[
https://issues.apache.org/jira/browse/SPARK-5560?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joseph K. Bradley resolved SPARK-5560.
--------------------------------------
Resolution: Fixed
Fix Version/s: 1.4.0
Target Version/s: (was: 1.5.0)
> LDA EM should scale to more iterations
> --------------------------------------
>
> Key: SPARK-5560
> URL: https://issues.apache.org/jira/browse/SPARK-5560
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
> Assignee: Joseph K. Bradley
> Fix For: 1.4.0
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> Latent Dirichlet Allocation (LDA) sometimes fails to run for many iterations
> on large datasets, even when it is able to run for a few iterations. It
> should be able to run for as many iterations as the user likes, with proper
> persistence and checkpointing.
> Here is an example from a test on 16 workers (EC2 r3.2xlarge) on a big
> Wikipedia dataset:
> * 100 topics
> * Training set size: 4072243 documents
> * Vocabulary size: 9869422 terms
> * Training set size: 1041734290 tokens
> It runs for about 10-15 iterations before failing, even when using a variety
> of checkpointInterval values and longer timeout settings (up to 5 minutes).
> The failure varies from disconnections from workers/driver to workers running
> out of disk space. I would not expect workers to run out of memory or disk
> space based on rough calculations. There was some job imbalance, but not a
> significant amount.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
