from joblib import parallel_backend
import numpy as np
from dask.distributed import Client
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
digits = load_digits()
from sklearn.model_selection import cross_val_score
client=Client(processes=None)
clf = RandomForestClassifier(n_estimators=45000,verbose=1)
X = np.concatenate((digits.data,digits.data),axis=0)
y = np.concatenate((digits.target,digits.target))
with parallel_backend('dask'):
    clf.fit(X,y)