Pants v2: The ergonomic build system

Welcome to the Pants v2 documentation hub!

Pants v2 is a fast, scalable, user-friendly 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

Protobuf and gRPC

How to generate Python from Protocol Buffers.

When you depend on a protobuf_library in a Python target (like a python_library), Pants will run the Protoc compiler to generate Python code that you can import and use like normal Python code.


Example repository

See the Python example repository for an example of using Protobuf to generate Python.


Benefit of Pants: generated files are always up-to-date

With Pants, there's no need to manually regenerate your code. Pants will ensure you are always using up-to-date files in your builds.

Thanks to fine-grained caching, Pants will regenerate the minimum amount of code required when you do make changes.

Step 1: Activate the Protobuf Python backend

Add this to your pants.toml:

backend_packages.add = [

This adds the new protobuf_library target, which you can confirm by running ./pants help protobuf_library.


Enable the MyPy Protobuf plugin

The MyPy Protobuf plugin generates .pyi type stubs. If you use MyPy through Pants's typecheck goal, this will ensure MyPy understands your generated code.

To activate, set mypy_plugin = true in the [python-protobuf] scope:

mypy_plugin = true

MyPy will use the generated .pyi type stub file, rather than looking at the .py implementation file.


Want to use other protocols, like Thrift?

Please message us on Slack if you would like support for more protocols. We would be happy to either add support to the core Pants distribution or to help you to write a plugin.

Step 2: Set up the protobuf and/or grpcio runtime libraries

Generated Python files require the protobuf library for their imports to work properly. If you're using gRPC, you also need the grpcio library.

First, add protobufβ€”and grpcio, if relevantβ€” to your requirements.txt (see Third-party dependencies).


Then, add the targets' addresses to the option runtime_dependencies in the [python-protobuf] scope. Pants will use this to automatically add the target(s) to the dependencies field for every protobuf_library() target you write.

# Use the path to your 3rd-party requirements,
# e.g. `3rdparty/python:protobuf`.
runtime_dependencies = ["//:grpcio", "//:protobuf"]

Step 3: Define a protobuf_library target

Wherever you create your .proto files, add a protobuf_library.

# `sources` defaults to `['*.proto']`.
syntax = "proto3";

package project.example;

message Example {

Pants will use dependency inference for any import statements in your .proto files, which you can confirm by running ./pants dependencies path/to/file.proto. You can also manually add to the dependencies field.

If you want gRPC code generated, set grpc=True.


Step 4: Use the protobuf_library in your Python code

Now, you can import the generated Python module in your Python code. For example, to import project/example/f.proto, add import project.example.f_pb2 to your code.

Pants's dependency inference will detect Python imports of Protobuf modules, which you can confirm by running ./pants dependencies path/to/

If gRPC is activated, you can also import the module with _pb2_grpc at the end, e.g. project.example.f_pb2_grpc.

from project.example.f_pb2 import HelloReply
from project.example.f_pb2_grcp import GreeterServicer


Run ./pants export-codegen :: to inspect the files

./pants export-codegen :: will run all relevant code generators and write the files to dist/codegen using the same paths used normally by Pants.

You do not need to run this goal for codegen to work when using Pants; export-codegen is only for external consumption outside of Pants.


You likely need to add empty files

By default, Pants will generate the Python files in the same directory as the .proto file. To get Python imports working properly, you will likely need to add an empty in the same location, and possibly in ancestor directories. You do not need to add a python_library() target; Pants will automatically include the file.

See the below section "Protobuf and source roots" for how to generate into a different directory. If you use this option, you will still likely need an empty file in the destination directory.

Protobuf and source roots

By default, generated code goes into the same source root as the .proto file from which it was generated. For example, a file src/proto/example/f.proto will generate src/proto/example/

However this may not always be what you want. In particular, you may not want to have to add __init__py files under src/proto just so you can import Python code generated to that source root.

You can configure a different source root for generated code by setting the python_source_root field:


Now src/proto/example/f.proto will generate src/python/example/, i.e., the generated files will share a source root with your other Python code.


Set the .proto file's package relative to the source root

Remember that the package directive in your .proto file should be relative to the source root.

For example, if you have a file at src/proto/example/subdir/f.proto, you'd set its package to example.subdir; and in your Python code, from example.subdir import f_pb2.

Updated 2 months ago

Protobuf and gRPC

How to generate Python from Protocol Buffers.

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