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https://issues.apache.org/jira/browse/SPARK-10078?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15819964#comment-15819964
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Weichen Xu commented on SPARK-10078:
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[~debasish83] But when we implement VF-LBFGS/VF-OWLQN base on spark, we found
that many optimizations need to combine spark features and the optimizer
algorithm closely, make a abstract interface supporting distributed vector (for
example, Vector space operator include dot, add, scale, persist/unpersist
operators and so on...) seems not enough.
I give two simple problem to show the complexity when considering general
interface:
1. Look this VF-OWLQN implementation based on spark:
https://github.com/yanboliang/spark-vlbfgs/blob/master/src/main/scala/org/apache/spark/ml/optim/VectorFreeOWLQN.scala
We know that OWLQN internal will help compute the pseudo-gradient for L1 reg,
look the code function `calculateComponentWithL1`, here when computing
pseudo-gradient using RDD, it also use an accumulator(only spark have) to
calculate the adjusted fnValue, so that will the abstract interface containing
something about `accumulator` in spark ?
2. About persist, unpersist, checkpoint problem in spark. Because of spark lazy
computation feature, improper persist/unpersist/checkpoint order may cause
serious problem (may cause RDD recomputation, checkpoint take no effect and so
on), about this complexity, we can take a look into the VF-BFGS implementation
on spark:
it use the pattern "persist current step RDDs, then unpersist previous step
RDDs" like many other algos in spark mllib. The complexity is at, spark always
do lazy computation, when you persist RDD, it do not persist immediately, but
postponed to RDD.action called. If the internal code call `unpersist` too
early, it will cause the problem that an RDD haven't been computed and haven't
been persisted, but already been unpersisted.
This feature may be much different than other distributed platform, so that a
general interface can really handle this problem correctly and still keep high
efficient in the same time?
[~sethah] Do you consider this detail problems when you designing the general
optimizer interface ?
> Vector-free L-BFGS
> ------------------
>
> Key: SPARK-10078
> URL: https://issues.apache.org/jira/browse/SPARK-10078
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Xiangrui Meng
> Assignee: Yanbo Liang
>
> This is to implement a scalable version of vector-free L-BFGS
> (http://papers.nips.cc/paper/5333-large-scale-l-bfgs-using-mapreduce.pdf).
> Design document:
> https://docs.google.com/document/d/1VGKxhg-D-6-vZGUAZ93l3ze2f3LBvTjfHRFVpX68kaw/edit?usp=sharing
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