cx-freeze, runpy and multiprocessing - multiple paths to failure

I used cx_freeze for a project at work. I'm not sure if this is your problem... but I was using the Anaconda distribution, and cx_freeze was not properly gathering the .dll's that I needed for my project.

The solution was to:

1. Install a plane version of Python
2. make an environment with the packages that I needed for that project
3. Run cx_freeze.

Magically, all of the problems went away and everything compiled like it was supposed too.

I know that probably this is not what you want to hear, but the reality is that you will never be able to succeed with this approach and here is why:

• Linux does not have portably Python package binaries, meaning that a compiled numpy on one distro will probably not work on another. It can even happen with the same distribution and version, just because one system library received an update.
• manylinux1 is supposed to solve this but is really hard to build packages for it and only few started using it.
• cx-freeze project had latest release in December 2014, which on Python timescale puts it as an abandoned project.

Well the problem you are facing may be due to the lack of dependencies.cx_Freeze have some dependencies.In order to distribute your work in python many lightweight packages are avalible.You can use:-

1. Pyinstaller: it can be installed by

   pip install pyinstallerpyinstaller is really good for making executables with less size

2. Py2exe