Hello all,
I am running the Spark recommendation algorithm in MLlib and I have been
studying its output with various model configurations. Ideally I would like to
be able to run one job that trains the recommendation model with many different
configurations to try to optimize for performance. A sample code in python is
copied below.
The issue I have is that each new model which is trained caches a set of RDDs
and eventually the executors run out of memory. Is there any way in Pyspark to
unpersist() these RDDs after each iteration? The names of the RDDs which I
gather from the UI is:
itemInBlocks
itemOutBlocks
Products
ratingBlocks
userInBlocks
userOutBlocks
users
I am using Spark 1.3. Thank you for any help!
Regards,
Jonathan
data_train, data_cv, data_test = data.randomSplit([99,1,1], 2)
functions = [rating] #defined elsewhere
ranks = [10,20]
iterations = [10,20]
lambdas = [0.01,0.1]
alphas = [1.0,50.0]
results = []
for ratingFunction, rank, numIterations, m_lambda, m_alpha in
itertools.product( functions, ranks, iterations, lambdas, alphas ):
#train model
ratings_train = data_train.map(lambda l: Rating( l.user, l.product,
ratingFunction(l) ) )
model = ALS.trainImplicit( ratings_train, rank, numIterations,
lambda_=float(m_lambda), alpha=float(m_alpha) )
#test performance on CV data
ratings_cv = data_cv.map(lambda l: Rating( l.uesr, l.product,
ratingFunction(l) ) )
auc = areaUnderCurve( ratings_cv, model.predictAll )
#save results
result = ",".join(str(l) for l in
[ratingFunction.__name__,rank,numIterations,m_lambda,m_alpha,auc])
results.append(result)
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