parallel is still running even after each model fits. 60GB ram, cpu-per-task ==60

[kalkite]

Hello, it's wrong issue. i don't know how to contact you . but i created issue to contact for my problem.

def dt_hyper_parameter_model(df,feature_number,data_samples):
    class_name = df.iloc[:, -1].name
    #print(class_name)
    le = LabelEncoder()
    df[class_name] = le.fit_transform(df.iloc[:, -1])
    data = df.groupby(class_name).apply(lambda x: x.sample(n=data_samples)).reset_index(drop=True)
    X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, 0:feature_number], data.iloc[:, -1], test_size=0.2,random_state=42)   
    params = {
        "criterion": ("gini", "entropy"),
        "splitter": ("best", "random"),
        "max_depth": (list(range(1, 50))),
        "min_samples_split": [2,3,4,5,6,7,8,9,10],
        'max_features': ['auto', 'sqrt', 'log2'],
        "min_samples_leaf": list(range(1, 50)),
    }
    
    tree_clf = DecisionTreeClassifier(random_state=42)
    tree_cv = GridSearchCV(tree_clf, params, scoring="accuracy",verbose=1, cv=2,n_jobs=-1)
    tree_cv.fit(X_train, y_train)
    
    best_params = tree_cv.best_params_
    dt_model = DecisionTreeClassifier(**best_params)
    return dt_model.fit(X_train, y_train)
result = Parallel(n_jobs=9 ,backend="threading")(delayed(dt_hyper_parameter_model)(class3_df[i], 2,5 ) for i in range(len(class3_df)))

I trying to fit the model for model for the each dataframe of class3df . but result variable still continouly running even after fitting the model
could you please tell me the what the problem is it. I am sorry for posting the issue here.