安装keras和tensorflow之后，python脚本因Segmentation Fault（崩溃的核心）崩溃

Question

Why does import statement (eventually leading to keras import) work only after handling a deliberate exception???????

I have a python3.5 project with multiple main.py entry points.

One entry point runs on server, another on client(s) using pycos to communicate (network IPC).

My server/main.py used to execute fine, but after installing keras and tensorflow with

pip install -r requirements.txt --user

the server/main.py crashes reading

Using TensorFlow backend.
Segmentation fault (core dumped)

I have monitored RAM on the server VM, hard disk space and nothing indicates why server/main.py is crashing... It doesn't even import keras OR tensorflow in the import trace. I guess _ init _.py import trace eventually leads to the module which imports keras / tensorflow packages

Can anyone advise on how to find the issue?

requirements.txt

jsonpickle
matplotlib
seaborn
numpy
pycos
jsonpickle
statsmodels
pandas
sklearn
pymongo
keras
tensorflow==2.0.0-beta1

Answer 1

Turns out pycos executes an underlying scheduler which somehow interferes with Tensorflow (causing seg faults) if the python modules which call pycos are exposed in the _ _ init _ _.py of their respective python sub-packages.

Removing monitoringSimulation/monitor.py etc.. imports from monitoringSimulation/ _ _ init _ _.py fixes the issue.

Will submit a bug to pycos as I will eventually need to expose monitor.py module to other subpackages.