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I'm using CPU-only Thundersvm on a centos colony, each of the calculating node has 28 cores. But I got a problem when I try to optimize my parameters when using RandomizedSearchCV, beacuse I do not know how to set the 'n_jobs' value in SVC() and in RandomizedSearchCV(). The former seems to allow each svm_model to utlize n_jobs numbers of threads, and the latter seems to allow each parameters combination utlize n_jobs numbers of PIDs.
My problem is how to set these two n_jobs to aplly to my calculating node cores restrictions? And if these two n_jobs have different meanings? By the way, I noticed in another issue saying that ThunderSVM do not support gridsearchcv's n_jobs in skcit, is this true?
I'm using CPU-only Thundersvm on a centos colony, each of the calculating node has 28 cores. But I got a problem when I try to optimize my parameters when using RandomizedSearchCV, beacuse I do not know how to set the 'n_jobs' value in SVC() and in RandomizedSearchCV(). The former seems to allow each svm_model to utlize n_jobs numbers of threads, and the latter seems to allow each parameters combination utlize n_jobs numbers of PIDs.
My problem is how to set these two n_jobs to aplly to my calculating node cores restrictions? And if these two n_jobs have different meanings? By the way, I noticed in another issue saying that ThunderSVM do not support gridsearchcv's n_jobs in skcit, is this true?
My codes are as follows:
#ThunderSVM
parameters = {
'C': [1, 5, 9],
'gamma': [0.00001, 0.0001, 0.001, 0.1],
'kernel': ['rbf']
}
#I just set both of them into '5'
svm_model = SVC(kernel='rbf', probability=False, random_state=42, max_iter=1000000, n_jobs = 5,verbose = 1)
rdsearch = RandomizedSearchCV(estimator = svm_model, param_distributions = parameters, n_iter = 10, cv = 3, n_jobs = 5,verbose = 1, random_state = 42)
#train model
rdsearch.fit(X_train, y_train)
print(f"Best parameters: {rdsearch.best_params_}")
print(f"Best score: {rdsearch.best_score_}")
#save model
import joblib
joblib.dump(rdsearch, "ThunderSVM_model.joblib")
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