Closed praveenjana closed 5 months ago
Hi @praveenjana,
Can you please the entire code snippet so that we can reproduce the issue on our side? Including how you make these two models run in parallel. Thank you!
Hi @madtoinou , Thanks for the prompt response.
The below file has the Python code. I have renamed as txt file as git is not allowing me to upload .py file. reproduction_code_python.txt Below are the log files. threads_1.txt threads_2.txt
Steps to follow:
python reproduction_code_python.py 1
python reproduction_code_python.py 2
Please let me know any further details are needed from my end.
Thanks, Praveen.
@praveenjana,
My guess is that the over-head associated with multi-threading makes it not worth it for your example and I don't think there is much that we can do at the Darts level to remediate this.
On another note, the deep learning models (including NBEATSModel
) training can be accelerated by allocating workers to the dataloader (here). Similarly, a lot of the regression models expose the 'n_jobs
parameter to optimize the usage of the threads. I would recommend exploring these directions.
thanks @madtoinou .
Describe the bug N-Beats and xgboost models are taking more time when executing in parallel. If I am using single process, each algorithm is executing within one sec. Where as If I increase process count to 2(or >1) each algorithm is taking 15sec to complete execution for the same data.
To Reproduce Models are initiated like below.
Expected behavior N-Beats and Xgboost should take same amount of time while executing in paralell System (please complete the following information):
Additional context Is there some parameter to tune these models? so that execution time will reduce