package
Create a deployable artifact.
The package goal creates an artifact that can be deployed or distributed.
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.
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.
You can depend on a package target in a python_test / 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, allowing you to test things like that the artifact has the correct files present and that it's executable.
This allows you to test your packaging pipeline by simply running pants test ::, without needing custom integration test scripts.
See test for more information.
Check out our blog Streamling Docker Builds to read about how you can combine these package formats with Pants's Docker support. Also see our Docker docs
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 and wheels) of all targets the binary transitively depends on.
The PEX metadata will include:
- The entry point or console script specified by the
pex_binarytarget, if any. - The intersection of all interpreter constraints applicable to the code in the Pex. See Interpreter compatibility.
You can also tweak many options, such as the execution_mode option to optimize for faster initial runs vs. subsequent runs. Run pants help pex_binary.
The entry_point and script fields
The entry_point and script fields set the behavior for what happens when you run ./dist/my_app.pex, such as if it runs a particular script or launches an app.
Usually, you'll want to use entry_point, which lets you specify a module and optionally a function to execute, such as project.my_app:main. This is especially useful when you want to run first-party code.
script is useful when you want to directly run a third-party dependency that sets console_scripts in its distribution. This allows you to, for example, set script="black" to create black.pex that behaves like if you had pip installed black and then run black in your shell:
❯ ./dist/black.pex --version
python -m black, 23.1.0 (compiled: yes)
You can also leave off both fields, which will cause ./dist/my_app.pex to launch a Python interpreter with all the relevant code and dependencies loaded.
❯ ./dist/my_app.pex
Python 3.9.6 (default, Jun 28 2021, 19:24:41)
[Clang 12.0.5 (clang-1205.0.22.9)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
(InteractiveConsole)
If you use the entry_point field, Pants will use dependency inference, which you can confirm by running pants dependencies path/to:app. Otherwise, you must manually add to the dependencies field.
entry_point with a file name
You can specify a file name, which Pants will convert into a well-formed entry point. Like with the source / sources field, file paths are relative to the BUILD file, rather than the build root.
# The default `sources` field will include `main.py`.
python_sources(name="lib")
# 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 the 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.
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_sources(name="lib")
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 using entry_point with a file name, this does not work with file arguments; you must use the target address, like pants package helloworld:app.
script
You can set the script to any console_script or script exposed by your third-party requirements.
python_requirement(name="black_req", requirements=["black==23.1.0"])
pex_binary(
name="black_bin",
script="black",
dependencies=[":black_req"],
)
You must explicitly add the dependencies you'd like to the dependencies field.
This does not work with file arguments; you must use the target address, like pants package helloworld:black_bin.
Injecting command-line arguments and environment variables
You can use the inject_args and inject_env fields to "freeze" command-line arguments and environment variables into the PEX file. This can save you from having to create shim files around generic binaries. For example:
python_requirement(name="gunicorn", requirements=["gunicorn==20.1.0"])
pex_binary(
name="myservice_bin",
script="gunicorn",
args=["myproduct.myservice.wsgi:app", "--name=myservice"],
env={"MY_ENV_VAR=1"},
dependencies=[":gunicorn"],
)
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"].
.pex file with unzipBecause 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.
resource instead of filefile and files targets will not be included in the built PEX because filesystem APIs like open() would not load them as expected. Instead, use the resource and resources target or wrap your pex_binary in an archive target. See Assets and archives for further explanation.
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
pex_binaries target generator
If you have several scripts in the same directory, it can be convenient to use the pex_binaries target generator, which will generate one pex_binary target per entry in the entry_points field:
# The default `sources` will include all our source files.
python_sources(name="lib")
pex_binaries(
name="binaries",
entry_points=[
"app1.py",
"app2.py",
"app3.py:my_func",
],
overrides={
"app2.py:my_func": {"execution_mode": "venv"},
},
)
Use pants peek path/to/dir: to inspect the generated pex_binary targets.
Create a setuptools distribution
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.
Create a zip or tar file
See Resources and archives for how to create a zip or tar file with built binaries and/or loose files in it by using the archive target.
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.
Create an AWS Lambda
See AWS Lambda for how to build a zip file that works with AWS Lambda.
Create a Google Cloud Function
See Google Cloud Functions for how to build a zip file that works with Google Cloud Functions.
Create a PyOxidizer binary
See PyOxidizer for how to distribute your code as a binary, like PEX, but with the Python interpreter included.