xlearn python API's .predict method in doesn't kill the created threads after execution in python API, which leads to resource exhausted.

[HovhannesManushyan]

I was getting strange resource exhausted bug when running xlearn fm model predict method for a while.

When I profiled the processes via htop, I have noticed that the number of threads gradually increases by 8 when invoking model.predict("model/model.out", f"output/output.txt") which leads to resource exhausted when the number of threads reaches a critical level.

One solution, I found to solve this problem is invoke the model.predict in a separate process via the multiprocessing module, however this solution is extremely slow in cases when model.predict needs to be invoked many times.

Is there a way to kill the created threads after the execution of the predict method has completed?

[HovhannesManushyan]

This problem could be solved by building the command line xlearn and then executing the binary using Python's subprocess module.

[litchi6666]

we have the same problem！