There appear to be 6 leaked semaphore objects to clean up at shutdown warnings.warn('resource_tracker: There appear to be %d

Question

I'm trying a test connection on my Firebase Realtime database via python 3.8. I have two scripts, one is wdata (write data) and the other one is rdata (read data). The wdata.py is:

from firebase import firebase
firebase = firebase.FirebaseApplication("https://test-282f7.firebaseio.com/", None)
datos={
        'id':'99',
        'primer_sensor':'1111',
        'segundo_sensor':'512'
        } 
resultado=firebase.post('/tutorial_firebase/datos_post', datos)
read = firebase.get('/tutorial_firebase/datos_post', datos)

This script returns the same error but it inserts "datos" values in firebase.

The rdata.py is:

from firebase import firebase 
firebase = firebase.FirebaseApplication("https://test- 282f7.firebaseio.com/", None) 
lectura = firebase.get('/tutorial_firebase/datos_post', datos_post) 
print (lectura)

And this code also returns an error. The error is:

/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 6 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '

Please can anyone tell me where is the error and how can I fix it?

p.s.:

My python compiler is: Python 3.8.2. (with 3.7 I install firebase but it returns "ModuleNotFoundError") I'm on macOS Catalina 10.15.7 Tried to compile in VS Code and MacVIM but the result is the same.

Thank you advance!

Solution 1

I had the same problem as you when doing deep learning and the problem came from that I was loading too much data in memory. Be sure you don't try to load more data in your RAM than it's capacity.

Solution 2

If this problem has occurred in training (deep learning), it's because of ram capacity. Use a smaller value for -batch parameter.

Comments

[Genarito]

This issue seems to be reported on the official Python bugs site: link

[TripleAntigen]

Thanks, I just reduced the batch size and tried again, and it worked.

[slhck]

In my case, this was caused by using multiprocessing.Queue without a reasonable maximum size. Too much data was being put into the queue at once. Instantiate it with multiprocessing.Queue(1000) or however many items you want in there.