zhengruifeng commented on code in PR #50013:
URL: https://github.com/apache/spark/pull/50013#discussion_r1970737074
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sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLHandler.scala:
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@@ -125,6 +127,15 @@ private[connect] object MLHandler extends Logging {
val dataset = MLUtils.parseRelationProto(fitCmd.getDataset,
sessionHolder)
val estimator =
MLUtils.getEstimator(sessionHolder, estimatorProto,
Some(fitCmd.getParams))
+
+ // pre-training model size check
+ val maxSize =
conf.getConf(Connect.CONNECT_SESSION_ML_CACHE_SINGLE_ITEM_SIZE)
+ if (maxSize > 0) {
+ val estimatedSize = estimator.estimateModelSize(dataset)
Review Comment:
That depends, we are trying to estimate an upper bound of this size. E.g.
> - Given a LogisticRegression estimator, assume the coefficients are dense,
even though the actual fitted model might be sparse (by L1 penalty).
> - Given a tree model, assume all underlying trees are complete binary
trees, even though some branches might be pruned or truncated.
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