Interface with omega|ml to get/put models and objects

The below snippet displays how omega|ml can be used to store and retrieve models and objects.

from omegaml import Omega
om = Omega()
x = np.array(range(10, 20))
y = x * 2
df = pd.DataFrame(dict(x=x, y=y))

# store dataset object
om.datasets.put(X, 'datax')
om.datasets.put(Y, 'datay')

# fit locally and store model for comparison
lr = LinearRegression()
lr.fit(X, Y)
pred = lr.predict(X)
# store the fitted model
om.models.put(lr, 'duplicate')

# for spark models
# create and store spark KMeans model
# for the below to work
# 'pyspark.mllib.clustering.KMeans' must be a working model provided by spark
# further required parameters can be sent to the model for processing using params
om.models.put('pyspark.mllib.clustering.KMeans', 'sparktest', params=dict(k=10))

# retrieve dataset
datax = om.datasets.get('datax')