No memory profiling on windows (WSL or hyperv virtualization)

[farnyser]

Hi,

I can't make the memory profiling works on windows 11 neither using WSL not using a virtualized ubuntu distribution (through hyper-v)
I get Memory timeline: (max: 0.000 MB, growth: 0.0%) whatever the script I'm trying to run.

I'm using

• Scalene version 1.5.31.1
• Python 3.11.5

Any idea how to debug this ?
(Works fine on my personal linux computer, sadly I can't take company code there)

Thanks

[emeryberger]

Can you provide some example code and the command line options you are using?

[farnyser]

It doesn't look to be dependent on the executed code, but for example:
Command line: scalene --cli alloc_profiling.py

Code sample:

import numpy as np

if __name__ == "__main__":
    arr = np.random.random((10_000_000, 10))

On my laptop:

       ╷       ╷       ╷       ╷        ╷       ╷               ╷       ╷                                                                                                                                         
       │Time   │–––––– │–––––– │Memory  │–––––– │–––––––––––    │Copy   │                                                                                                                                         
  Line │Python │native │system │Python  │peak   │timeline/%     │(MB/s) │/home/farnyser/Coding/ng/alloc_profiling.py                                                                                                 
╺━━━━━━┿━━━━━━━┿━━━━━━━┿━━━━━━━┿━━━━━━━━┿━━━━━━━┿━━━━━━━━━━━━━━━┿━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸
     1 │    4% │   26% │   4%  │ 100%   │   10M │▁   1%         │    81 │import numpy as np                                                                                                                       
     2 │       │       │       │        │       │               │       │                                                                                                                                         
     3 │       │       │       │        │       │               │       │if __name__ == "__main__":                                                                                                               
     4 │       │   26% │  40%  │  22%   │  767M │█  99%         │       │    arr = np.random.random((10_000_000, 10))                                                                                             
     5 │       │       │       │        │       │               │       │                                                                                                                                         
       ╵       ╵       ╵       ╵        ╵       ╵               ╵       ╵                                                                                                                                         

On windows (WSL or Hyperv):

       │Time   │–––––– │–––––– │Memory  │–––––– │–––––––––––    │Copy   │                                                                                                                         
  Line │Python │native │system │Python  │peak   │timeline/%     │(MB/s) │/home/sfarny/shared-drives/C:/dev/python/alloc_profiling.py                                                                       
╺━━━━━━┿━━━━━━━┿━━━━━━━┿━━━━━━━┿━━━━━━━━┿━━━━━━━┿━━━━━━━━━━━━━━━┿━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸
     1 │    4% │   37% │       │        │       │               │       │import numpy as np                                                                                                       
     2 │       │       │       │        │       │               │       │                                                                                                                         
     3 │       │       │       │        │       │               │       │if __name__ == "__main__":                                                                                               
     4 │    1% │   52% │   7%  │        │       │               │       │    arr = np.random.random((10_000_000, 10))                                                                             
     5 │       │       │       │        │       │               │       │                                                                                                                         
       ╵       ╵       ╵       ╵        ╵       ╵               ╵       ╵            

[farnyser]

@emeryberger Any idea what could be wrong ? How could I check on my side ?