Hello,
I have noticed that the option in the Khiops Python library to set a maximum number of cores for fitting and predicting does not seem to work when using the scikit-learn interface (khc.fit(X_train, y_train,max_cores=5)) or when setting the environment variable os.environ["KHIOPS_PROC_NUMBER"] = "5".
This is particularly problematic when running Khiops in parallel or concurrently, for example, when multiple tools or processes are using Khiops simultaneously. The inability to limit core usage can lead to resource contention and degraded performance.
Could you please look into this issue? It would be great to have this core limiting feature work as expected across different usage scenarios.
Thank you.
Hello,
I have noticed that the option in the Khiops Python library to set a maximum number of cores for fitting and predicting does not seem to work when using the scikit-learn interface (khc.fit(X_train, y_train,max_cores=5)) or when setting the environment variable os.environ["KHIOPS_PROC_NUMBER"] = "5".
This is particularly problematic when running Khiops in parallel or concurrently, for example, when multiple tools or processes are using Khiops simultaneously. The inability to limit core usage can lead to resource contention and degraded performance.
Could you please look into this issue? It would be great to have this core limiting feature work as expected across different usage scenarios.
Thank you.