Iterate a logistic regression code over different training datas in pyspark

Question

I want to iterate the following sample logistic regression code over different training data that are stored in different files using pyspark:

from pyspark.mllib.classification import LogisticRegressionWithLBFGS,LogisticRegressionModel
from pyspark.mllib.regression import LabeledPoint

# Load and parse the data
def parsePoint(line):
    values = [float(x) for x in line.split(' ')]
    return LabeledPoint(values[0], values[1:])

data = sc.textFile("data/mllib/sample_svm_data.txt")
parsedData = data.map(parsePoint)

# Build the model
model = LogisticRegressionWithLBFGS.train(parsedData)

# Evaluating the model on training data
labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() /    float(parsedData.count())
print("Training Error = " + str(trainErr))

# Save and load model
model.save(sc, "target/tmp/pythonLogisticRegressionWithLBFGSModel")
sameModel = LogisticRegressionModel.load(sc, "target/tmp/pythonLogisticRegressionWithLBFGSModel")

I would then like to save all the models generated. Any ideas on how I can do this?