Hi Daniel The bottleneck in Spark ML is most likely (a) the fact that the weight vector itself is dense, and (b) the related communication via the driver. A tree aggregation mechanism is used for computing gradient sums (see https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala#L1080 and https://databricks.com/blog/2014/09/22/spark-1-1-mllib-performance-improvements.html), which helps efficiency, but ultimately the driver must collect that gradient vector and re-broadcast the updated weight vector on every iteration.
>From a quick glance, MultivariateOnlineSummarizer doesn't seem optimized to sparse data either (though that is only one pass of the dataset so doubtful it adds too much overhead). It would be helpful to understand some further details: 1. Some more exact timing numbers - if you could provide screenshots / output from the UI to indicate the stages and call sites where the time is being spent that would be really useful 2. Is this part of a pipeline, and if so, what is the contribution of other parts of that pipeline to overall runtime? 3. Some stats on input / output data sizes from the critical stages (again from the UI) 4. The dataset size (# examples, avg sparsity % per example, etc) 5. Related to (4), the number of partitions of your dataset 6. Cluster details (# nodes and spec), as well as Spark version If you have a lot of partitions, you could find performance will be better with fewer partitions because the communication overhead will tend to dominate the overall runtime. Still, 10 hours and >100GB of driver memory seems extreme for a 20 million size dense weight vector (which should only be a few 100MB memory), so perhaps something else is going on. Nick On Tue, 8 Mar 2016 at 22:55 Daniel Siegmann <daniel.siegm...@teamaol.com> wrote: > Just for the heck of it I tried the old MLlib implementation, but it had > the same scalability problem. > > Anyone familiar with the logistic regression implementation who could > weigh in? > > On Mon, Mar 7, 2016 at 5:35 PM, Michał Zieliński < > zielinski.mich...@gmail.com> wrote: > >> We're using SparseVector columns in a DataFrame, so they are definitely >> supported. But maybe for LR some implicit magic is happening inside. >> >> On 7 March 2016 at 23:04, Devin Jones <devin.jo...@columbia.edu> wrote: >> >>> I could be wrong but its possible that toDF populates a dataframe which >>> I understand do not support sparsevectors at the moment. >>> >>> If you use the MlLib logistic regression implementation (not ml) you can >>> pass the RDD[LabeledPoint] data type directly to the learner. >>> >>> >>> http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS >>> >>> Only downside is that you can't use the pipeline framework from spark >>> ml. >>> >>> Cheers, >>> Devin >>> >>> >>> >>> On Mon, Mar 7, 2016 at 4:54 PM, Daniel Siegmann < >>> daniel.siegm...@teamaol.com> wrote: >>> >>>> Yes, it is a SparseVector. Most rows only have a few features, and all >>>> the rows together only have tens of thousands of features, but the vector >>>> size is ~ 20 million because that is the largest feature. >>>> >>>> On Mon, Mar 7, 2016 at 4:31 PM, Devin Jones <devin.jo...@columbia.edu> >>>> wrote: >>>> >>>>> Hi, >>>>> >>>>> Which data structure are you using to train the model? If you haven't >>>>> tried yet, you should consider the SparseVector >>>>> >>>>> >>>>> http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector >>>>> >>>>> >>>>> On Mon, Mar 7, 2016 at 4:03 PM, Daniel Siegmann < >>>>> daniel.siegm...@teamaol.com> wrote: >>>>> >>>>>> I recently tried to a model using >>>>>> org.apache.spark.ml.classification.LogisticRegression on a data set >>>>>> where the feature vector size was around ~20 million. It did *not* >>>>>> go well. It took around 10 hours to train on a substantial cluster. >>>>>> Additionally, it pulled a lot data back to the driver - I eventually set >>>>>> --conf >>>>>> spark.driver.memory=128g --conf spark.driver.maxResultSize=112g when >>>>>> submitting. >>>>>> >>>>>> Attempting the same application on the same cluster with the feature >>>>>> vector size reduced to 100k took only ~ 9 minutes. Clearly there is an >>>>>> issue with scaling to large numbers of features. I'm not doing anything >>>>>> fancy in my app, here's the relevant code: >>>>>> >>>>>> val lr = new LogisticRegression().setRegParam(1) >>>>>> val model = lr.fit(trainingSet.toDF()) >>>>>> >>>>>> In comparison, a coworker trained a logistic regression model on her >>>>>> *laptop* using the Java library liblinear in just a few minutes. >>>>>> That's with the ~20 million-sized feature vectors. This suggests to me >>>>>> there is some issue with Spark ML's implementation of logistic regression >>>>>> which is limiting its scalability. >>>>>> >>>>>> Note that my feature vectors are *very* sparse. The maximum feature >>>>>> is around 20 million, but I think there are only 10's of thousands of >>>>>> features. >>>>>> >>>>>> Has anyone run into this? Any idea where the bottleneck is or how >>>>>> this problem might be solved? >>>>>> >>>>>> One solution of course is to implement some dimensionality reduction. >>>>>> I'd really like to avoid this, as it's just another thing to deal with - >>>>>> not so hard to put it into the trainer, but then anything doing scoring >>>>>> will need the same logic. Unless Spark ML supports this out of the box? >>>>>> An >>>>>> easy way to save / load a model along with the dimensionality reduction >>>>>> logic so when transform is called on the model it will handle the >>>>>> dimensionality reduction transparently? >>>>>> >>>>>> Any advice would be appreciated. >>>>>> >>>>>> ~Daniel Siegmann >>>>>> >>>>> >>>>> >>>> >>> >> >
