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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.

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" of dependencies

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

Each third-party dependency is modeled by a `python_requirement` target:

To minimize boilerplate, Pants has macros to generate `python_requirement` targets for you:

  • `python_requirements` for `requirements.txt`.

  • `poetry_requirements` for Poetry projects.

### `requirements.txt`

The `python_requirements()` macro parses a [`requirements.txt`-style file](🔗) to produce a `python_requirement` target for each entry.

For example:

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

Where should I put the `requirements.txt`?

You can put the file 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 `//:my_requirement`, its address would be `3rdparty/python:my_requirement`, for example.

BUT: if you place your `requirements.txt` in a non-standard location (or give it another name via the `python_requirements(source=..)` argument), you will need to configure `pantsd` to restart for edits to the non-standard filename: see [#9946](🔗).

### Poetry

The `poetry_requirements()` macro parses the [Poetry](🔗) section in `pyproject.toml` to produce a `python_requirement` target for each entry.

See the section "Lockfiles" below for how you can also hook up `poetry.lock` to Pants.

## How dependencies are chosen

Once Pants knows about your "universe" 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/file.py`](🔗) 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:

### 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:

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` macros. (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

When you have multiple targets for the same dependency—such as a target with `Django==2` and another with `Django==3`—dependency inference will not work due to ambiguity. Instead, you'll have to manually choose which target to use and add to the `dependencies` field. Pants will warn when dependency inference is ambiguous.

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`. If the version of the overlapping constraints are the same, you may wish to move the common requirements into a common requirements file.

It can, however, be helpful to have multiple targets for the same dependency. For example, this allows part of your repository to upgrade to Django 3 even if the rest of your code still uses Django 2:

## Lockfiles

We strongly recommend using lockfiles because they make your builds [more stable](🔗) so that new releases will not break your project.

### User lockfile

This lockfile is used for your own code, such as when packaging into a PEX binary or resolving dependencies for your tests.

Better user lockfile support is coming

We've been [redesigning user lockfile support](🔗) to be more ergonomic and flexible. In the meantime, there are limitations:

  • Only one lockfile may be used for the whole repository.

    • If you must use conflicting versions for the same dependency, such as `Django==2` and `Django==3`, then you'll need to leave the dependency out of the lockfile.

  • You can leave off dependencies, which means the lockfile might not be comprehensive.

  • The lockfile must be manually generated.

  • The lockfile cannot include `--hash` to ensure that the downloaded files are what is expected.

User lockfiles greatly improve performance

As explained above, Pants only uses the subset of the "universe" of your dependencies that is actually needed for a build, such as running tests and packaging a wheel file. This gives fine-grained caching and has other benefits like built packages (e.g. PEX binaries) only including their true dependencies. However, this also means that you may need to resolve dependencies multiple times, which can be slow.

If you use a lockfile, Pants will optimize to only resolve your requirements one time for your project. Then, for each build, Pants will [extract](🔗) from that resolve the exact subset needed. This greatly speeds up the performance of goals like `test`, `run`, `package`, and `repl`.

If the build's dependencies are not all included in the lockfile, this performance optimization will not be used.

To use a lockfile, first generate a `constraints.txt` file, such as by using one of these techniques:

`venv` + `pip freeze`Create a script like the one in "Set up a virtual environment" below.The lockfile may not work on platforms and Python versions other than what was used to create the virtual env.
[pip-compile](🔗)`pip-compile --allow-unsafe --strip-extras -o constraints.txt requirements.txt`The lockfile may not work on platforms and Python versions other than what was used to run `pip-compile`. Will not capture any `python_requirement` targets declared explicitly in BUILD files or in `pyproject.toml`.
[Poetry](🔗)`poetry export --dev --without-hashes -o constraints.txt`Requires that you are using Poetry for dependency management. Will not capture any `python_requirement ` targets declared explicitly in BUILD files or in `requirements.txt`.

Then, set the option `[python-setup].requirements_constraints` like this:

Pants will then ensure that pip always uses the versions pinned in your lockfile when installing requirements.

### Tool lockfiles

Each Python tool Pants runs for you—like Black, Pytest, Flake8, and MyPy—has a dedicated lockfile.

Pants distributes a lockfile with each tool by default. However, if you change the tool's `version` and `extra_requirements`—or you change its interpreter constraints to not be compatible with our default lockfile—you will need to use a custom lockfile. Set the `lockfile` option in `pants.toml` for that tool, and then run `./pants generate-lockfiles`.

You can also run `./pants generate-lockfiles --resolve=tool`, e.g. `--resolve=flake8`, to only generate that tool's lockfile rather then generating all lockfiles.

Anytime your custom lockfile becomes stale, such as because you changed the `version` again, Pants will error with instructions to regenerate the lockfile.

To disable lockfiles, set `lockfile = "<none>"` for that tool, although we do not recommend this!

## 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:

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.

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

### Version control and local requirements

You might be used to using pip's proprietary VCS-style requirements for this, like `git+https://github.com/django/django.git#egg=django`. However, this proprietary format does not work with Pants.

Instead of pip VCS-style requirements:

Use direct references from [PEP 440](🔗):

You can also install from local files using [PEP 440 direct references](🔗). You must use an absolute path to the file, and you should ensure that the file exists on your machine.

Pip still works with these PEP 440-compliant formats, so you won't be losing any functionality by switching to using them.

Version control via SSH

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

...you may see errors like:

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

### Custom repositories

If you host your own wheels at a custom index (aka "cheese shop"), you can instruct Pants to use it with the option `indexes` in the `[python-repos]` scope.

To exclusively use your custom index—i.e. to not use PyPI—use `indexes = [..]` instead of `indexes.add = [..]`.

## Tip: Set up a virtual environment (optional)

While Pants itself doesn't need a [virtualenv](🔗), it may be useful to create one for working with your tooling outside Pants, such as your IDE.

You can create a virtualenv using standard Python tooling—such as [Python's builtin `venv` module](🔗)—along with running Pants to query for all of your Python requirements.

For example, this script will first create a venv, and then generate a `constraints.txt` file to use as a lockfile.

(Note that this requires installing the tool [jq](🔗).)

This script only captures dependencies that are consumed

Unless the dependency was specified in `requirements.txt`, Pants will only capture it if it is actually used by your code.