I have a Python project that uses setuptools for deployment and I mostly followed this guide regarding project structure. The project uses Google Protocol Buffers to define a network message format. My main issue is how to make setup.py call the protoc-compiler during installation to build the definitions into a _pb2.py file.
In this question the advice was given to just distribute the resulting _pb2.py files along with the project. While this might work for very similar platforms, I've found several cases where this does not work. For example, when I develop on a Mac that uses Anaconda Python and copy the resulting _pb2.py along with the rest of the project to a Raspberry Pi running Raspbian, there are always import errors coming from the _pb2.py modules. However, if I compile the .proto files freshly on the Pi, the project works as expected. So, distributing the compiled files does not seem like an option.
Kind of looking for working and best practice solutions here. It can be assumed that the protoc-compiler is installed on the target platform.
Edit:
Since people ask for the reasons of the failure. On the Mac, the protobuf version is 2.6.1. and on the Pi it's 2.4.1. Apparently, the internal API as used by the generated protoc compiler output has changed. The output is basically:
File "[...]network_manager.py", line 8, in <module>
import InstrumentControl.transports.serial_bridge_protocol_pb2 as protocol
File "[...]serial_bridge_protocol_pb2.py", line 9, in <module>
from google.protobuf import symbol_database as _symbol_database
ImportError: cannot import name symbol_database
Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler. You define how you want your data to be structured once … The ProtoBuf interface describes the structure of the data to be sent.
Protocol buffers provide a language-neutral, platform-neutral, extensible mechanism for serializing structured data in a forward-compatible and backward-compatible way. It's like JSON, except it's smaller and faster, and it generates native language bindings.
Picking the Right Format Protobuf, on the other hand, usually compresses data better and has built-in protocol documentation via the schema. Another major factor is the CPU performance — the time it takes for the library to serialize and deserializes a message.
Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler.
Ok, I solved the issue without requiring the user to install a specific old version or compile the proto files on another platform than my dev machine. It's inspired by this setup.py script from protobuf itself.
Firstly, protoc needs to be found, this can be done using
# Find the Protocol Compiler.
if 'PROTOC' in os.environ and os.path.exists(os.environ['PROTOC']):
protoc = os.environ['PROTOC']
else:
protoc = find_executable("protoc")
This function will compile a .proto file and put the _pb2.py in the same spot. However, the behavior can be changed arbitrarily.
def generate_proto(source):
"""Invokes the Protocol Compiler to generate a _pb2.py from the given
.proto file. Does nothing if the output already exists and is newer than
the input."""
output = source.replace(".proto", "_pb2.py")
if (not os.path.exists(output) or
(os.path.exists(source) and
os.path.getmtime(source) > os.path.getmtime(output))):
print "Generating %s..." % output
if not os.path.exists(source):
sys.stderr.write("Can't find required file: %s\n" % source)
sys.exit(-1)
if protoc == None:
sys.stderr.write(
"Protocol buffers compiler 'protoc' not installed or not found.\n"
)
sys.exit(-1)
protoc_command = [ protoc, "-I.", "--python_out=.", source ]
if subprocess.call(protoc_command) != 0:
sys.exit(-1)
Next, the classes _build_py and _clean are derived to add building and cleaning up the protocol buffers.
# List of all .proto files
proto_src = ['file1.proto', 'path/to/file2.proto']
class build_py(_build_py):
def run(self):
for f in proto_src:
generate_proto(f)
_build_py.run(self)
class clean(_clean):
def run(self):
# Delete generated files in the code tree.
for (dirpath, dirnames, filenames) in os.walk("."):
for filename in filenames:
filepath = os.path.join(dirpath, filename)
if filepath.endswith("_pb2.py"):
os.remove(filepath)
# _clean is an old-style class, so super() doesn't work.
_clean.run(self)
And finally, the parameter
cmdclass = { 'clean': clean, 'build_py': build_py }
needs to be added to the call to setup and everything should work. Still have to check for possible quirks, but so far it works flawlessly on the Mac and on the Pi.
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