I am programming a C++ extension for Python and I am using distutils to compile the project. As the project grows, rebuilding it takes longer and longer. Is there a way to speed up the build process?
I read that parallel builds (as with make -j
) are not possible with distutils. Are there any good alternatives to distutils which might be faster?
I also noticed that it's recompiling all object files every time I call python setup.py build
, even when I only changed one source file. Should this be the case or might I be doing something wrong here?
In case it helps, here are some of the files which I try to compile: https://gist.github.com/2923577
Thanks!
Try building with environment variable CC="ccache gcc"
, that will speed up build significantly when the source has not changed. (strangely, distutils uses CC
also for c++ source files). Install the ccache package, of course.
Since you have a single extension which is assembled from multiple compiled object files, you can monkey-patch distutils to compile those in parallel (they are independent) - put this into your setup.py (adjust the N=2
as you wish):
# monkey-patch for parallel compilation
def parallelCCompile(self, sources, output_dir=None, macros=None, include_dirs=None, debug=0, extra_preargs=None, extra_postargs=None, depends=None):
# those lines are copied from distutils.ccompiler.CCompiler directly
macros, objects, extra_postargs, pp_opts, build = self._setup_compile(output_dir, macros, include_dirs, sources, depends, extra_postargs)
cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
# parallel code
N=2 # number of parallel compilations
import multiprocessing.pool
def _single_compile(obj):
try: src, ext = build[obj]
except KeyError: return
self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)
# convert to list, imap is evaluated on-demand
list(multiprocessing.pool.ThreadPool(N).imap(_single_compile,objects))
return objects
import distutils.ccompiler
distutils.ccompiler.CCompiler.compile=parallelCCompile
For the sake of completeness, if you have multiple extensions, you can use the following solution:
import os
import multiprocessing
try:
from concurrent.futures import ThreadPoolExecutor as Pool
except ImportError:
from multiprocessing.pool import ThreadPool as LegacyPool
# To ensure the with statement works. Required for some older 2.7.x releases
class Pool(LegacyPool):
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
self.join()
def build_extensions(self):
"""Function to monkey-patch
distutils.command.build_ext.build_ext.build_extensions
"""
self.check_extensions_list(self.extensions)
try:
num_jobs = os.cpu_count()
except AttributeError:
num_jobs = multiprocessing.cpu_count()
with Pool(num_jobs) as pool:
pool.map(self.build_extension, self.extensions)
def compile(
self, sources, output_dir=None, macros=None, include_dirs=None,
debug=0, extra_preargs=None, extra_postargs=None, depends=None,
):
"""Function to monkey-patch distutils.ccompiler.CCompiler"""
macros, objects, extra_postargs, pp_opts, build = self._setup_compile(
output_dir, macros, include_dirs, sources, depends, extra_postargs
)
cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
for obj in objects:
try:
src, ext = build[obj]
except KeyError:
continue
self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)
# Return *all* object filenames, not just the ones we just built.
return objects
from distutils.ccompiler import CCompiler
from distutils.command.build_ext import build_ext
build_ext.build_extensions = build_extensions
CCompiler.compile = compile