Segmentation fault using tensorflow or pytorch about scalene HOT 3 CLOSED

The Tensorflow script seems working on Linux
Environment:
python: Python 3.6.4 |Anaconda custom (64-bit)| (default, Jan 16 2018, 18:10:19)
tensorflow: 1.7.8 (CPU)
scalene: installed by pip
System: Ubuntu 18.04
(Memory profiling not enabled)

from scalene.

PyTorch script fails though

Fatal Python error: GC object already tracked

Thread 0x00007f6d7e613700 (most recent call first):

Thread 0x00007f6d7ee14700 (most recent call first):

Current thread 0x00007f6ea0975240 (most recent call first):
  File "test.py", line 31 in forward
  File "/usr/local/share/anaconda3/envs/Tensorflow/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550 in __call__
  File "test.py", line 55 in <module>
  File "/usr/local/share/anaconda3/envs/Tensorflow/lib/python3.6/site-packages/scalene/scalene.py", line 1394 in main
  File "/usr/local/share/anaconda3/envs/Tensorflow/lib/python3.6/site-packages/scalene/__main__.py", line 4 in main
  File "/usr/local/share/anaconda3/envs/Tensorflow/lib/python3.6/site-packages/scalene/__main__.py", line 7 in <module>
  File "/usr/local/share/anaconda3/envs/Tensorflow/lib/python3.6/runpy.py", line 85 in _run_code
  File "/usr/local/share/anaconda3/envs/Tensorflow/lib/python3.6/runpy.py", line 193 in _run_module_as_main

from scalene.

Thanks for the report, @guanh01 ! :)

I tracked down the issue(s), and have successfully run both programs on Mac OS and Linux. You can get the latest version via pip install -U scalene. (A note to you and @GammaPi - by default, scalene now runs with memory profiling -- you have to explicitly disable it with --cpu-only. This is faster but of course does not give you memory usage info.)

The issues were as follows:

1. There was a race in the heap, so I have locked the entire heap for now. I have plans to make the sampling mechanism scale, which is actually somewhat tricky, so I have not yet implemented that. For now, unfortunately, this will degrade the performance of third-party libraries that run with multiple threads.
2. TensorFlow, at least, appears to be sending memory to be freed that wasn't actually allocated via the memory profiling library; I added some additional checks to discard such free calls. Unfortunately, this may leak memory (possibly avoidably).

from scalene.