Github user auskalia commented on the issue:
https://github.com/apache/spark/pull/17919
Hi @mpjlu , your are right. But I consider that sometimes we have to use
several spark mission to finish our work, especially the resource is
insufficient in hadoop cluster. Due to save and reload
Github user mpjlu commented on the issue:
https://github.com/apache/spark/pull/17919
Hi @auskalia , you are right. repartition can improve the performance of
recommendForAll.
In my experiment for PR 17742, I have 120 cores, I use 20 partition for
userFeatures, and itemFeatures.
Github user auskalia commented on the issue:
https://github.com/apache/spark/pull/17919
Hi, @MLnick, We find that just do repartition for userFeatures and
productFeatures can improve the efficiency significantly on the ALS
recommendForAll().
Here is our procedure:
1.
Github user MLnick commented on the issue:
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Merged to master/branch-2.2
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Github user MLnick commented on the issue:
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Jenkins retest this please
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Github user MLnick commented on the issue:
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Just decided to use `ml.BLAS` and expose `f2jBLAS` as `m / mllib private`.
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Github user jkbradley commented on the issue:
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taking a look
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Github user mpjlu commented on the issue:
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Thanks, I am ok for this change.
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Github user MLnick commented on the issue:
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cc @mpjlu @jkbradley
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