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Version: 2.20

Third-party dependencies

How to use third-party Python libraries in your project.

Pants handles dependencies with more precision than traditional Python workflows. Traditionally, you have a single heavyweight virtual environment that includes a large set of dependencies, whether or not you actually need them for your current task.

Instead, Pants understands exactly which dependencies every file in your project needs, and efficiently uses just that subset of dependencies needed for the task.

❯ pants dependencies src/py/

❯ pants dependencies --transitive src/py/

Among other benefits, this precise and automatic understanding of your dependencies gives you fine-grained caching. This means, for example, that if none of the dependencies for a particular test file have changed, the cached result can be safely used.

Teaching Pants your "universe"(s) of dependencies

For Pants to know which dependencies each file uses, it must first know which specific dependencies are in your "universe", that is, all the third-party dependencies your project directly uses.

By default, Pants uses a single universe for your whole project, but it's possible to set up multiple. See the header "Multiple resolves" in the "Lockfiles" section.

Each third-party dependency you directly use is modeled by a python_requirement target:


You do not need a python_requirement target for transitive dependencies, that is, requirements that you do not directly import.

To minimize boilerplate, Pants has target generators to generate python_requirement targets for you:

  • python_requirements for requirements.txt or PEP 621-compliant pyproject.toml.
  • poetry_requirements for Poetry projects.


The python_requirements() target generator parses a requirements.txt-style file to produce a python_requirement target for each entry.

For example:

dataclasses ; python_version<'3.7'

If the file uses a different name than requirements.txt, set source like this:

Where should I put the requirements.txt?

You can name the file whatever you want, and put it wherever makes the most sense for your project.

In smaller repositories that only use Python, it's often convenient to put the file at the "build root" (top-level), as used on this page.

For larger repositories or multilingual repositories, it's often useful to have a 3rdparty or 3rdparty/python directory. Rather than the target's address being //:reqs#my_requirement, its address would be 3rdparty/python:reqs#my_requirement, for example; or 3rdparty/python#my_requirement if you leave off the name field for python_requirements. See Target Generation.

PEP 621-compliant pyproject.toml

The python_requirements() target generator also supports parsing dependencies from a PEP 621-compliant pyproject.toml. You must manually specify the source file if you want to use a pyproject.toml file to generate python_requirement targets. For example:


Further information about PEP 621 fields can be found in the PEP documentation. Pants will read dependencies from the project.dependencies list, as well as the project.optional-dependencies mappings. Pants makes no distinction between dependencies and optional-dependencies, all dependencies are treated in the same manner as though they were listed in the dependencies list. For example:

dependencies = [

dataclass = ["dataclasses ; python_version<'3.7'"]


The poetry_requirements() target generator parses the Poetry section in pyproject.toml to produce a python_requirement target for each entry.

python = "^3.8"
requests = {extras = ["security"], version = "~1"}
flask = "~1.12"

isort = "~5.5"

Note that Pants does not consume your poetry.lock file. Instead, see the page on lockfiles.

How dependencies are chosen

Once Pants knows about your "universe"(s) of dependencies, it determines which subset should be used through dependency inference. Pants will read your import statements, like import django, and map it back to the relevant python_requirement target. Run pants dependencies path/to/ or pants dependencies path/to:target to confirm this works.

If dependency inference does not work—such as because it's a runtime dependency you do not import—you can explicitly add the python_requirement target to the dependencies field, like this:

# We don't have an import statement for this dep, so inference
# won't add it automatically. We add it explicitly instead.

Use modules and module_mapping when the module name is not standard

Some dependencies expose a module different than their project name, such as beautifulsoup4 exposing bs4. Pants assumes that a dependency's module is its normalized name—i.e. My-distribution exposes the module my_distribution. If that default does not apply to a dependency, it will not be inferred.

Pants already defines a default module mapping for some common Python requirements, but you may need to augment this by teaching Pants additional mappings:

# `modules` and `module_mapping` is only needed for requirements where
# the defaults do not work.


module_mapping={"my_distribution": ["custom_module"]},

module_mapping={"my_distribution": ["custom_module"]},

If the dependency is a type stub, and the default does not work, set type_stub_modules on the python_requirement target, and type_stubs_module_mapping on the python_requirements and poetry_requirements target generators. (The default for type stubs is to strip off types-, -types, -stubs, and stubs-. So, types-requests gives type stubs for the module requests.)

Warning: multiple versions of the same dependency

It's invalid in Python to have conflicting versions of the same requirement, e.g. Django==2 and Django==3. Instead, Pants supports "multiple resolves" (i.e. multiple lockfiles), as explained in the below section on lockfiles.

When you have multiple targets for the same dependency and they belong to the same resolve, dependency inference will not work due to ambiguity. If you're using lockfiles—which we strongly recommend—the solution is to set the resolve field for problematic python_requirement targets so that each resolve has only one requirement and there is no ambiguity.

This ambiguity is often a problem when you have 2+ requirements.txt or pyproject.toml files in your project, such as project1/requirements.txt and project2/requirements.txt both specifying django. You may want to set up each poetry_requirements/python_requirements target generator to use a distinct resolve so that there is no overlap. Alternatively, if the versions are the same, you may want to consolidate the requirements into a common file.


We strongly recommend using lockfiles to ensure secure, repeatable dependency resolution. See here for details on how to do so.

Advanced usage

Requirements with undeclared dependencies

Sometimes a requirement does not properly declare in its packaging metadata the other dependencies it depends on, so those will not be installed. It's especially common to leave off dependencies on setuptools, which results in import errors like this:

import pkg_resources
ModuleNotFoundError: No module named 'pkg_resources'

To work around this, you can use the dependencies field of python_requirement, so that anytime you depend on your requirement, you also bring in the undeclared dependency.

# First, make sure you have a `python_requirement` target for
# the undeclared dependency.


If you are using the python_requirements and poetry_requirements target generators, you can use the overrides field to do the same thing:

"mongomock": {"dependencies": [":reqs#setuptools"]},

Version control requirements

You can install requirements from version control using two styles:

  • pip's proprietary VCS-style requirements, e.g.
    • git+
    • git+
    • git+
  • direct references from PEP 440, e.g.
    • Django@ git+
    • Django@ git+
    • Django@ git+
Version control via SSH

When using version controlled direct references hosted on private repositories with SSH access:

target@ git+ssh://[email protected]:/myorg/myrepo.git@myhash may see errors like:

 Complete output (5 lines):
[email protected]: Permission denied (publickey).
fatal: Could not read from remote repository.
Please make sure you have the correct access rights
and the repository exists.

To fix this, Pants needs to be configured to pass relevant SSH specific environment variables to processes by adding the following to pants.toml:

env_vars.add = [

Custom repositories

There are two mechanisms for setting up custom Python distribution repositories:

PEP-503 compatible indexes

Use [python-repos].indexes to add PEP 503-compatible indexes, like PyPI.

indexes.add = [""]

To exclusively use your custom index, i.e. to not use the default of PyPI, use indexes = [..] instead of indexes.add = [..].

Use the option [python-repos].find_links for flat lists of packages. Same as pip's --find-links option, you can either use:

  • a URL to an HTML file with links to wheel and/or sdist files, or
  • a file:// absolute path to an HTML file with links, or to a local directory with wheel and/or sdist files. See the section on local requirements below.
find_links = [

Authenticating to custom repos

To authenticate to custom repos, you may need to provide credentials (such as a username and password) in the URL.

You can use config file %(env.ENV_VAR)s interpolation to load the values via environment variables. This avoids checking in sensitive information to version control.

indexes.add = ["http://%(env.INDEX_USERNAME)s:%(INDEX_PASSWORD)[email protected]/index"]

Alternatively, you can hardcode the value in a private (not checked-in) .pants.rc file in each user's Pants repo, that sets this config for the user:

indexes.add = ["http://$USERNAME:[email protected]/index"]

Local requirements

There are two ways to specify local requirements from the filesystem:

# Use an absolute path to a .whl or sdist file.
requirements=["Django @ file:///Users/pantsbuild/prebuilt_wheels/django-3.1.1-py3-none-any.whl"],

# Reminder: we could also put this requirement string in requirements.txt and use the
# `python_requirements` target generator.
  • The option [python-repos].find_links
# Use an absolute path to a directory containing `.whl` and/or sdist files.
find_links = ["file:///Users/pantsbuild/prebuilt_wheels"]
ls /Users/pantsbuild/prebuilt_wheels
# Use normal requirement strings, i.e. without file paths.
python_requirement(name="ansicolors", requirements=["ansicolors==1.1.8"])
python_requirement(name="django", requirements=["django>=3.1,<3.2"])

# Reminder: we could also put these requirement strings in requirements.txt and use the
# `python_requirements` target generator

Unlike PEP 440 direct references, [python-repos].find_links allows you to use multiple artifacts for the same project name. For example, you can include multiple .whl and sdist files for the same project in the directory; if [python-repos].indexes is still set, then Pex/pip may use artifacts both from indexes like PyPI and from your local --find-links.

Both approaches require using absolute paths, and the files must exist on your machine. This is usually fine when locally iterating and debugging. This approach also works well if your entire team can use the same fixed location. Otherwise, see the below section.

Working around absolute paths

If you need to share a lockfile on different machines, and you cannot use the same absolute path, then you can use the option [python-repos].path_mappings along with [python-repos].find_links. (path_mappings is not intended for PEP 440 direct requirements.)

The path_mappings option allows you to substitute a portion of the absolute path with a logical name, which can be set to a different value than your teammates. For example, the path file:///Users/pantsbuild/prebuilt_wheels/django-3.1.1-py3-none-any.whl could become file://${WHEELS_DIR}/django-3.1.1-py3-none-any.whl, where each Pants user defines what WHEELS_DIR should be on their machine.

This feature only works when using Pex lockfiles via [python].resolves.

[python-repos].path_mappings expects values in the form NAME|PATH, e.g. WHEELS_DIR|/Users/pantsbuild/prebuilt_wheels. Also, still use an absolute path for [python-repos].find_links.

If possible, we recommend using a common file location for your whole team, and leveraging Pants's interpolation, so that you avoid each user needing to manually configure [python-repos].path_mappings and [python-repos].find_links. For example, in pants.toml, you could set [python-repos].path_mappings to WHEELS_DIR|%(buildroot)s/python_wheels and [python-repos].find_links to %(buildroot)s/python_wheels. Then, as long as every user has the folder python_wheels in the root of the repository, things will work without additional configuration. Or, you could use a value like %(env.HOME)s/pants_wheels for the path ~/pants_wheels.

# No one needs to change these values, as long as they can use the same shared location.
find_links = ["file://%(buildroot)s/prebuilt_wheels"]
path_mappings = ["WHEELS_DIR|%(buildroot)s/prebuilt_wheels"]

If you cannot use a common file location via interpolation, then we recommend setting these options in a .pants.rc file. Every teammate will need to set this up for their machine.

# Each user must set both of these to the absolute paths on their machines.
find_links = ["file:///Users/pantsbuild/prebuilt_wheels"]
path_mappings = ["WHEELS_DIR|/Users/pantsbuild/prebuilt_wheels"]

After initially setting up [python-repos].path_mappings and [python-repos].find_links, run pants generate-lockfiles or pants generate-lockfiles --resolve=<resolve-name>. You should see the path_mappings key set in the lockfile's JSON.

Constraints files

Sometimes, transitive dependencies of one of your third-party requirements can cause trouble. For example, sometimes requirements do not pin their dependencies well enough, and a newer version of its transitive dependency is released that breaks the requirement. Constraints files allow you to pin transitive dependencies to certain versions, overriding the version that pip/Pex would normally choose.

Constraints files are configured per-resolve, meaning that the resolves for your user code from [python].resolves and each Python tool, such as Black and Pytest, can have different configuration. Use the option [python].resolves_to_constraints_file to map resolve names to paths to pip-compatible constraints files. For example:

data-science = "3rdparty/python/data_science_constraints.txt"
pytest = "3rdparty/python/pytest_constraints.txt"

You can also set the key __default__ to apply the same constraints file to every resolve by default, although this is not always useful because resolves often need different constraints.

only_binary and no_binary

You can use [python].resolves_to_only_binary to avoid using sdists (source distributions) for certain requirements, and [python].resolve_to_no_binary to avoid using bdists (wheel files) for certain requirements.

only_binary and no_binary are configured per-resolve, meaning that the resolves for your user code from [python].resolves and each Python tool, such as Black and Pytest, can have different configuration. Use the options [python].resolves_to_only_binary and [python].resolves_to_no_binary to map resolve names to list of Python requirement names.

For example:

data-science = ["numpy"]

pytest = ["pytest-xdist"]
mypy = ["django-stubs"]

You can also set the key __default__ to apply the same value to every resolve by default.

Tip: use pants export to create a virtual environment for IDEs

See Setting up an IDE for more information on pants export. This will create a virtual environment for your user code for compatibility with the rest of the Python ecosystem, e.g. IDEs like Pycharm.