Loading large trained machine learning model with joblib

I am trying to train the Radom Forest classifier.

Here is my code:

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction import DictVectorizer
import pickle

tr_features = pickle.load(open('./dataset/train_features.dump', 'rb'))
tr_labels = pickle.load(open('./dataset/train_labels.dump', 'rb'))


vectorizer = DictVectorizer()  
vectorizer.fit(tr_features)                                    
vectorized_features = vectorizer.transform(tr_features)  


rf = RandomForestClassifier()
parameters = {
    'n_estimators': [5, 50, 250],
    'max_depth': [2, 4, 8, 16, 32, None]
}

cv = GridSearchCV(rf, parameters, cv=5)
cv.fit(vectorized_features, tr_labels)

After completion of training, I dumped the model with the joblib as follow:

import joblib
joblib.dump(cv.best_estimator_, './models/RF_model.pkl')

Later, when I load the model to run it on test data

import joblib
rf = joblib.load('./models/RF_model.pkl')

When I run the above line, the Jupuyter notebook's kernel restarts. When I checked the RF_model.pkl file size, it is 422.7MB.

I tried this solution, and passed the compress=3 argument to the joblib.dump() method.

joblib.dump(cv.best_estimator_, './models/RF_model.pkl',compress=3)

Even though the size changed to 43MB but still the kernel restarts and I cannot load the model.

Note: I am running the Jupyter notebook from a docker container.