python
import dpark
import numpy as np
from sklearn.linear_model import LogisticRegression
dpark.config(port=8000, nCpus=4, mem="16g", retries=5)
X = np.random.rand(1000, 10)
y = np.random.randint(2, size=1000)
with dpark.parallelize([X, y]).as_input:
model = LogisticRegression()
model.fit(X, y)
with dpark.parallelize(X).as_input:
predicted = model.predict(X)
