To be Unpersisted the RDD must be persisted first. If it's set to None, then it's not persisted, and as such does not need to be freed. Does that make sense ?
Thank you, Ilya Ganelin -----Original Message----- From: Stahlman, Jonathan [jonathan.stahl...@capitalone.com<mailto:jonathan.stahl...@capitalone.com>] Sent: Wednesday, July 22, 2015 01:42 PM Eastern Standard Time To: user@spark.apache.org Subject: Re: How to unpersist RDDs generated by ALS/MatrixFactorizationModel Hello again, In trying to understand the caching of intermediate RDDs by ALS, I looked into the source code and found what may be a bug. Looking here: https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala#L230 you see that ALS.train() is being called with finalRDDStorageLevel = StorageLevel.NONE, which I would understand to mean that the intermediate RDDs will not be persisted. Looking here: https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala#L631 unpersist() is only being called on the intermediate RDDs (all the *Blocks RDDs listed in my first post) if finalRDDStorageLevel != StorageLevel.NONE. This doesn’t make sense to me – I would expect the RDDs to be removed from the cache if finalRDDStorageLevel == StorageLevel.NONE, not the other way around. Jonathan From: <Stahlman>, Stahlman Jonathan <jonathan.stahl...@capitalone.com<mailto:jonathan.stahl...@capitalone.com>> Date: Thursday, July 16, 2015 at 2:18 PM To: "user@spark.apache.org<mailto:user@spark.apache.org>" <user@spark.apache.org<mailto:user@spark.apache.org>> Subject: How to unpersist RDDs generated by ALS/MatrixFactorizationModel 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) ________________________________ The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates and may only be used solely in performance of work or services for Capital One. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer. ________________________________________________________ The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates and may only be used solely in performance of work or services for Capital One. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.