Github user WeichenXu123 commented on a diff in the pull request: https://github.com/apache/spark/pull/19122#discussion_r137264588 --- Diff: python/pyspark/ml/tuning.py --- @@ -255,18 +257,23 @@ def _fit(self, dataset): randCol = self.uid + "_rand" df = dataset.select("*", rand(seed).alias(randCol)) metrics = [0.0] * numModels + + pool = ThreadPool(processes=min(self.getParallelism(), numModels)) + for i in range(nFolds): validateLB = i * h validateUB = (i + 1) * h condition = (df[randCol] >= validateLB) & (df[randCol] < validateUB) - validation = df.filter(condition) + validation = df.filter(condition).cache() --- End diff -- Hmm... How to checking input dataset caching status is not easy, there are still discussions in `SPARK-18608`. But for now I think we can keep consistent with scala-side to cache both of `training` and `validation` dataset. So I update code.
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