Is there any way to broadcast sk-learn model?

[allwefantasy]

I hava try some code like this：

from spark_sklearn import GridSearchCV
import cPickle as pickle

session = SparkSession.builder.master("local[2]").appName("test").getOrCreate()

# iris = datasets.load_iris()
# print(iris.target)

documentDF = session.createDataFrame([
    ("Hi I heard about Spark", "spark"),
    ("I wish Java could use case classes", "java"),
    ("Logistic regression models are neat", "mlib"),
    ("Logistic regression models are neat", "spark"),
    ("Logistic regression models are neat", "mlib"),
    ("Logistic regression models are neat", "java"),
    ("Logistic regression models are neat", "spark"),
    ("Logistic regression models are neat", "java"),
    ("Logistic regression models are neat", "mlib")
], ["text", "preds"]).select(f.split("text", "\\s+").alias("new_text"), "preds")

word2vec = Word2Vec(vectorSize=100, minCount=1, inputCol="new_text",
                    outputCol="features")
indexer = StringIndexer(inputCol="preds", outputCol="labels")

pipline = Pipeline(stages=[word2vec, indexer])
ds = pipline.fit(documentDF).transform(documentDF)

data = ds.toPandas()
parameters = {'kernel': ('linear', 'rbf')}
svr = svm.SVC()
clf = GridSearchCV(session.sparkContext, svr, parameters)
X = [x.values for x in data.features.values]
y = [int(x) for x in data.labels.values]
model = clf.fit(X, y)
modelB = session.sparkContext.broadcast(pickle.dumps(model))
wow = documentDF.rdd.map(lambda row: pickle.loads(modelB.value).transform(row["features"].values)).collect()
print(wow)

but the code will fail because sk-learn model can not been pickle. Is there any way to broadcast model?