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https://issues.apache.org/jira/browse/SPARK-1006?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15085862#comment-15085862
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Alger Remirata commented on SPARK-1006:
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First of all, I would like to thank you guys for developing spark and putting
it open source that we can use. I'm new to Spark and Scala, and working in a
project involving matrix factorizations in Spark. I have a problem regarding
running ALS in Spark. It has a stackoverflow due to long linage chain as per
comments on the internet. One of their suggestion is to use the
setCheckpointInterval so that for every 10-20 iterations, we can checkpoint the
RDDs and it prevents the error. Just want to ask details on how to do
checkpointing with ALS. I am using spark-kernel developed by IBM:
https://github.com/ibm-et/spark-kernel instead of spark-shell.
Here are some of my specific questions regarding details on checkpoint:
1. In setting checkpoint directory through SparkContext.setCheckPointDir(), it
needs to be a hadoop compatible directory. Can we use any available
hdfs-compatible directory?
2. What do you mean by this comment on the code in ALS checkpointing:
If the checkpoint directory is not set in [[org.apache.spark.SparkContext]],
* this setting is ignored.
3. Is the use of setCheckPointInterval the only code I needed to add to have
checkpointing for ALS work?
4. I am getting this error: Name: java.lang.IllegalArgumentException, Message:
Wrong FS: expected file :///. How can I solve this? What is the proper way of
using checkpointing.
Thanks a lot!
> MLlib ALS gets stack overflow with too many iterations
> ------------------------------------------------------
>
> Key: SPARK-1006
> URL: https://issues.apache.org/jira/browse/SPARK-1006
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Reporter: Matei Zaharia
>
> The tipping point seems to be around 50. We should fix this by checkpointing
> the RDDs every 10-20 iterations to break the lineage chain, but checkpointing
> currently requires HDFS installed, which not all users will have.
> We might also be able to fix DAGScheduler to not be recursive.
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