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https://issues.apache.org/jira/browse/SPARK-19247?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15824820#comment-15824820
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Asher Krim commented on SPARK-19247:
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Good question. I've seen it come up before
(http://stackoverflow.com/questions/40842736/spark-word2vecmodel-exceeds-max-rpc-size-for-saving).
Additionally, the issue from SPARK-11994 is unpatched in ml, so loading large
models currently requires setting a large `spark.kryoserializer.buffer.max`.
(Personally, I've been on a goose chase fighting OOM's while saving large
ml.word2vec models (Spark 1.6.3). This seemed like a good place to start
digging into it. However in further testing, it looks like my issue may stem
from CatalystTypeConverters)
I'm happy to follow any backwards compatibility guidelines.
> improve ml word2vec save/load
> -----------------------------
>
> Key: SPARK-19247
> URL: https://issues.apache.org/jira/browse/SPARK-19247
> Project: Spark
> Issue Type: Bug
> Reporter: Asher Krim
>
> ml word2vec models can be somewhat large (~4gb is not uncommon). The current
> save implementation saves the model as a single large datum, which can cause
> rpc issues and fail to save the model.
> On the loading side, there are issues with loading this large datum as well.
> This was already solved for mllib word2vec in
> https://issues.apache.org/jira/browse/SPARK-11994, but the change was never
> ported to the ml word2vec implementation.
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