"]
fix = ["black", "isort", "pyupgrade"]

This PEP makes no attempt to standardize such names or reserve them for such uses. IDEs may standardize or may allow users to configure the group names used for various purposes.

This declaration allows the project author’s knowledge of the appropriate tools for the project to be shared with all editors of that project.

Source: https://github.com/python/peps/blob/main/peps/pep-0735.rst

Last modified: 2024-02-26 19:05:24 GMT