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https://issues.apache.org/jira/browse/SPARK-6065?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14958585#comment-14958585
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Franck Zhang commented on SPARK-6065:
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When I used the same dataset (text8 - around100mb), same parameters for
training,
python runs 10x faster than spark in my notebook(2015 MacBook Pro 15")
I think the word2vec model in spark still have a long way to go ...
> Optimize word2vec.findSynonyms speed
> ------------------------------------
>
> Key: SPARK-6065
> URL: https://issues.apache.org/jira/browse/SPARK-6065
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
> Assignee: Manoj Kumar
> Fix For: 1.4.0
>
>
> word2vec.findSynonyms iterates through the entire vocabulary to find similar
> words. This is really slow relative to the [gcode-hosted word2vec
> implementation | https://code.google.com/p/word2vec/]. It should be
> optimized by storing words in a datastructure designed for finding nearest
> neighbors.
> This would require storing a copy of the model (basically an inverted
> dictionary), which could be a problem if users have a big model (e.g., 100
> features x 10M words or phrases = big dictionary). It might be best to
> provide a function for converting the model into a model optimized for
> findSynonyms.
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