Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/18281#discussion_r128632376
--- Diff: python/pyspark/ml/classification.py ---
@@ -1560,8 +1581,9 @@ def trainSingleClass(index):
(classifier.predictionCol, predictionCol)])
return classifier.fit(trainingDataset, paramMap)
- # TODO: Parallel training for all classes.
- models = [trainSingleClass(i) for i in range(numClasses)]
+ pool = ThreadPool(processes=min(self.getParallelism(), numClasses))
--- End diff --
So in Scala the threadpool is cached, here we aren't doing that and I think
its a bit more heavy weight in Python so we might want to consider if there is
a reasonable way to reuse (if not that's probably OK to since this overhead
pales in comparison to training serially).
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