With reticulate, xgboost with certain type of hyperparameters cannot be paralleled.

I am building a random forest via xgboost following the instruction at https://xgboost.readthedocs.io/en/latest/tutorials/rf.html; moreover, FLAML (GitHub - microsoft/FLAML: A fast and lightweight AutoML library.) is used for hyperparameter optimization. The python codes are like below:

!pip install flaml;
from flaml.data import load_openml_dataset
from xgboost import XGBClassifier
from flaml.model import BaseEstimator
X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir='./')
from flaml import AutoML
from flaml import tune
automl = AutoML()
num_cores = 16
randomseed = 19850606

class MyMonotonicRandomForestClassifier(BaseEstimator):

def __init__(self, task = 'binary:logistic', n_jobs = num_cores, **params):

    super().__init__(task, **params)

    self.estimator_class = XGBClassifier

    # convert to int for integer hyperparameters
    self.params = {
        'n_jobs': params['n_jobs'] if 'n_jobs' in params else num_cores,
        'booster':params['booster'] if 'booster' in params else 'gbtree',
        'learning_rate': params['learning_rate'] if 'learning_rate' in params else 1,
        'gamma': params['gamma'],
        'reg_lambda': params['reg_lambda'],
        'reg_alpha': params['reg_alpha'],
        'max_depth': int(params['max_depth']),
        'min_child_weight': int(params['min_child_weight']),
        'subsample': params['subsample'],
        'n_estimators':params['n_estimators'] if 'n_estimators' in params else 1,
        'random_state': params['random_state'] if 'random_state' in params else randomseed

def search_space(cls, data_size, task):
    space = {        
    'max_depth': {'domain': tune.uniform(lower=15, upper=100), 'init_value': 30},
    'num_parallel_tree': {'domain': tune.uniform(lower = 5000, upper = 20000), 'init_value': 10000},
    'min_child_weight': {'domain': tune.uniform(lower = 1, upper = 1000), 'init_value': 100},
    'subsample': {'domain': tune.uniform(lower = 0.5, upper = 1), 'init_value': 0.67},
    'colsample_bylevel': {'domain': tune.uniform(lower = 0.7, upper = 1), 'init_value': 0.9},
    'gamma': {'domain': tune.loguniform(lower = 0.000000000001, upper = 0.001), 'init_value': 0.00001},
    'reg_lambda': {'domain': tune.loguniform(lower = 0.000000000001, upper = 1), 'init_value': 1},
     'reg_alpha': {'domain': tune.loguniform(lower = 0.000000000001, upper = 1), 'init_value': 0.000000000001},
    return space

automl.add_learner(learner_name = 'MonotonicRandomForest', learner_class = MyMonotonicRandomForestClassifier)

settings = {
"time_budget": 300, # total running time in seconds
"metric": 'roc_auc',
"estimator_list": ['MonotonicRandomForest'], # list of ML learners
"task": 'classification', # task type
"log_file_name": 'airlines_experiment_custom.log', # flaml log file
"log_training_metric": True, # whether to log training metric

automl.fit(X_train = X_train, y_train = y_train,
X_val = X_test, y_val = y_test, **settings)

When running the codes above in Juypter on a Linux server using centos, from the backend I can see that 16 CPUs are activated and used for the model (num_cores = 16 as set) .

However, when I run the same codes in RStudio via reticulate, no matter how I set num_cores, only one CPU is active, indicating that the process becomes single-threaded.

In addition, for a 'traditional' xgboost with multiple rounds and num_parallel_tree = 1, there is no such issue in reticulate. The equivalent R codes also run well with multi-cores in RStudio.

Any ideas? The process takes a lot of time after becoming single-threaded, which is very unfavorable for my work.

Your help will be highly appreciated.

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.