Pants v2: Fast, consistent builds for Python and more

Welcome to the Pants v2 documentation hub!

Pants v2 is a fast, scalable build system for growing codebases. It's currently focused on Python, with support for other languages coming soon.

Here you'll find guides to help you get started with Pants v2, comprehensive documentation on how to configure, run and customize Pants v2, and information on how to get help from the Pants community.

Get Started

package

Create a deployable artifact.

The package goal creates an artifact that can be deployed or distributed.

👍

Benefit of Pants: artifacts only include your true dependencies

Because Pants understands the dependencies of your code, and the dependencies of those dependencies, the generated artifact will only include the exact code needed for your package to work. This results in smaller, more focused packages.

The exact type of artifact depends on the type of target the goal is invoked on.

You can run ./pants package :: to build all artifacts in your project. Pants will filter to only the relevant targets.

📘

Use built packages in integration tests

You can depend on a package target in your python_tests target through the runtime_package_dependencies field. Pants will run the equivalent of ./pants package beforehand and copy the built artifact into the test's chroot. See test for more imformation.

Creating a PEX file from a pex_binary target

Running package on a pex_binary target will create an executable PEX file.

The PEX file will contain all the code needed to run the binary, namely:

  • All Python code and resources the binary transitively depends on.
  • The resolved 3rd-party Python dependencies (sdists, eggs and wheels) of all targets the binary transitively depends on.

The PEX metadata will include:

  • The entry point specified by the pex_binary target.
  • The intersection of all interpreter constraints applicable to the code in the Pex. See Interpreter compatibility.

You can also tweak many options, such as if the binary is zip safe. Run ./pants help pex_binary.

The entry_point field

The entry point sets the behavior for what happens when you run ./dist/my_app.pex, such as if it runs a particular script or launches an app. You must specify the entry_point field for every pex_binary.

For the first two approaches, Pants will use dependency inference, which you can confirm by running ./pants dependencies path/to:app. You can also manually add to the dependencies field.

Approach #1, a file name

You can specify a file name, which Pants will convert into a well-formed entry point. Like with the sources field, file paths are relative to the BUILD file, rather than the build root.

# The default `sources` field will include `main.py`.
python_library()

# Pants will convert the entry point to `helloworld.main`.
pex_binary(
  name="app",
  entry_point="main.py",
)

# You can also specify the function to run.
pex_binary(
  name="app_with_func",
  entry_point="main.py:my_func",
)

This approach has an added benefit that you can use file arguments, e.g. ./pants package helloworld/main.py, rather than needing to use target addresses like ./pants package helloworld:app.

Approach #2, explicit entry_point

You can directly specify the entry point in the format path.to.module or path.to.module:my_func. This allows you to use an entry point for a third-party requirement or the Python standard library.

# The default `sources` field will include `main.py`.
python_library()

pex_binary(
  name="app",
  entry_point="helloworld.main",
)

# You can also specify the function to run.
pex_binary(
  name="app_with_func",
  entry_point="helloworld.main:my_func",
)

# You can specify third-party requirements and the std lib.
pex_binary(
  name="3rdparty_app",
  entry_point="bandit:main",
)

Unlike Approach #1, this does not work with file arguments; you must use the target address, like ./pants package helloworld:app.

Approach #3, no entry point

Set entry_point="<none>" to leave off an entry point. This will create a PEX that behaves similarly to a virtual environment's Python interpreter; for example, running ./dist/my_app.pex will open a Python REPL with all of the first party code and third-party requirements included.

Note that Pants cannot use dependency inference in this case, so you must manually add to the dependencies field if you want to include first-party code and/or third-party requirements in the binary.

python_library(name="lib")

pex_binary(
  name="app",
  entry_point="<none>",
  dependencies=[":lib", "3rdparty/python:my_req"],
)

🚧

PEX files may be platform-specific

If your code's requirements include distributions that include native code, then the resulting PEX file will only run on the platform it was built on.

However, if all native code requirements are available as wheels for the target platform, then you can cross-build a PEX file on a different source platform by specifying the platforms field on the pex_binary, e.g. platforms=["linux-x86_64-cp-37-cp37m", "macosx_10_15_x86_64-cp-38-cp38"].

📘

Tip: inspect the .pex file with unzip

Because a .pex file is simply a ZIP file, you can use the Unix tool unzip to inspect the contents. For example, run unzip -l dist/app.pex to see all file members.

Examples

$ ./pants package helloworld/main.py

17:36:42 [INFO] Wrote dist/helloworld/helloworld.pex

We can also build the same Pex by using the address of the pex_binary target, as described here.

$ ./pants package helloworld:app

17:36:42 [INFO] Wrote dist/helloworld/helloworld.pex

Creating a setuptools distribution from a python_distribution target

Running package on a python_distribution target will create a standard setuptools-style Python distribution, such as an sdist or a wheel. See Building Distributions for details.

Creating a zip or tar file from an archive target

See Resources and archives for how to create a zip or tar file with built binaries and/or loose files in it. This is often useful when you want to create a PEX binary using the pex_binary target, and bundle it with some loose config files.

Creating an AWS Lambda from a python_awslambda target

If you have the pants.backend.awslambda.python backend enabled, then you can use the package goal to build AWS Lambdas. See AWS Lambda for more details.

Updated 2 months ago


package


Create a deployable artifact.

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