Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/14519#discussion_r73832261 --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala --- @@ -478,21 +482,23 @@ object AFTSurvivalRegressionModel extends MLReadable[AFTSurvivalRegressionModel] * $$ * </blockquote></p> * - * @param parameters including three part: The log of scale parameter, the intercept and - * regression coefficients corresponding to the features. + * @param bcParameters The broadcasted value includes three part: The log of scale parameter, + * the intercept and regression coefficients corresponding to the features. * @param fitIntercept Whether to fit an intercept term. - * @param featuresStd The standard deviation values of the features. + * @param bcFeaturesStd The broadcast standard deviation values of the features. */ private class AFTAggregator( - parameters: BDV[Double], + bcParameters: Broadcast[BDV[Double]], fitIntercept: Boolean, - featuresStd: Array[Double]) extends Serializable { + bcFeaturesStd: Broadcast[Array[Double]]) extends Serializable { + // make transient so we do not serialize between aggregation stages + @transient private lazy val parameters = bcParameters.value // the regression coefficients to the covariates - private val coefficients = parameters.slice(2, parameters.length) - private val intercept = parameters(1) + @transient private lazy val coefficients = parameters.slice(2, parameters.length) + @transient private lazy val intercept = parameters(1) // sigma is the scale parameter of the AFT model - private val sigma = math.exp(parameters(0)) + @transient private lazy val sigma = math.exp(parameters(0)) --- End diff -- In line `506`, ```scala private val gradientSumArray = Array.ofDim[Double](parameters.length) ```, the code will evaluate the `lazy parameters` in the driver.
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