Training gets stuck after some epochs when using Tensorflow with multiprocessing

[n-Guard]

Describe the bug

I'm using Keras/Tensorflow and the training stalls indefinitely after some epochs when I enable multiprocessing.
It happend only when I used LSTM or TimeDistributed layers. Dense and Conv layers alone don't seem to have this problem.
Without ClearML everything works fine.

To reproduce

Start a training with Tensorflow and multiprocessing enabled.
Choose a model with LSTM and/or TimeDistributed layers.

I provided a script, the bug happens mostly within the first 100 epochs:
https://gist.github.com/n-Guard/0f5d568cfedb3a22bfa56785e82961ad

Expected behaviour

The training should continue without getting stuck.

Environment

• Server type: self hosted
• ClearML SDK Version: clearml-agent==1.7.0 clearml==1.14.4
• ClearML Server Version: 1.14.1
• Tensorflow Version: 2.15.0
• Python Version: 3.11
• OS: Linux

[eajechiloae]

Hi @n-Guard ! We managed to reproduce this. It is not clear why it happens. In the meantime, you could try calling the following snippet at the very beginning of your script:

try:
    import multiprocessing
    multiprocessing.set_start_method("spawn")
except Exception:
    pass

What it does: it makes python use spawn instead of fork when creating a new process, so the state of the locks, queues etc will not be copied on the child processes.
Not 100% sure if it will help.