Change version to 1.0.0rc2

* Fix loading old logit model.
* Add a helper script for converting old pickle file.
* Add version as a model parameter.
* Remove the size check in R test to relax the size constraint.
* Add missing R doc for passing linting. Run devtools.
* Cleanup old model IO logic.
* Test compatibility on CI.
* Make the argument as required.

CMakeLists.txt

@@ -49,7 +49,7 @@ option(USE_SANITIZER "Use santizer flags" OFF)
 option(SANITIZER_PATH "Path to sanitizes.")
 set(ENABLED_SANITIZERS "address" "leak" CACHE STRING
   "Semicolon separated list of sanitizer names. E.g 'address;leak'. Supported sanitizers are
-address, leak and thread.")
+address, leak, undefined and thread.")
 ## Plugins
 option(PLUGIN_LZ4 "Build lz4 plugin" OFF)
 option(PLUGIN_DENSE_PARSER "Build dense parser plugin" OFF)

R-package/R/xgb.Booster.R

@@ -139,6 +139,8 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' @param reshape whether to reshape the vector of predictions to a matrix form when there are several
 #'        prediction outputs per case. This option has no effect when either of predleaf, predcontrib,
 #'        or predinteraction flags is TRUE.
+#' @param training whether is the prediction result used for training.  For dart booster,
+#'        training predicting will perform dropout.
 #' @param ... Parameters passed to \code{predict.xgb.Booster}
 #'
 #' @details

R-package/man/predict.xgb.Booster.Rd

@@ -49,6 +49,9 @@ It will use all the trees by default (\code{NULL} value).}
 prediction outputs per case. This option has no effect when either of predleaf, predcontrib,
 or predinteraction flags is TRUE.}
 
+\item{training}{whether is the prediction result used for training.  For dart booster,
+training predicting will perform dropout.}
+
 \item{...}{Parameters passed to \code{predict.xgb.Booster}}
 }
 \value{

R-package/tests/testthat/test_custom_objective.R

@@ -31,7 +31,6 @@ num_round <- 2
 test_that("custom objective works", {
   bst <- xgb.train(param, dtrain, num_round, watchlist)
   expect_equal(class(bst), "xgb.Booster")
-  expect_equal(length(bst$raw), 1100)
   expect_false(is.null(bst$evaluation_log))
   expect_false(is.null(bst$evaluation_log$eval_error))
   expect_lt(bst$evaluation_log[num_round, eval_error], 0.03)
@@ -58,5 +57,4 @@ test_that("custom objective using DMatrix attr works", {
   param$objective = logregobjattr
   bst <- xgb.train(param, dtrain, num_round, watchlist)
   expect_equal(class(bst), "xgb.Booster")
-  expect_equal(length(bst$raw), 1100)
 })

cmake/Python_version.in

@@ -1 +1 @@
-@xgboost_VERSION_MAJOR@.@xgboost_VERSION_MINOR@.@xgboost_VERSION_PATCH@-SNAPSHOT
+@xgboost_VERSION_MAJOR@.@xgboost_VERSION_MINOR@.@xgboost_VERSION_PATCH@rc2

doc/python/convert_090to100.py

@@ -0,0 +1,79 @@
+'''This is a simple script that converts a pickled XGBoost
+Scikit-Learn interface object from 0.90 to a native model.  Pickle
+format is not stable as it's a direct serialization of Python object.
+We advice not to use it when stability is needed.
+
+'''
+import pickle
+import json
+import os
+import argparse
+import numpy as np
+import xgboost
+import warnings
+
+
+def save_label_encoder(le):
+    '''Save the label encoder in XGBClassifier'''
+    meta = dict()
+    for k, v in le.__dict__.items():
+        if isinstance(v, np.ndarray):
+            meta[k] = v.tolist()
+        else:
+            meta[k] = v
+    return meta
+
+
+def xgboost_skl_90to100(skl_model):
+    '''Extract the model and related metadata in SKL model.'''
+    model = {}
+    with open(skl_model, 'rb') as fd:
+        old = pickle.load(fd)
+        if not isinstance(old, xgboost.XGBModel):
+            raise TypeError(
+                'The script only handes Scikit-Learn interface object')
+
+    # Save Scikit-Learn specific Python attributes into a JSON document.
+    for k, v in old.__dict__.items():
+        if k == '_le':
+            model[k] = save_label_encoder(v)
+        elif k == 'classes_':
+            model[k] = v.tolist()
+        elif k == '_Booster':
+            continue
+        else:
+            try:
+                json.dumps({k: v})
+                model[k] = v
+            except TypeError:
+                warnings.warn(str(k) + ' is not saved in Scikit-Learn meta.')
+    booster = old.get_booster()
+    # Store the JSON serialization as an attribute
+    booster.set_attr(scikit_learn=json.dumps(model))
+
+    # Save it into a native model.
+    i = 0
+    while True:
+        path = 'xgboost_native_model_from_' + skl_model + '-' + str(i) + '.bin'
+        if os.path.exists(path):
+            i += 1
+            continue
+        booster.save_model(path)
+        break
+
+
+if __name__ == '__main__':
+    assert xgboost.__version__ != '1.0.0', ('Please use the XGBoost version'
+                                            ' that generates this pickle.')
+    parser = argparse.ArgumentParser(
+        description=('A simple script to convert pickle generated by'
+                     ' XGBoost 0.90 to XGBoost 1.0.0 model (not pickle).'))
+    parser.add_argument(
+        '--old-pickle',
+        type=str,
+        help='Path to old pickle file of Scikit-Learn interface object.  '
+        'Will output a native model converted from this pickle file',
+        required=True)
+    args = parser.parse_args()
+
+    xgboost_skl_90to100(args.old_pickle)

doc/tutorials/saving_model.rst

@@ -91,7 +91,12 @@ Loading pickled file from different version of XGBoost
 
 As noted, pickled model is neither portable nor stable, but in some cases the pickled
 models are valuable.  One way to restore it in the future is to load it back with that
-specific version of Python and XGBoost, export the model by calling `save_model`.
+specific version of Python and XGBoost, export the model by calling `save_model`.  To help
+easing the mitigation, we created a simple script for converting pickled XGBoost 0.90
+Scikit-Learn interface object to XGBoost 1.0.0 native model.  Please note that the script
+suits simple use cases, and it's advised not to use pickle when stability is needed.
+It's located in ``xgboost/doc/python`` with the name ``convert_090to100.py``.  See
+comments in the script for more details.
 
 ********************************************************
 Saving and Loading the internal parameters configuration

include/xgboost/learner.h

@@ -208,6 +208,8 @@ struct LearnerModelParam {
   // As the old `LearnerModelParamLegacy` is still used by binary IO, we keep
   // this one as an immutable copy.
   LearnerModelParam(LearnerModelParamLegacy const& user_param, float base_margin);
+  /* \brief Whether this parameter is initialized with LearnerModelParamLegacy. */
+  bool Initialized() const { return num_feature != 0; }
 };
 
 }  // namespace xgboost

jvm-packages/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>ml.dmlc</groupId>
     <artifactId>xgboost-jvm_2.12</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.0.0-RC2</version>
     <packaging>pom</packaging>
     <name>XGBoost JVM Package</name>
     <description>JVM Package for XGBoost</description>
@@ -37,6 +37,7 @@
         <spark.version>2.4.3</spark.version>
         <scala.version>2.12.8</scala.version>
         <scala.binary.version>2.12</scala.binary.version>
+        <hadoop.version>2.7.3</hadoop.version>
     </properties>
     <repositories>
         <repository>

jvm-packages/xgboost4j-example/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.0.0-SNAPSHOT</version>
+        <version>1.0.0-RC2</version>
     </parent>
     <artifactId>xgboost4j-example_2.12</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.0.0-RC2</version>
     <packaging>jar</packaging>
     <build>
         <plugins>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
-            <version>1.0.0-SNAPSHOT</version>
+            <version>1.0.0-RC2</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>
@@ -37,7 +37,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
-            <version>1.0.0-SNAPSHOT</version>
+            <version>1.0.0-RC2</version>
         </dependency>
         <dependency>
             <groupId>org.apache.commons</groupId>

jvm-packages/xgboost4j-flink/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.0.0-SNAPSHOT</version>
+        <version>1.0.0-RC2</version>
     </parent>
     <artifactId>xgboost4j-flink_2.12</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.0.0-RC2</version>
     <build>
         <plugins>
             <plugin>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-            <version>1.0.0-SNAPSHOT</version>
+            <version>1.0.0-RC2</version>
         </dependency>
         <dependency>
             <groupId>org.apache.commons</groupId>

jvm-packages/xgboost4j-spark/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.0.0-SNAPSHOT</version>
+        <version>1.0.0-RC2</version>
     </parent>
     <artifactId>xgboost4j-spark_2.12</artifactId>
     <build>
@@ -24,7 +24,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-            <version>1.0.0-SNAPSHOT</version>
+            <version>1.0.0-RC2</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/CheckpointManager.scala

@@ -1,164 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark
-
-import ml.dmlc.xgboost4j.scala.Booster
-import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost}
-import org.apache.commons.logging.LogFactory
-import org.apache.hadoop.fs.{FileSystem, Path}
-import org.apache.spark.SparkContext
-
-/**
-  * A class which allows user to save checkpoints every a few rounds. If a previous job fails,
-  * the job can restart training from a saved checkpoints instead of from scratch. This class
-  * provides interface and helper methods for the checkpoint functionality.
-  *
-  * NOTE: This checkpoint is different from Rabit checkpoint. Rabit checkpoint is a native-level
-  * checkpoint stored in executor memory. This is a checkpoint which Spark driver store on HDFS
-  * for every a few iterations.
-  *
-  * @param sc the sparkContext object
-  * @param checkpointPath the hdfs path to store checkpoints
-  */
-private[spark] class CheckpointManager(sc: SparkContext, checkpointPath: String) {
-  private val logger = LogFactory.getLog("XGBoostSpark")
-  private val modelSuffix = ".model"
-
-  private def getPath(version: Int) = {
-    s"$checkpointPath/$version$modelSuffix"
-  }
-
-  private def getExistingVersions: Seq[Int] = {
-    val fs = FileSystem.get(sc.hadoopConfiguration)
-    if (checkpointPath.isEmpty || !fs.exists(new Path(checkpointPath))) {
-      Seq()
-    } else {
-      fs.listStatus(new Path(checkpointPath)).map(_.getPath.getName).collect {
-        case fileName if fileName.endsWith(modelSuffix) => fileName.stripSuffix(modelSuffix).toInt
-      }
-    }
-  }
-
-  def cleanPath(): Unit = {
-    if (checkpointPath != "") {
-      FileSystem.get(sc.hadoopConfiguration).delete(new Path(checkpointPath), true)
-    }
-  }
-
-  /**
-    * Load existing checkpoint with the highest version as a Booster object
-    *
-    * @return the booster with the highest version, null if no checkpoints available.
-    */
-  private[spark] def loadCheckpointAsBooster: Booster = {
-    val versions = getExistingVersions
-    if (versions.nonEmpty) {
-      val version = versions.max
-      val fullPath = getPath(version)
-      val inputStream = FileSystem.get(sc.hadoopConfiguration).open(new Path(fullPath))
-      logger.info(s"Start training from previous booster at $fullPath")
-      val booster = SXGBoost.loadModel(inputStream)
-      booster.booster.setVersion(version)
-      booster
-    } else {
-      null
-    }
-  }
-
-  /**
-    * Clean up all previous checkpoints and save a new checkpoint
-    *
-    * @param checkpoint the checkpoint to save as an XGBoostModel
-    */
-  private[spark] def updateCheckpoint(checkpoint: Booster): Unit = {
-    val fs = FileSystem.get(sc.hadoopConfiguration)
-    val prevModelPaths = getExistingVersions.map(version => new Path(getPath(version)))
-    val fullPath = getPath(checkpoint.getVersion)
-    val outputStream = fs.create(new Path(fullPath), true)
-    logger.info(s"Saving checkpoint model with version ${checkpoint.getVersion} to $fullPath")
-    checkpoint.saveModel(outputStream)
-    prevModelPaths.foreach(path => fs.delete(path, true))
-  }
-
-  /**
-    * Clean up checkpoint boosters with version higher than or equal to the round.
-    *
-    * @param round the number of rounds in the current training job
-    */
-  private[spark] def cleanUpHigherVersions(round: Int): Unit = {
-    val higherVersions = getExistingVersions.filter(_ / 2 >= round)
-    higherVersions.foreach { version =>
-      val fs = FileSystem.get(sc.hadoopConfiguration)
-      fs.delete(new Path(getPath(version)), true)
-    }
-  }
-
-  /**
-    * Calculate a list of checkpoint rounds to save checkpoints based on the checkpointInterval
-    * and total number of rounds for the training. Concretely, the checkpoint rounds start with
-    * prevRounds + checkpointInterval, and increase by checkpointInterval in each step until it
-    * reaches total number of rounds. If checkpointInterval is 0, the checkpoint will be disabled
-    * and the method returns Seq(round)
-    *
-    * @param checkpointInterval Period (in iterations) between checkpoints.
-    * @param round the total number of rounds for the training
-    * @return a seq of integers, each represent the index of round to save the checkpoints
-    */
-  private[spark] def getCheckpointRounds(checkpointInterval: Int, round: Int): Seq[Int] = {
-    if (checkpointPath.nonEmpty && checkpointInterval > 0) {
-      val prevRounds = getExistingVersions.map(_ / 2)
-      val firstCheckpointRound = (0 +: prevRounds).max + checkpointInterval
-      (firstCheckpointRound until round by checkpointInterval) :+ round
-    } else if (checkpointInterval <= 0) {
-      Seq(round)
-    } else {
-      throw new IllegalArgumentException("parameters \"checkpoint_path\" should also be set.")
-    }
-  }
-}
-
-object CheckpointManager {
-
-  case class CheckpointParam(
-      checkpointPath: String,
-      checkpointInterval: Int,
-      skipCleanCheckpoint: Boolean)
-
-  private[spark] def extractParams(params: Map[String, Any]): CheckpointParam = {
-    val checkpointPath: String = params.get("checkpoint_path") match {
-      case None => ""
-      case Some(path: String) => path
-      case _ => throw new IllegalArgumentException("parameter \"checkpoint_path\" must be" +
-        " an instance of String.")
-    }
-
-    val checkpointInterval: Int = params.get("checkpoint_interval") match {
-      case None => 0
-      case Some(freq: Int) => freq
-      case _ => throw new IllegalArgumentException("parameter \"checkpoint_interval\" must be" +
-        " an instance of Int.")
-    }
-
-    val skipCheckpointFile: Boolean = params.get("skip_clean_checkpoint") match {
-      case None => false
-      case Some(skipCleanCheckpoint: Boolean) => skipCleanCheckpoint
-      case _ => throw new IllegalArgumentException("parameter \"skip_clean_checkpoint\" must be" +
-        " an instance of Boolean")
-    }
-    CheckpointParam(checkpointPath, checkpointInterval, skipCheckpointFile)
-  }
-}

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala

@@ -25,12 +25,13 @@ import scala.collection.JavaConverters._
 
 import ml.dmlc.xgboost4j.java.{IRabitTracker, Rabit, XGBoostError, RabitTracker => PyRabitTracker}
 import ml.dmlc.xgboost4j.scala.rabit.RabitTracker
-import ml.dmlc.xgboost4j.scala.spark.CheckpointManager.CheckpointParam
 import ml.dmlc.xgboost4j.scala.spark.params.LearningTaskParams
+import ml.dmlc.xgboost4j.scala.ExternalCheckpointManager
 import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost, _}
 import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
 import org.apache.commons.io.FileUtils
 import org.apache.commons.logging.LogFactory
+import org.apache.hadoop.fs.FileSystem
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.{SparkContext, SparkParallelismTracker, TaskContext, TaskFailedListener}
@@ -64,7 +65,7 @@ private[this] case class XGBoostExecutionInputParams(trainTestRatio: Double, see
 
 private[this] case class XGBoostExecutionParams(
     numWorkers: Int,
-    round: Int,
+    numRounds: Int,
     useExternalMemory: Boolean,
     obj: ObjectiveTrait,
     eval: EvalTrait,
@@ -72,7 +73,7 @@ private[this] case class XGBoostExecutionParams(
     allowNonZeroForMissing: Boolean,
     trackerConf: TrackerConf,
     timeoutRequestWorkers: Long,
-    checkpointParam: CheckpointParam,
+    checkpointParam: Option[ExternalCheckpointParams],
     xgbInputParams: XGBoostExecutionInputParams,
     earlyStoppingParams: XGBoostExecutionEarlyStoppingParams,
     cacheTrainingSet: Boolean) {
@@ -167,7 +168,6 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
                                  .getOrElse("allow_non_zero_for_missing", false)
                                  .asInstanceOf[Boolean]
     validateSparkSslConf
-
     if (overridedParams.contains("tree_method")) {
       require(overridedParams("tree_method") == "hist" ||
         overridedParams("tree_method") == "approx" ||
@@ -198,7 +198,7 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
         " an instance of Long.")
     }
     val checkpointParam =
-      CheckpointManager.extractParams(overridedParams)
+      ExternalCheckpointParams.extractParams(overridedParams)
 
     val trainTestRatio = overridedParams.getOrElse("train_test_ratio", 1.0)
       .asInstanceOf[Double]
@@ -339,11 +339,9 @@ object XGBoost extends Serializable {
       watches: Watches,
       xgbExecutionParam: XGBoostExecutionParams,
       rabitEnv: java.util.Map[String, String],
-      round: Int,
       obj: ObjectiveTrait,
       eval: EvalTrait,
       prevBooster: Booster): Iterator[(Booster, Map[String, Array[Float]])] = {
-
     // to workaround the empty partitions in training dataset,
     // this might not be the best efficient implementation, see
     // (https://github.com/dmlc/xgboost/issues/1277)
@@ -357,14 +355,23 @@ object XGBoost extends Serializable {
     rabitEnv.put("DMLC_TASK_ID", taskId)
     rabitEnv.put("DMLC_NUM_ATTEMPT", attempt)
     rabitEnv.put("DMLC_WORKER_STOP_PROCESS_ON_ERROR", "false")
-
+    val numRounds = xgbExecutionParam.numRounds
+    val makeCheckpoint = xgbExecutionParam.checkpointParam.isDefined && taskId.toInt == 0
     try {
       Rabit.init(rabitEnv)
       val numEarlyStoppingRounds = xgbExecutionParam.earlyStoppingParams.numEarlyStoppingRounds
-      val metrics = Array.tabulate(watches.size)(_ => Array.ofDim[Float](round))
-      val booster = SXGBoost.train(watches.toMap("train"), xgbExecutionParam.toMap, round,
+      val metrics = Array.tabulate(watches.size)(_ => Array.ofDim[Float](numRounds))
+      val externalCheckpointParams = xgbExecutionParam.checkpointParam
+      val booster = if (makeCheckpoint) {
+        SXGBoost.trainAndSaveCheckpoint(
+          watches.toMap("train"), xgbExecutionParam.toMap, numRounds,
+          watches.toMap, metrics, obj, eval,
+          earlyStoppingRound = numEarlyStoppingRounds, prevBooster, externalCheckpointParams)
+      } else {
+        SXGBoost.train(watches.toMap("train"), xgbExecutionParam.toMap, numRounds,
           watches.toMap, metrics, obj, eval,
           earlyStoppingRound = numEarlyStoppingRounds, prevBooster)
+      }
       Iterator(booster -> watches.toMap.keys.zip(metrics).toMap)
     } catch {
       case xgbException: XGBoostError =>
@@ -437,7 +444,6 @@ object XGBoost extends Serializable {
       trainingData: RDD[XGBLabeledPoint],
       xgbExecutionParams: XGBoostExecutionParams,
       rabitEnv: java.util.Map[String, String],
-      checkpointRound: Int,
       prevBooster: Booster,
       evalSetsMap: Map[String, RDD[XGBLabeledPoint]]): RDD[(Booster, Map[String, Array[Float]])] = {
     if (evalSetsMap.isEmpty) {
@@ -446,8 +452,8 @@ object XGBoost extends Serializable {
           processMissingValues(labeledPoints, xgbExecutionParams.missing,
             xgbExecutionParams.allowNonZeroForMissing),
           getCacheDirName(xgbExecutionParams.useExternalMemory))
-        buildDistributedBooster(watches, xgbExecutionParams, rabitEnv, checkpointRound,
-          xgbExecutionParams.obj, xgbExecutionParams.eval, prevBooster)
+        buildDistributedBooster(watches, xgbExecutionParams, rabitEnv, xgbExecutionParams.obj,
+          xgbExecutionParams.eval, prevBooster)
       }).cache()
     } else {
       coPartitionNoGroupSets(trainingData, evalSetsMap, xgbExecutionParams.numWorkers).
@@ -459,8 +465,8 @@ object XGBoost extends Serializable {
                   xgbExecutionParams.missing, xgbExecutionParams.allowNonZeroForMissing))
               },
               getCacheDirName(xgbExecutionParams.useExternalMemory))
-            buildDistributedBooster(watches, xgbExecutionParams, rabitEnv, checkpointRound,
-              xgbExecutionParams.obj, xgbExecutionParams.eval, prevBooster)
+            buildDistributedBooster(watches, xgbExecutionParams, rabitEnv, xgbExecutionParams.obj,
+              xgbExecutionParams.eval, prevBooster)
         }.cache()
     }
   }
@@ -469,7 +475,6 @@ object XGBoost extends Serializable {
       trainingData: RDD[Array[XGBLabeledPoint]],
       xgbExecutionParam: XGBoostExecutionParams,
       rabitEnv: java.util.Map[String, String],
-      checkpointRound: Int,
       prevBooster: Booster,
       evalSetsMap: Map[String, RDD[XGBLabeledPoint]]): RDD[(Booster, Map[String, Array[Float]])] = {
     if (evalSetsMap.isEmpty) {
@@ -478,7 +483,7 @@ object XGBoost extends Serializable {
           processMissingValuesWithGroup(labeledPointGroups, xgbExecutionParam.missing,
             xgbExecutionParam.allowNonZeroForMissing),
           getCacheDirName(xgbExecutionParam.useExternalMemory))
-        buildDistributedBooster(watches, xgbExecutionParam, rabitEnv, checkpointRound,
+        buildDistributedBooster(watches, xgbExecutionParam, rabitEnv,
           xgbExecutionParam.obj, xgbExecutionParam.eval, prevBooster)
       }).cache()
     } else {
@@ -490,7 +495,7 @@ object XGBoost extends Serializable {
                 xgbExecutionParam.missing, xgbExecutionParam.allowNonZeroForMissing))
             },
             getCacheDirName(xgbExecutionParam.useExternalMemory))
-          buildDistributedBooster(watches, xgbExecutionParam, rabitEnv, checkpointRound,
+          buildDistributedBooster(watches, xgbExecutionParam, rabitEnv,
             xgbExecutionParam.obj,
             xgbExecutionParam.eval,
             prevBooster)
@@ -529,33 +534,30 @@ object XGBoost extends Serializable {
     logger.info(s"Running XGBoost ${spark.VERSION} with parameters:\n${params.mkString("\n")}")
     val xgbParamsFactory = new XGBoostExecutionParamsFactory(params, trainingData.sparkContext)
     val xgbExecParams = xgbParamsFactory.buildXGBRuntimeParams
-    val xgbRabitParams = xgbParamsFactory.buildRabitParams.asJava
     val sc = trainingData.sparkContext
-    val checkpointManager = new CheckpointManager(sc, xgbExecParams.checkpointParam.
-      checkpointPath)
-    checkpointManager.cleanUpHigherVersions(xgbExecParams.round)
     val transformedTrainingData = composeInputData(trainingData, xgbExecParams.cacheTrainingSet,
       hasGroup, xgbExecParams.numWorkers)
-    var prevBooster = checkpointManager.loadCheckpointAsBooster
+    val prevBooster = xgbExecParams.checkpointParam.map { checkpointParam =>
+      val checkpointManager = new ExternalCheckpointManager(
+        checkpointParam.checkpointPath,
+        FileSystem.get(sc.hadoopConfiguration))
+      checkpointManager.cleanUpHigherVersions(xgbExecParams.numRounds)
+      checkpointManager.loadCheckpointAsScalaBooster()
+    }.orNull
     try {
       // Train for every ${savingRound} rounds and save the partially completed booster
-      val producedBooster = checkpointManager.getCheckpointRounds(
-        xgbExecParams.checkpointParam.checkpointInterval,
-        xgbExecParams.round).map {
-        checkpointRound: Int =>
       val tracker = startTracker(xgbExecParams.numWorkers, xgbExecParams.trackerConf)
-          try {
+      val (booster, metrics) = try {
         val parallelismTracker = new SparkParallelismTracker(sc,
           xgbExecParams.timeoutRequestWorkers,
           xgbExecParams.numWorkers)
-
-            tracker.getWorkerEnvs().putAll(xgbRabitParams)
+        val rabitEnv = tracker.getWorkerEnvs
         val boostersAndMetrics = if (hasGroup) {
-              trainForRanking(transformedTrainingData.left.get, xgbExecParams,
-                tracker.getWorkerEnvs(), checkpointRound, prevBooster, evalSetsMap)
+          trainForRanking(transformedTrainingData.left.get, xgbExecParams, rabitEnv, prevBooster,
+            evalSetsMap)
         } else {
-              trainForNonRanking(transformedTrainingData.right.get, xgbExecParams,
-                tracker.getWorkerEnvs(), checkpointRound, prevBooster, evalSetsMap)
+          trainForNonRanking(transformedTrainingData.right.get, xgbExecParams, rabitEnv,
+            prevBooster, evalSetsMap)
         }
         val sparkJobThread = new Thread() {
           override def run() {
@@ -569,20 +571,21 @@ object XGBoost extends Serializable {
         logger.info(s"Rabit returns with exit code $trackerReturnVal")
         val (booster, metrics) = postTrackerReturnProcessing(trackerReturnVal,
           boostersAndMetrics, sparkJobThread)
-            if (checkpointRound < xgbExecParams.round) {
-              prevBooster = booster
-              checkpointManager.updateCheckpoint(prevBooster)
-            }
         (booster, metrics)
       } finally {
         tracker.stop()
       }
-      }.last
       // we should delete the checkpoint directory after a successful training
-      if (!xgbExecParams.checkpointParam.skipCleanCheckpoint) {
+      xgbExecParams.checkpointParam.foreach {
+        cpParam =>
+          if (!xgbExecParams.checkpointParam.get.skipCleanCheckpoint) {
+            val checkpointManager = new ExternalCheckpointManager(
+              cpParam.checkpointPath,
+              FileSystem.get(sc.hadoopConfiguration))
             checkpointManager.cleanPath()
           }
-      producedBooster
+      }
+      (booster, metrics)
     } catch {
       case t: Throwable =>
         // if the job was aborted due to an exception

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostEstimatorCommon.scala

@@ -24,7 +24,7 @@ private[spark] sealed trait XGBoostEstimatorCommon extends GeneralParams with Le
   with BoosterParams with RabitParams with ParamMapFuncs with NonParamVariables {
 
   def needDeterministicRepartitioning: Boolean = {
-    getCheckpointPath.nonEmpty && getCheckpointInterval > 0
+    getCheckpointPath != null && getCheckpointPath.nonEmpty && getCheckpointInterval > 0
   }
 }
 

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/ExternalCheckpointManagerSuite.scala

@@ -18,54 +18,71 @@ package ml.dmlc.xgboost4j.scala.spark
 
 import java.io.File
 
-import ml.dmlc.xgboost4j.scala.{Booster, DMatrix, XGBoost => SXGBoost}
-import org.scalatest.FunSuite
+import ml.dmlc.xgboost4j.scala.{Booster, DMatrix, ExternalCheckpointManager, XGBoost => SXGBoost}
+import org.scalatest.{FunSuite, Ignore}
 import org.apache.hadoop.fs.{FileSystem, Path}
 
-class CheckpointManagerSuite extends FunSuite with TmpFolderPerSuite with PerTest {
+class ExternalCheckpointManagerSuite extends FunSuite with TmpFolderPerSuite with PerTest {
 
-  private lazy val (model4, model8) = {
+  private def produceParamMap(checkpointPath: String, checkpointInterval: Int):
+  Map[String, Any] = {
+    Map("eta" -> "1", "max_depth" -> "2", "silent" -> "1",
+      "objective" -> "binary:logistic", "num_workers" -> sc.defaultParallelism,
+      "checkpoint_path" -> checkpointPath, "checkpoint_interval" -> checkpointInterval)
+  }
+
+  private def createNewModels():
+    (String, XGBoostClassificationModel, XGBoostClassificationModel) = {
+    val tmpPath = createTmpFolder("test").toAbsolutePath.toString
+    val (model4, model8) = {
       val training = buildDataFrame(Classification.train)
-    val paramMap = Map("eta" -> "1", "max_depth" -> "2", "silent" -> "1",
-      "objective" -> "binary:logistic", "num_workers" -> sc.defaultParallelism)
+      val paramMap = produceParamMap(tmpPath, 2)
       (new XGBoostClassifier(paramMap ++ Seq("num_round" -> 2)).fit(training),
         new XGBoostClassifier(paramMap ++ Seq("num_round" -> 4)).fit(training))
     }
+    (tmpPath, model4, model8)
+  }
 
   test("test update/load models") {
-    val tmpPath = createTmpFolder("test").toAbsolutePath.toString
-    val manager = new CheckpointManager(sc, tmpPath)
-    manager.updateCheckpoint(model4._booster)
+    val (tmpPath, model4, model8) = createNewModels()
+    val manager = new ExternalCheckpointManager(tmpPath, FileSystem.get(sc.hadoopConfiguration))
+
+    manager.updateCheckpoint(model4._booster.booster)
     var files = FileSystem.get(sc.hadoopConfiguration).listStatus(new Path(tmpPath))
     assert(files.length == 1)
     assert(files.head.getPath.getName == "4.model")
-    assert(manager.loadCheckpointAsBooster.booster.getVersion == 4)
+    assert(manager.loadCheckpointAsScalaBooster().getVersion == 4)
 
     manager.updateCheckpoint(model8._booster)
     files = FileSystem.get(sc.hadoopConfiguration).listStatus(new Path(tmpPath))
     assert(files.length == 1)
     assert(files.head.getPath.getName == "8.model")
-    assert(manager.loadCheckpointAsBooster.booster.getVersion == 8)
+    assert(manager.loadCheckpointAsScalaBooster().getVersion == 8)
   }
 
   test("test cleanUpHigherVersions") {
-    val tmpPath = createTmpFolder("test").toAbsolutePath.toString
-    val manager = new CheckpointManager(sc, tmpPath)
+    val (tmpPath, model4, model8) = createNewModels()
+
+    val manager = new ExternalCheckpointManager(tmpPath, FileSystem.get(sc.hadoopConfiguration))
     manager.updateCheckpoint(model8._booster)
-    manager.cleanUpHigherVersions(round = 8)
+    manager.cleanUpHigherVersions(8)
     assert(new File(s"$tmpPath/8.model").exists())
 
-    manager.cleanUpHigherVersions(round = 4)
+    manager.cleanUpHigherVersions(4)
     assert(!new File(s"$tmpPath/8.model").exists())
   }
 
   test("test checkpoint rounds") {
-    val tmpPath = createTmpFolder("test").toAbsolutePath.toString
-    val manager = new CheckpointManager(sc, tmpPath)
-    assertResult(Seq(7))(manager.getCheckpointRounds(checkpointInterval = 0, round = 7))
-    assertResult(Seq(2, 4, 6, 7))(manager.getCheckpointRounds(checkpointInterval = 2, round = 7))
+    import scala.collection.JavaConverters._
+    val (tmpPath, model4, model8) = createNewModels()
+    val manager = new ExternalCheckpointManager(tmpPath, FileSystem.get(sc.hadoopConfiguration))
+    assertResult(Seq(7))(
+      manager.getCheckpointRounds(0, 7).asScala)
+    assertResult(Seq(2, 4, 6, 7))(
+      manager.getCheckpointRounds(2, 7).asScala)
     manager.updateCheckpoint(model4._booster)
-    assertResult(Seq(4, 6, 7))(manager.getCheckpointRounds(2, 7))
+    assertResult(Seq(4, 6, 7))(
+      manager.getCheckpointRounds(2, 7).asScala)
   }
 
 
@@ -75,17 +92,18 @@ class CheckpointManagerSuite extends FunSuite with TmpFolderPerSuite with PerTes
     val testDM = new DMatrix(Classification.test.iterator)
 
     val tmpPath = createTmpFolder("model1").toAbsolutePath.toString
+
+    val paramMap = produceParamMap(tmpPath, 2)
+
     val cacheDataMap = if (cacheData) Map("cacheTrainingSet" -> true) else Map()
     val skipCleanCheckpointMap =
       if (skipCleanCheckpoint) Map("skip_clean_checkpoint" -> true) else Map()
-    val paramMap = Map("eta" -> "1", "max_depth" -> 2,
-      "objective" -> "binary:logistic", "checkpoint_path" -> tmpPath,
-      "checkpoint_interval" -> 2, "num_workers" -> numWorkers) ++ cacheDataMap ++
-      skipCleanCheckpointMap
 
-    val prevModel = new XGBoostClassifier(paramMap ++ Seq("num_round" -> 5)).fit(training)
-    def error(model: Booster): Float = eval.eval(
-      model.predict(testDM, outPutMargin = true), testDM)
+    val finalParamMap = paramMap ++ cacheDataMap ++ skipCleanCheckpointMap
+
+    val prevModel = new XGBoostClassifier(finalParamMap ++ Seq("num_round" -> 5)).fit(training)
+
+    def error(model: Booster): Float = eval.eval(model.predict(testDM, outPutMargin = true), testDM)
 
     if (skipCleanCheckpoint) {
       // Check only one model is kept after training
@@ -95,7 +113,7 @@ class CheckpointManagerSuite extends FunSuite with TmpFolderPerSuite with PerTes
       val tmpModel = SXGBoost.loadModel(s"$tmpPath/8.model")
       // Train next model based on prev model
       val nextModel = new XGBoostClassifier(paramMap ++ Seq("num_round" -> 8)).fit(training)
-      assert(error(tmpModel) > error(prevModel._booster))
+      assert(error(tmpModel) >= error(prevModel._booster))
       assert(error(prevModel._booster) > error(nextModel._booster))
       assert(error(nextModel._booster) < 0.1)
     } else {

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/MissingValueHandlingSuite.scala

@@ -127,7 +127,6 @@ class MissingValueHandlingSuite extends FunSuite with PerTest {
     " stop the application") {
     val spark = ss
     import spark.implicits._
-    ss.sparkContext.setLogLevel("INFO")
     // spark uses 1.5 * (nnz + 1.0) < size as the condition to decide whether using sparse or dense
     // vector,
     val testDF = Seq(
@@ -155,7 +154,6 @@ class MissingValueHandlingSuite extends FunSuite with PerTest {
     "does not stop application") {
     val spark = ss
     import spark.implicits._
-    ss.sparkContext.setLogLevel("INFO")
     // spark uses 1.5 * (nnz + 1.0) < size as the condition to decide whether using sparse or dense
     // vector,
     val testDF = Seq(

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/ParameterSuite.scala

@@ -17,7 +17,7 @@
 package ml.dmlc.xgboost4j.scala.spark
 
 import ml.dmlc.xgboost4j.java.XGBoostError
-import org.scalatest.{BeforeAndAfterAll, FunSuite}
+import org.scalatest.{BeforeAndAfterAll, FunSuite, Ignore}
 
 import org.apache.spark.ml.param.ParamMap
 

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/RabitRobustnessSuite.scala

@@ -20,14 +20,12 @@ import java.util.concurrent.LinkedBlockingDeque
 
 import scala.util.Random
 
-import ml.dmlc.xgboost4j.java.{IRabitTracker, Rabit, RabitTracker => PyRabitTracker}
+import ml.dmlc.xgboost4j.java.{Rabit, RabitTracker => PyRabitTracker}
 import ml.dmlc.xgboost4j.scala.rabit.{RabitTracker => ScalaRabitTracker}
 import ml.dmlc.xgboost4j.java.IRabitTracker.TrackerStatus
 import ml.dmlc.xgboost4j.scala.DMatrix
 
-import org.apache.spark.{SparkConf, SparkContext}
-import org.scalatest.FunSuite
-
+import org.scalatest.{FunSuite, Ignore}
 
 class RabitRobustnessSuite extends FunSuite with PerTest {
 

jvm-packages/xgboost4j/pom.xml

@@ -6,13 +6,25 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.0.0-SNAPSHOT</version>
+        <version>1.0.0-RC2</version>
     </parent>
     <artifactId>xgboost4j_2.12</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.0.0-RC2</version>
     <packaging>jar</packaging>
 
     <dependencies>
+        <dependency>
+            <groupId>org.apache.hadoop</groupId>
+            <artifactId>hadoop-hdfs</artifactId>
+            <version>${hadoop.version}</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.hadoop</groupId>
+            <artifactId>hadoop-common</artifactId>
+            <version>${hadoop.version}</version>
+            <scope>provided</scope>
+        </dependency>
         <dependency>
             <groupId>junit</groupId>
             <artifactId>junit</artifactId>

jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/ExternalCheckpointManager.java

@@ -0,0 +1,117 @@
+package ml.dmlc.xgboost4j.java;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.util.*;
+import java.util.stream.Collectors;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+
+public class ExternalCheckpointManager {
+
+  private Log logger = LogFactory.getLog("ExternalCheckpointManager");
+  private String modelSuffix = ".model";
+  private Path checkpointPath;
+  private FileSystem fs;
+
+  public ExternalCheckpointManager(String checkpointPath, FileSystem fs) throws XGBoostError {
+    if (checkpointPath == null || checkpointPath.isEmpty()) {
+      throw new XGBoostError("cannot create ExternalCheckpointManager with null or" +
+              " empty checkpoint path");
+    }
+    this.checkpointPath = new Path(checkpointPath);
+    this.fs = fs;
+  }
+
+  private String getPath(int version) {
+    return checkpointPath.toUri().getPath() + "/" + version + modelSuffix;
+  }
+
+  private List<Integer> getExistingVersions() throws IOException {
+    if (!fs.exists(checkpointPath)) {
+      return new ArrayList<>();
+    } else {
+      return Arrays.stream(fs.listStatus(checkpointPath))
+              .map(path -> path.getPath().getName())
+              .filter(fileName -> fileName.endsWith(modelSuffix))
+              .map(fileName -> Integer.valueOf(
+                      fileName.substring(0, fileName.length() - modelSuffix.length())))
+              .collect(Collectors.toList());
+    }
+  }
+
+  public void cleanPath() throws IOException {
+    fs.delete(checkpointPath, true);
+  }
+
+  public Booster loadCheckpointAsBooster() throws IOException, XGBoostError {
+    List<Integer> versions = getExistingVersions();
+    if (versions.size() > 0) {
+      int latestVersion = versions.stream().max(Comparator.comparing(Integer::valueOf)).get();
+      String checkpointPath = getPath(latestVersion);
+      InputStream in = fs.open(new Path(checkpointPath));
+      logger.info("loaded checkpoint from " + checkpointPath);
+      Booster booster = XGBoost.loadModel(in);
+      booster.setVersion(latestVersion);
+      return booster;
+    } else {
+      return null;
+    }
+  }
+
+  public void updateCheckpoint(Booster boosterToCheckpoint) throws IOException, XGBoostError {
+    List<String> prevModelPaths = getExistingVersions().stream()
+            .map(this::getPath).collect(Collectors.toList());
+    String eventualPath = getPath(boosterToCheckpoint.getVersion());
+    String tempPath = eventualPath + "-" + UUID.randomUUID();
+    try (OutputStream out = fs.create(new Path(tempPath), true)) {
+      boosterToCheckpoint.saveModel(out);
+      fs.rename(new Path(tempPath), new Path(eventualPath));
+      logger.info("saving checkpoint with version " + boosterToCheckpoint.getVersion());
+      prevModelPaths.stream().forEach(path -> {
+        try {
+          fs.delete(new Path(path), true);
+        } catch (IOException e) {
+          logger.error("failed to delete outdated checkpoint at " + path, e);
+        }
+      });
+    }
+  }
+
+  public void cleanUpHigherVersions(int currentRound) throws IOException {
+    getExistingVersions().stream().filter(v -> v / 2 >= currentRound).forEach(v -> {
+      try {
+        fs.delete(new Path(getPath(v)), true);
+      } catch (IOException e) {
+        logger.error("failed to clean checkpoint from other training instance", e);
+      }
+    });
+  }
+
+  public List<Integer> getCheckpointRounds(int checkpointInterval, int numOfRounds)
+      throws IOException {
+    if (checkpointInterval > 0) {
+      List<Integer> prevRounds =
+              getExistingVersions().stream().map(v -> v / 2).collect(Collectors.toList());
+      prevRounds.add(0);
+      int firstCheckpointRound = prevRounds.stream()
+              .max(Comparator.comparing(Integer::valueOf)).get() + checkpointInterval;
+      List<Integer> arr = new ArrayList<>();
+      for (int i = firstCheckpointRound; i <= numOfRounds; i += checkpointInterval) {
+        arr.add(i);
+      }
+      arr.add(numOfRounds);
+      return arr;
+    } else if (checkpointInterval <= 0) {
+      List<Integer> l = new ArrayList<Integer>();
+      l.add(numOfRounds);
+      return l;
+    } else {
+      throw new IllegalArgumentException("parameters \"checkpoint_path\" should also be set.");
+    }
+  }
+}

jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoost.java

@@ -15,12 +15,16 @@
  */
 package ml.dmlc.xgboost4j.java;
 
+import java.io.File;
 import java.io.IOException;
 import java.io.InputStream;
+import java.io.OutputStream;
 import java.util.*;
 
 import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
 
 /**
  * trainer for xgboost
@@ -108,35 +112,34 @@ public class XGBoost {
     return train(dtrain, params, round, watches, metrics, obj, eval, earlyStoppingRound, null);
   }
 
-  /**
-   * Train a booster given parameters.
-   *
-   * @param dtrain  Data to be trained.
-   * @param params  Parameters.
-   * @param round   Number of boosting iterations.
-   * @param watches a group of items to be evaluated during training, this allows user to watch
-   *                performance on the validation set.
-   * @param metrics array containing the evaluation metrics for each matrix in watches for each
-   *                iteration
-   * @param earlyStoppingRounds if non-zero, training would be stopped
-   *                           after a specified number of consecutive
-   *                           goes to the unexpected direction in any evaluation metric.
-   * @param obj     customized objective
-   * @param eval    customized evaluation
-   * @param booster train from scratch if set to null; train from an existing booster if not null.
-   * @return The trained booster.
-   */
-  public static Booster train(
+  private static void saveCheckpoint(
+          Booster booster,
+          int iter,
+          Set<Integer> checkpointIterations,
+          ExternalCheckpointManager ecm) throws XGBoostError {
+    try {
+      if (checkpointIterations.contains(iter)) {
+        ecm.updateCheckpoint(booster);
+      }
+    } catch (Exception e) {
+      logger.error("failed to save checkpoint in XGBoost4J at iteration " + iter, e);
+      throw new XGBoostError("failed to save checkpoint in XGBoost4J at iteration" + iter, e);
+    }
+  }
+
+  public static Booster trainAndSaveCheckpoint(
       DMatrix dtrain,
       Map<String, Object> params,
-          int round,
+      int numRounds,
       Map<String, DMatrix> watches,
       float[][] metrics,
       IObjective obj,
       IEvaluation eval,
       int earlyStoppingRounds,
-          Booster booster) throws XGBoostError {
-
+      Booster booster,
+      int checkpointInterval,
+      String checkpointPath,
+      FileSystem fs) throws XGBoostError, IOException {
     //collect eval matrixs
     String[] evalNames;
     DMatrix[] evalMats;
@@ -144,6 +147,11 @@ public class XGBoost {
     int bestIteration;
     List<String> names = new ArrayList<String>();
     List<DMatrix> mats = new ArrayList<DMatrix>();
+    Set<Integer> checkpointIterations = new HashSet<>();
+    ExternalCheckpointManager ecm = null;
+    if (checkpointPath != null) {
+      ecm = new ExternalCheckpointManager(checkpointPath, fs);
+    }
 
     for (Map.Entry<String, DMatrix> evalEntry : watches.entrySet()) {
       names.add(evalEntry.getKey());
@@ -158,7 +166,7 @@ public class XGBoost {
       bestScore = Float.MAX_VALUE;
     }
     bestIteration = 0;
-    metrics = metrics == null ? new float[evalNames.length][round] : metrics;
+    metrics = metrics == null ? new float[evalNames.length][numRounds] : metrics;
 
     //collect all data matrixs
     DMatrix[] allMats;
@@ -181,14 +189,19 @@ public class XGBoost {
       booster.setParams(params);
     }
 
+    if (ecm != null) {
+      checkpointIterations = new HashSet<>(ecm.getCheckpointRounds(checkpointInterval, numRounds));
+    }
+
     // begin to train
-    for (int iter = booster.getVersion() / 2; iter < round; iter++) {
+    for (int iter = booster.getVersion() / 2; iter < numRounds; iter++) {
       if (booster.getVersion() % 2 == 0) {
         if (obj != null) {
           booster.update(dtrain, obj);
         } else {
           booster.update(dtrain, iter);
         }
+        saveCheckpoint(booster, iter, checkpointIterations, ecm);
         booster.saveRabitCheckpoint();
       }
 
@@ -239,6 +252,44 @@ public class XGBoost {
     return booster;
   }
 
+  /**
+   * Train a booster given parameters.
+   *
+   * @param dtrain  Data to be trained.
+   * @param params  Parameters.
+   * @param round   Number of boosting iterations.
+   * @param watches a group of items to be evaluated during training, this allows user to watch
+   *                performance on the validation set.
+   * @param metrics array containing the evaluation metrics for each matrix in watches for each
+   *                iteration
+   * @param earlyStoppingRounds if non-zero, training would be stopped
+   *                           after a specified number of consecutive
+   *                           goes to the unexpected direction in any evaluation metric.
+   * @param obj     customized objective
+   * @param eval    customized evaluation
+   * @param booster train from scratch if set to null; train from an existing booster if not null.
+   * @return The trained booster.
+   */
+  public static Booster train(
+          DMatrix dtrain,
+          Map<String, Object> params,
+          int round,
+          Map<String, DMatrix> watches,
+          float[][] metrics,
+          IObjective obj,
+          IEvaluation eval,
+          int earlyStoppingRounds,
+          Booster booster) throws XGBoostError {
+    try {
+      return trainAndSaveCheckpoint(dtrain, params, round, watches, metrics, obj, eval,
+              earlyStoppingRounds, booster,
+              -1, null, null);
+    } catch (IOException e) {
+      logger.error("training failed in xgboost4j", e);
+      throw new XGBoostError("training failed in xgboost4j ", e);
+    }
+  }
+
   private static Integer tryGetIntFromObject(Object o) {
     if (o instanceof Integer) {
       return (int)o;

jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoostError.java

@@ -24,4 +24,8 @@ public class XGBoostError extends Exception {
   public XGBoostError(String message) {
     super(message);
   }
+
+  public XGBoostError(String message, Throwable cause) {
+    super(message, cause);
+  }
 }

jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/ExternalCheckpointManager.scala

@@ -0,0 +1,37 @@
+/*
+ Copyright (c) 2014 by Contributors
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+
+package ml.dmlc.xgboost4j.scala
+
+import ml.dmlc.xgboost4j.java.{ExternalCheckpointManager => JavaECM}
+import org.apache.hadoop.fs.FileSystem
+
+class ExternalCheckpointManager(checkpointPath: String, fs: FileSystem)
+  extends JavaECM(checkpointPath, fs) {
+
+  def updateCheckpoint(booster: Booster): Unit = {
+    super.updateCheckpoint(booster.booster)
+  }
+
+  def loadCheckpointAsScalaBooster(): Booster = {
+    val loadedBooster = super.loadCheckpointAsBooster()
+    if (loadedBooster == null) {
+      null
+    } else {
+      new Booster(loadedBooster)
+    }
+  }
+}

jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/XGBoost.scala

@@ -18,14 +18,60 @@ package ml.dmlc.xgboost4j.scala
 
 import java.io.InputStream
 
-import ml.dmlc.xgboost4j.java.{Booster => JBooster, XGBoost => JXGBoost, XGBoostError}
+import ml.dmlc.xgboost4j.java.{XGBoostError, Booster => JBooster, XGBoost => JXGBoost}
 import scala.collection.JavaConverters._
 
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileSystem, Path}
+
 /**
   * XGBoost Scala Training function.
   */
 object XGBoost {
 
+  private[scala] def trainAndSaveCheckpoint(
+      dtrain: DMatrix,
+      params: Map[String, Any],
+      numRounds: Int,
+      watches: Map[String, DMatrix] = Map(),
+      metrics: Array[Array[Float]] = null,
+      obj: ObjectiveTrait = null,
+      eval: EvalTrait = null,
+      earlyStoppingRound: Int = 0,
+      prevBooster: Booster,
+      checkpointParams: Option[ExternalCheckpointParams]): Booster = {
+    val jWatches = watches.mapValues(_.jDMatrix).asJava
+    val jBooster = if (prevBooster == null) {
+      null
+    } else {
+      prevBooster.booster
+    }
+    val xgboostInJava = checkpointParams.
+      map(cp => {
+          JXGBoost.trainAndSaveCheckpoint(
+            dtrain.jDMatrix,
+            // we have to filter null value for customized obj and eval
+            params.filter(_._2 != null).mapValues(_.toString.asInstanceOf[AnyRef]).asJava,
+            numRounds, jWatches, metrics, obj, eval, earlyStoppingRound, jBooster,
+            cp.checkpointInterval,
+            cp.checkpointPath,
+            new Path(cp.checkpointPath).getFileSystem(new Configuration()))
+        }).
+      getOrElse(
+        JXGBoost.train(
+          dtrain.jDMatrix,
+          // we have to filter null value for customized obj and eval
+          params.filter(_._2 != null).mapValues(_.toString.asInstanceOf[AnyRef]).asJava,
+          numRounds, jWatches, metrics, obj, eval, earlyStoppingRound, jBooster)
+      )
+    if (prevBooster == null) {
+      new Booster(xgboostInJava)
+    } else {
+      // Avoid creating a new SBooster with the same JBooster
+      prevBooster
+    }
+  }
+
   /**
     * Train a booster given parameters.
     *
@@ -55,23 +101,8 @@ object XGBoost {
       eval: EvalTrait = null,
       earlyStoppingRound: Int = 0,
       booster: Booster = null): Booster = {
-    val jWatches = watches.mapValues(_.jDMatrix).asJava
-    val jBooster = if (booster == null) {
-      null
-    } else {
-      booster.booster
-    }
-    val xgboostInJava = JXGBoost.train(
-      dtrain.jDMatrix,
-      // we have to filter null value for customized obj and eval
-      params.filter(_._2 != null).mapValues(_.toString.asInstanceOf[AnyRef]).asJava,
-      round, jWatches, metrics, obj, eval, earlyStoppingRound, jBooster)
-    if (booster == null) {
-      new Booster(xgboostInJava)
-    } else {
-      // Avoid creating a new SBooster with the same JBooster
-      booster
-    }
+    trainAndSaveCheckpoint(dtrain, params, round, watches, metrics, obj, eval, earlyStoppingRound,
+      booster, None)
   }
 
   /**
@@ -126,3 +157,41 @@ object XGBoost {
     new Booster(xgboostInJava)
   }
 }
+
+private[scala] case class ExternalCheckpointParams(
+    checkpointInterval: Int,
+    checkpointPath: String,
+    skipCleanCheckpoint: Boolean)
+
+private[scala] object ExternalCheckpointParams {
+
+  def extractParams(params: Map[String, Any]): Option[ExternalCheckpointParams] = {
+    val checkpointPath: String = params.get("checkpoint_path") match {
+      case None | Some(null) | Some("") => null
+      case Some(path: String) => path
+      case _ => throw new IllegalArgumentException("parameter \"checkpoint_path\" must be" +
+        s" an instance of String, but current value is ${params("checkpoint_path")}")
+    }
+
+    val checkpointInterval: Int = params.get("checkpoint_interval") match {
+      case None => 0
+      case Some(freq: Int) => freq
+      case _ => throw new IllegalArgumentException("parameter \"checkpoint_interval\" must be" +
+        " an instance of Int.")
+    }
+
+    val skipCleanCheckpointFile: Boolean = params.get("skip_clean_checkpoint") match {
+      case None => false
+      case Some(skipCleanCheckpoint: Boolean) => skipCleanCheckpoint
+      case _ => throw new IllegalArgumentException("parameter \"skip_clean_checkpoint\" must be" +
+        " an instance of Boolean")
+    }
+    if (checkpointPath == null || checkpointInterval == 0) {
+      None
+    } else {
+      Some(ExternalCheckpointParams(checkpointInterval, checkpointPath, skipCleanCheckpointFile))
+    }
+  }
+}
+
+

python-package/setup.py

@@ -62,6 +62,7 @@ setup(name='xgboost',
                    'Programming Language :: Python :: 3',
                    'Programming Language :: Python :: 3.5',
                    'Programming Language :: Python :: 3.6',
-                   'Programming Language :: Python :: 3.7'],
+                   'Programming Language :: Python :: 3.7',
+                   'Programming Language :: Python :: 3.8'],
       python_requires='>=3.5',
       url='https://github.com/dmlc/xgboost')

python-package/setup_pip.py

@@ -79,6 +79,7 @@ setup(name='xgboost',
                    'Programming Language :: Python :: 3',
                    'Programming Language :: Python :: 3.5',
                    'Programming Language :: Python :: 3.6',
-                   'Programming Language :: Python :: 3.7'],
+                   'Programming Language :: Python :: 3.7',
+                   'Programming Language :: Python :: 3.8'],
       python_requires='>=3.5',
       url='https://github.com/dmlc/xgboost')

python-package/xgboost/VERSION

@@ -1 +1 @@
-1.0.0-SNAPSHOT
+1.0.0rc2

python-package/xgboost/dask.py

@@ -600,6 +600,7 @@ class DaskXGBRegressor(DaskScikitLearnBase):
         results = train(self.client, params, dtrain,
                         num_boost_round=self.get_num_boosting_rounds(),
                         evals=evals)
+        # pylint: disable=attribute-defined-outside-init
         self._Booster = results['booster']
         # pylint: disable=attribute-defined-outside-init
         self.evals_result_ = results['history']

python-package/xgboost/sklearn.py

@@ -200,7 +200,7 @@ Parameters
 @xgboost_model_doc("""Implementation of the Scikit-Learn API for XGBoost.""",
                    ['estimators', 'model', 'objective'])
 class XGBModel(XGBModelBase):
-    # pylint: disable=too-many-arguments, too-many-instance-attributes, invalid-name, missing-docstring
+    # pylint: disable=too-many-arguments, too-many-instance-attributes, missing-docstring
     def __init__(self, max_depth=None, learning_rate=None, n_estimators=100,
                  verbosity=None, objective=None, booster=None,
                  tree_method=None, n_jobs=None, gamma=None,
@@ -210,7 +210,8 @@ class XGBModel(XGBModelBase):
                  scale_pos_weight=None, base_score=None, random_state=None,
                  missing=None, num_parallel_tree=None,
                  monotone_constraints=None, interaction_constraints=None,
-                 importance_type="gain", gpu_id=None, **kwargs):
+                 importance_type="gain", gpu_id=None,
+                 validate_parameters=False, **kwargs):
         if not SKLEARN_INSTALLED:
             raise XGBoostError(
                 'sklearn needs to be installed in order to use this module')
@@ -243,6 +244,10 @@ class XGBModel(XGBModelBase):
         self.interaction_constraints = interaction_constraints
         self.importance_type = importance_type
         self.gpu_id = gpu_id
+        # Parameter validation is not working with Scikit-Learn interface, as
+        # it passes all paraemters into XGBoost core, whether they are used or
+        # not.
+        self.validate_parameters = validate_parameters
 
     def __setstate__(self, state):
         # backward compatibility code
@@ -314,11 +319,35 @@ class XGBModel(XGBModelBase):
         if isinstance(params['random_state'], np.random.RandomState):
             params['random_state'] = params['random_state'].randint(
                 np.iinfo(np.int32).max)
-        # Parameter validation is not working with Scikit-Learn interface, as
-        # it passes all paraemters into XGBoost core, whether they are used or
-        # not.
-        if 'validate_parameters' not in params.keys():
-            params['validate_parameters'] = False
+
+        def parse_parameter(value):
+            for t in (int, float):
+                try:
+                    ret = t(value)
+                    return ret
+                except ValueError:
+                    continue
+            return None
+
+        # Get internal parameter values
+        try:
+            config = json.loads(self.get_booster().save_config())
+            stack = [config]
+            internal = {}
+            while stack:
+                obj = stack.pop()
+                for k, v in obj.items():
+                    if k.endswith('_param'):
+                        for p_k, p_v in v.items():
+                            internal[p_k] = p_v
+                    elif isinstance(v, dict):
+                        stack.append(v)
+
+            for k, v in internal.items():
+                if k in params.keys() and params[k] is None:
+                    params[k] = parse_parameter(v)
+        except XGBoostError:
+            pass
         return params
 
     def get_xgb_params(self):

src/common/observer.h

@@ -15,12 +15,23 @@
 #include "xgboost/base.h"
 #include "xgboost/tree_model.h"
 
+#if defined(XGBOOST_STRICT_R_MODE)
+#define OBSERVER_PRINT LOG(INFO)
+#define OBSERVER_ENDL ""
+#define OBSERVER_NEWLINE ""
+#else
+#define OBSERVER_PRINT std::cout
+#define OBSERVER_ENDL std::endl
+#define OBSERVER_NEWLINE "\n"
+#endif  // defined(XGBOOST_STRICT_R_MODE)
+
 namespace xgboost {
 /*\brief  An observer for logging internal data structures.
  *
  *  This class is designed to be `diff` tool friendly, which means it uses plain
  *  `std::cout` for printing to avoid the time information emitted by `LOG(DEBUG)` or
- *  similiar facilities.
+ *  similiar facilities. Exception: use `LOG(INFO)` for the R package, to comply
+ *  with CRAN policy.
  */
 class TrainingObserver {
 #if defined(XGBOOST_USE_DEBUG_OUTPUT)
@@ -32,17 +43,17 @@ class TrainingObserver {
  public:
   void Update(int32_t iter) const {
     if (XGBOOST_EXPECT(!observe_, true)) { return; }
-    std::cout << "Iter: " << iter << std::endl;
+    OBSERVER_PRINT << "Iter: " << iter << OBSERVER_ENDL;
   }
   /*\brief Observe tree. */
   void Observe(RegTree const& tree) {
     if (XGBOOST_EXPECT(!observe_, true)) { return; }
-    std::cout << "Tree:" << std::endl;
+    OBSERVER_PRINT << "Tree:" << OBSERVER_ENDL;
     Json j_tree {Object()};
     tree.SaveModel(&j_tree);
     std::string str;
     Json::Dump(j_tree, &str, true);
-    std::cout << str << std::endl;
+    OBSERVER_PRINT << str << OBSERVER_ENDL;
   }
   /*\brief Observe tree. */
   void Observe(RegTree const* p_tree) {
@@ -54,15 +65,15 @@ class TrainingObserver {
   template <typename T>
   void Observe(std::vector<T> const& h_vec, std::string name) const {
     if (XGBOOST_EXPECT(!observe_, true)) { return; }
-    std::cout << "Procedure: " << name << std::endl;
+    OBSERVER_PRINT << "Procedure: " << name << OBSERVER_ENDL;
 
     for (size_t i = 0; i < h_vec.size(); ++i) {
-      std::cout << h_vec[i] << ", ";
+      OBSERVER_PRINT << h_vec[i] << ", ";
       if (i % 8 == 0) {
-        std::cout << '\n';
+        OBSERVER_PRINT << OBSERVER_NEWLINE;
       }
     }
-    std::cout << std::endl;
+    OBSERVER_PRINT << OBSERVER_ENDL;
   }
   /*\brief Observe data hosted by `HostDeviceVector'. */
   template <typename T>
@@ -85,16 +96,16 @@ class TrainingObserver {
     if (XGBOOST_EXPECT(!observe_, true)) { return; }
 
     Json obj {toJson(p)};
-    std::cout << "Parameter: " << name << ":\n" << obj << std::endl;
+    OBSERVER_PRINT << "Parameter: " << name << ":\n" << obj << OBSERVER_ENDL;
   }
   /*\brief Observe parameters provided by users. */
   void Observe(Args const& args) const {
     if (XGBOOST_EXPECT(!observe_, true)) { return; }
 
     for (auto kv : args) {
-      std::cout << kv.first << ": " << kv.second << "\n";
+      OBSERVER_PRINT << kv.first << ": " << kv.second << OBSERVER_NEWLINE;
     }
-    std::cout << std::endl;
+    OBSERVER_PRINT << OBSERVER_ENDL;
   }
 
   /*\brief Get a global instance. */

src/common/timer.cc

@@ -89,7 +89,7 @@ void Monitor::PrintStatistics(StatMap const& statistics) const {
           "Timer for " << kv.first << " did not get stopped properly.";
       continue;
     }
-    std::cout << kv.first << ": " << static_cast<double>(kv.second.second) / 1e+6
+    LOG(CONSOLE) << kv.first << ": " << static_cast<double>(kv.second.second) / 1e+6
                  << "s, " << kv.second.first << " calls @ "
                  << kv.second.second
                  << "us" << std::endl;
@@ -107,10 +107,9 @@ void Monitor::Print() const {
     if (rabit::GetRank() == 0) {
       LOG(CONSOLE) << "======== Monitor: " << label << " ========";
       for (size_t i = 0; i < world.size(); ++i) {
-        std::cout << "From rank: " << i << ": " << std::endl;
+        LOG(CONSOLE) << "From rank: " << i << ": " << std::endl;
         auto const& statistic = world[i];
         this->PrintStatistics(statistic);
-        std::cout << std::endl;
       }
     }
   } else {
@@ -123,7 +122,6 @@ void Monitor::Print() const {
     LOG(CONSOLE) << "======== Monitor: " << label << " ========";
     this->PrintStatistics(stat_map);
   }
-  std::cout << std::endl;
 }
 
 }  // namespace common

src/learner.cc

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2014-2019 by Contributors
+ * Copyright 2014-2020 by Contributors
  * \file learner.cc
  * \brief Implementation of learning algorithm.
  * \author Tianqi Chen
@@ -67,19 +67,26 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
   /* \brief global bias */
   bst_float base_score;
   /* \brief number of features  */
-  unsigned num_feature;
+  uint32_t num_feature;
   /* \brief number of classes, if it is multi-class classification  */
-  int num_class;
+  int32_t num_class;
   /*! \brief Model contain additional properties */
-  int contain_extra_attrs;
+  int32_t contain_extra_attrs;
   /*! \brief Model contain eval metrics */
-  int contain_eval_metrics;
+  int32_t contain_eval_metrics;
+  /*! \brief the version of XGBoost. */
+  uint32_t major_version;
+  uint32_t minor_version;
   /*! \brief reserved field */
-  int reserved[29];
+  int reserved[27];
   /*! \brief constructor */
   LearnerModelParamLegacy() {
     std::memset(this, 0, sizeof(LearnerModelParamLegacy));
     base_score = 0.5f;
+    major_version = std::get<0>(Version::Self());
+    minor_version = std::get<1>(Version::Self());
+    static_assert(sizeof(LearnerModelParamLegacy) == 136,
+                  "Do not change the size of this struct, as it will break binary IO.");
   }
   // Skip other legacy fields.
   Json ToJson() const {
@@ -118,7 +125,8 @@ LearnerModelParam::LearnerModelParam(
     : base_score{base_margin}, num_feature{user_param.num_feature},
       num_output_group{user_param.num_class == 0
                        ? 1
-                           : static_cast<uint32_t>(user_param.num_class)} {}
+                       : static_cast<uint32_t>(user_param.num_class)}
+{}
 
 struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
   // data split mode, can be row, col, or none.
@@ -140,7 +148,7 @@ struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
         .describe("Data split mode for distributed training.");
     DMLC_DECLARE_FIELD(disable_default_eval_metric)
         .set_default(0)
-        .describe("flag to disable default metric. Set to >0 to disable");
+        .describe("Flag to disable default metric. Set to >0 to disable");
     DMLC_DECLARE_FIELD(booster)
         .set_default("gbtree")
         .describe("Gradient booster used for training.");
@@ -200,6 +208,7 @@ class LearnerImpl : public Learner {
     Args args = {cfg_.cbegin(), cfg_.cend()};
 
     tparam_.UpdateAllowUnknown(args);
+    auto mparam_backup = mparam_;
     mparam_.UpdateAllowUnknown(args);
     generic_parameters_.UpdateAllowUnknown(args);
     generic_parameters_.CheckDeprecated();
@@ -217,17 +226,33 @@ class LearnerImpl : public Learner {
 
     // set seed only before the model is initialized
     common::GlobalRandom().seed(generic_parameters_.seed);
+
     // must precede configure gbm since num_features is required for gbm
     this->ConfigureNumFeatures();
     args = {cfg_.cbegin(), cfg_.cend()};  // renew
     this->ConfigureObjective(old_tparam, &args);
-    this->ConfigureGBM(old_tparam, args);
-    this->ConfigureMetrics(args);
-
-    generic_parameters_.ConfigureGpuId(this->gbm_->UseGPU());
 
+    // Before 1.0.0, we save `base_score` into binary as a transformed value by objective.
+    // After 1.0.0 we save the value provided by user and keep it immutable instead.  To
+    // keep the stability, we initialize it in binary LoadModel instead of configuration.
+    // Under what condition should we omit the transformation:
+    //
+    // - base_score is loaded from old binary model.
+    //
+    // What are the other possible conditions:
+    //
+    // - model loaded from new binary or JSON.
+    // - model is created from scratch.
+    // - model is configured second time due to change of parameter
+    if (!learner_model_param_.Initialized() || mparam_.base_score != mparam_backup.base_score) {
       learner_model_param_ = LearnerModelParam(mparam_,
                                                obj_->ProbToMargin(mparam_.base_score));
+    }
+
+    this->ConfigureGBM(old_tparam, args);
+    generic_parameters_.ConfigureGpuId(this->gbm_->UseGPU());
+
+    this->ConfigureMetrics(args);
 
     this->need_configuration_ = false;
     if (generic_parameters_.validate_parameters) {
@@ -269,10 +294,7 @@ class LearnerImpl : public Learner {
         }
       }
     }
-    auto learner_model_param = mparam_.ToJson();
-    for (auto const& kv : get<Object>(learner_model_param)) {
-      keys.emplace_back(kv.first);
-    }
+
     keys.emplace_back(kEvalMetric);
     keys.emplace_back("verbosity");
     keys.emplace_back("num_output_group");
@@ -340,9 +362,6 @@ class LearnerImpl : public Learner {
                                        cache_));
     gbm_->LoadModel(gradient_booster);
 
-    learner_model_param_ = LearnerModelParam(mparam_,
-                                             obj_->ProbToMargin(mparam_.base_score));
-
     auto const& j_attributes = get<Object const>(learner.at("attributes"));
     attributes_.clear();
     for (auto const& kv : j_attributes) {
@@ -425,6 +444,7 @@ class LearnerImpl : public Learner {
     auto& learner_parameters = out["learner"];
 
     learner_parameters["learner_train_param"] = toJson(tparam_);
+    learner_parameters["learner_model_param"] = mparam_.ToJson();
     learner_parameters["gradient_booster"] = Object();
     auto& gradient_booster = learner_parameters["gradient_booster"];
     gbm_->SaveConfig(&gradient_booster);
@@ -461,6 +481,7 @@ class LearnerImpl : public Learner {
     }
 
     if (header[0] == '{') {
+      // Dispatch to JSON
       auto json_stream = common::FixedSizeStream(&fp);
       std::string buffer;
       json_stream.Take(&buffer);
@@ -473,25 +494,10 @@ class LearnerImpl : public Learner {
     // read parameter
     CHECK_EQ(fi->Read(&mparam_, sizeof(mparam_)), sizeof(mparam_))
         << "BoostLearner: wrong model format";
-    {
-      // backward compatibility code for compatible with old model type
-      // for new model, Read(&name_obj_) is suffice
-      uint64_t len;
-      CHECK_EQ(fi->Read(&len, sizeof(len)), sizeof(len));
-      if (len >= std::numeric_limits<unsigned>::max()) {
-        int gap;
-        CHECK_EQ(fi->Read(&gap, sizeof(gap)), sizeof(gap))
-            << "BoostLearner: wrong model format";
-        len = len >> static_cast<uint64_t>(32UL);
-      }
-      if (len != 0) {
-        tparam_.objective.resize(len);
-        CHECK_EQ(fi->Read(&tparam_.objective[0], len), len)
-            << "BoostLearner: wrong model format";
-      }
-    }
+
+    CHECK(fi->Read(&tparam_.objective)) << "BoostLearner: wrong model format";
     CHECK(fi->Read(&tparam_.booster)) << "BoostLearner: wrong model format";
-    // duplicated code with LazyInitModel
+
     obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
     gbm_.reset(GradientBooster::Create(tparam_.booster, &generic_parameters_,
                                        &learner_model_param_, cache_));
@@ -510,34 +516,57 @@ class LearnerImpl : public Learner {
       }
       attributes_ = std::map<std::string, std::string>(attr.begin(), attr.end());
     }
-    if (tparam_.objective == "count:poisson") {
-      std::string max_delta_step;
-      fi->Read(&max_delta_step);
-      cfg_["max_delta_step"] = max_delta_step;
+    bool warn_old_model { false };
+    if (attributes_.find("count_poisson_max_delta_step") != attributes_.cend()) {
+      // Loading model from < 1.0.0, objective is not saved.
+      cfg_["max_delta_step"] = attributes_.at("count_poisson_max_delta_step");
+      attributes_.erase("count_poisson_max_delta_step");
+      warn_old_model = true;
+    } else {
+      warn_old_model = false;
     }
-    if (mparam_.contain_eval_metrics != 0) {
-      std::vector<std::string> metr;
-      fi->Read(&metr);
-      for (auto name : metr) {
-        metrics_.emplace_back(Metric::Create(name, &generic_parameters_));
+
+    if (mparam_.major_version >= 1) {
+      learner_model_param_ = LearnerModelParam(mparam_,
+                                               obj_->ProbToMargin(mparam_.base_score));
+    } else {
+      // Before 1.0.0, base_score is saved as a transformed value, and there's no version
+      // attribute in the saved model.
+      learner_model_param_ = LearnerModelParam(mparam_, mparam_.base_score);
+      warn_old_model = true;
+    }
+    if (attributes_.find("objective") != attributes_.cend()) {
+      auto obj_str = attributes_.at("objective");
+      auto j_obj = Json::Load({obj_str.c_str(), obj_str.size()});
+      obj_->LoadConfig(j_obj);
+      attributes_.erase("objective");
+    } else {
+      warn_old_model = true;
+    }
+    if (attributes_.find("metrics") != attributes_.cend()) {
+      auto metrics_str = attributes_.at("metrics");
+      std::vector<std::string> names { common::Split(metrics_str, ';') };
+      attributes_.erase("metrics");
+      for (auto const& n : names) {
+        this->SetParam(kEvalMetric, n);
       }
     }
 
+    if (warn_old_model) {
+      LOG(WARNING) << "Loading model from XGBoost < 1.0.0, consider saving it "
+                      "again for improved compatibility";
+    }
+
+    // Renew the version.
+    mparam_.major_version = std::get<0>(Version::Self());
+    mparam_.minor_version = std::get<1>(Version::Self());
+
     cfg_["num_class"] = common::ToString(mparam_.num_class);
     cfg_["num_feature"] = common::ToString(mparam_.num_feature);
 
     auto n = tparam_.__DICT__();
     cfg_.insert(n.cbegin(), n.cend());
 
-    Args args = {cfg_.cbegin(), cfg_.cend()};
-    generic_parameters_.UpdateAllowUnknown(args);
-    gbm_->Configure(args);
-    obj_->Configure({cfg_.begin(), cfg_.end()});
-
-    for (auto& p_metric : metrics_) {
-      p_metric->Configure({cfg_.begin(), cfg_.end()});
-    }
-
     // copy dsplit from config since it will not run again during restore
     if (tparam_.dsplit == DataSplitMode::kAuto && rabit::IsDistributed()) {
       tparam_.dsplit = DataSplitMode::kRow;
@@ -554,15 +583,8 @@ class LearnerImpl : public Learner {
   void SaveModel(dmlc::Stream* fo) const override {
     LearnerModelParamLegacy mparam = mparam_;  // make a copy to potentially modify
     std::vector<std::pair<std::string, std::string> > extra_attr;
-    // extra attributed to be added just before saving
-    if (tparam_.objective == "count:poisson") {
-      auto it = cfg_.find("max_delta_step");
-      if (it != cfg_.end()) {
-        // write `max_delta_step` parameter as extra attribute of booster
     mparam.contain_extra_attrs = 1;
-        extra_attr.emplace_back("count_poisson_max_delta_step", it->second);
-      }
-    }
+
     {
       std::vector<std::string> saved_params;
       // check if rabit_bootstrap_cache were set to non zero before adding to checkpoint
@@ -579,6 +601,24 @@ class LearnerImpl : public Learner {
         }
       }
     }
+    {
+      // Similar to JSON model IO, we save the objective.
+      Json j_obj { Object() };
+      obj_->SaveConfig(&j_obj);
+      std::string obj_doc;
+      Json::Dump(j_obj, &obj_doc);
+      extra_attr.emplace_back("objective", obj_doc);
+    }
+    // As of 1.0.0, JVM Package and R Package uses Save/Load model for serialization.
+    // Remove this part once they are ported to use actual serialization methods.
+    if (mparam.contain_eval_metrics != 0) {
+      std::stringstream os;
+      for (auto& ev : metrics_) {
+        os << ev->Name() << ";";
+      }
+      extra_attr.emplace_back("metrics", os.str());
+    }
+
     fo->Write(&mparam, sizeof(LearnerModelParamLegacy));
     fo->Write(tparam_.objective);
     fo->Write(tparam_.booster);
@@ -591,25 +631,6 @@ class LearnerImpl : public Learner {
       fo->Write(std::vector<std::pair<std::string, std::string>>(
           attr.begin(), attr.end()));
     }
-    if (tparam_.objective == "count:poisson") {
-      auto it = cfg_.find("max_delta_step");
-      if (it != cfg_.end()) {
-        fo->Write(it->second);
-      } else {
-        // recover value of max_delta_step from extra attributes
-        auto it2 = attributes_.find("count_poisson_max_delta_step");
-        const std::string max_delta_step
-          = (it2 != attributes_.end()) ? it2->second : kMaxDeltaStepDefaultValue;
-        fo->Write(max_delta_step);
-      }
-    }
-    if (mparam.contain_eval_metrics != 0) {
-      std::vector<std::string> metr;
-      for (auto& ev : metrics_) {
-        metr.emplace_back(ev->Name());
-      }
-      fo->Write(metr);
-    }
   }
 
   void Save(dmlc::Stream* fo) const override {
@@ -663,11 +684,13 @@ class LearnerImpl : public Learner {
 
   If you are loading a serialized model (like pickle in Python) generated by older
   XGBoost, please export the model by calling `Booster.save_model` from that version
-  first, then load it back in current version.  See:
+  first, then load it back in current version.  There's a simple script for helping
+  the process. See:
 
     https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
 
-  for more details about differences between saving model and serializing.
+  for reference to the script, and more details about differences between saving model and
+  serializing.
 
 )doc";
       int64_t sz {-1};
@@ -856,7 +879,8 @@ class LearnerImpl : public Learner {
 
   void ConfigureObjective(LearnerTrainParam const& old, Args* p_args) {
     // Once binary IO is gone, NONE of these config is useful.
-    if (cfg_.find("num_class") != cfg_.cend() && cfg_.at("num_class") != "0") {
+    if (cfg_.find("num_class") != cfg_.cend() && cfg_.at("num_class") != "0" &&
+        tparam_.objective != "multi:softprob") {
       cfg_["num_output_group"] = cfg_["num_class"];
       if (atoi(cfg_["num_class"].c_str()) > 1 && cfg_.count("objective") == 0) {
         tparam_.objective = "multi:softmax";
@@ -921,7 +945,6 @@ class LearnerImpl : public Learner {
     }
     CHECK_NE(mparam_.num_feature, 0)
         << "0 feature is supplied.  Are you using raw Booster interface?";
-    learner_model_param_.num_feature = mparam_.num_feature;
     // Remove these once binary IO is gone.
     cfg_["num_feature"] = common::ToString(mparam_.num_feature);
     cfg_["num_class"] = common::ToString(mparam_.num_class);

tests/ci_build/Dockerfile.cpu

@@ -21,8 +21,9 @@ ENV GOSU_VERSION 1.10
 
 # Install Python packages
 RUN \
-    pip install pyyaml cpplint pylint astroid sphinx numpy scipy pandas matplotlib sh recommonmark guzzle_sphinx_theme mock \
-                breathe matplotlib graphviz pytest scikit-learn wheel kubernetes urllib3 jsonschema && \
+    pip install pyyaml cpplint pylint astroid sphinx numpy scipy pandas matplotlib sh \
+    		recommonmark guzzle_sphinx_theme mock breathe matplotlib graphviz \
+		pytest scikit-learn wheel kubernetes urllib3 jsonschema boto3 && \
     pip install https://h2o-release.s3.amazonaws.com/datatable/stable/datatable-0.7.0/datatable-0.7.0-cp37-cp37m-linux_x86_64.whl && \
     pip install "dask[complete]"
 

tests/cpp/test_learner.cc

@@ -180,6 +180,41 @@ TEST(Learner, JsonModelIO) {
   delete pp_dmat;
 }
 
+TEST(Learner, BinaryModelIO) {
+  size_t constexpr kRows = 8;
+  int32_t constexpr kIters = 4;
+  auto pp_dmat = CreateDMatrix(kRows, 10, 0);
+  std::shared_ptr<DMatrix> p_dmat {*pp_dmat};
+  p_dmat->Info().labels_.Resize(kRows);
+
+  std::unique_ptr<Learner> learner{Learner::Create({p_dmat})};
+  learner->SetParam("eval_metric", "rmsle");
+  learner->Configure();
+  for (int32_t iter = 0; iter < kIters; ++iter) {
+    learner->UpdateOneIter(iter, p_dmat.get());
+  }
+  dmlc::TemporaryDirectory tempdir;
+  std::string const fname = tempdir.path + "binary_model_io.bin";
+  {
+    // Make sure the write is complete before loading.
+    std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
+    learner->SaveModel(fo.get());
+  }
+
+  learner.reset(Learner::Create({p_dmat}));
+  std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
+  learner->LoadModel(fi.get());
+  learner->Configure();
+  Json config { Object() };
+  learner->SaveConfig(&config);
+  std::string config_str;
+  Json::Dump(config, &config_str);
+  ASSERT_NE(config_str.find("rmsle"), std::string::npos);
+  ASSERT_EQ(config_str.find("WARNING"), std::string::npos);
+
+  delete pp_dmat;
+}
+
 #if defined(XGBOOST_USE_CUDA)
 // Tests for automatic GPU configuration.
 TEST(Learner, GPUConfiguration) {

tests/python/generate_models.py

@@ -0,0 +1,148 @@
+import xgboost
+import numpy as np
+import os
+
+kRounds = 2
+kRows = 1000
+kCols = 4
+kForests = 2
+kMaxDepth = 2
+kClasses = 3
+
+X = np.random.randn(kRows, kCols)
+w = np.random.uniform(size=kRows)
+
+version = xgboost.__version__
+
+np.random.seed(1994)
+target_dir = 'models'
+
+
+def booster_bin(model):
+    return os.path.join(target_dir,
+                        'xgboost-' + version + '.' + model + '.bin')
+
+
+def booster_json(model):
+    return os.path.join(target_dir,
+                        'xgboost-' + version + '.' + model + '.json')
+
+
+def skl_bin(model):
+    return os.path.join(target_dir,
+                        'xgboost_scikit-' + version + '.' + model + '.bin')
+
+
+def skl_json(model):
+    return os.path.join(target_dir,
+                        'xgboost_scikit-' + version + '.' + model + '.json')
+
+
+def generate_regression_model():
+    print('Regression')
+    y = np.random.randn(kRows)
+
+    data = xgboost.DMatrix(X, label=y, weight=w)
+    booster = xgboost.train({'tree_method': 'hist',
+                             'num_parallel_tree': kForests,
+                             'max_depth': kMaxDepth},
+                            num_boost_round=kRounds, dtrain=data)
+    booster.save_model(booster_bin('reg'))
+    booster.save_model(booster_json('reg'))
+
+    reg = xgboost.XGBRegressor(tree_method='hist',
+                               num_parallel_tree=kForests,
+                               max_depth=kMaxDepth,
+                               n_estimators=kRounds)
+    reg.fit(X, y, w)
+    reg.save_model(skl_bin('reg'))
+    reg.save_model(skl_json('reg'))
+
+
+def generate_logistic_model():
+    print('Logistic')
+    y = np.random.randint(0, 2, size=kRows)
+    assert y.max() == 1 and y.min() == 0
+
+    data = xgboost.DMatrix(X, label=y, weight=w)
+    booster = xgboost.train({'tree_method': 'hist',
+                             'num_parallel_tree': kForests,
+                             'max_depth': kMaxDepth,
+                             'objective': 'binary:logistic'},
+                            num_boost_round=kRounds, dtrain=data)
+    booster.save_model(booster_bin('logit'))
+    booster.save_model(booster_json('logit'))
+
+    reg = xgboost.XGBClassifier(tree_method='hist',
+                                num_parallel_tree=kForests,
+                                max_depth=kMaxDepth,
+                                n_estimators=kRounds)
+    reg.fit(X, y, w)
+    reg.save_model(skl_bin('logit'))
+    reg.save_model(skl_json('logit'))
+
+
+def generate_classification_model():
+    print('Classification')
+    y = np.random.randint(0, kClasses, size=kRows)
+    data = xgboost.DMatrix(X, label=y, weight=w)
+    booster = xgboost.train({'num_class': kClasses,
+                             'tree_method': 'hist',
+                             'num_parallel_tree': kForests,
+                             'max_depth': kMaxDepth},
+                            num_boost_round=kRounds, dtrain=data)
+    booster.save_model(booster_bin('cls'))
+    booster.save_model(booster_json('cls'))
+
+    cls = xgboost.XGBClassifier(tree_method='hist',
+                                num_parallel_tree=kForests,
+                                max_depth=kMaxDepth,
+                                n_estimators=kRounds)
+    cls.fit(X, y, w)
+    cls.save_model(skl_bin('cls'))
+    cls.save_model(skl_json('cls'))
+
+
+def generate_ranking_model():
+    print('Learning to Rank')
+    y = np.random.randint(5, size=kRows)
+    w = np.random.uniform(size=20)
+    g = np.repeat(50, 20)
+
+    data = xgboost.DMatrix(X, y, weight=w)
+    data.set_group(g)
+    booster = xgboost.train({'objective': 'rank:ndcg',
+                             'num_parallel_tree': kForests,
+                             'tree_method': 'hist',
+                             'max_depth': kMaxDepth},
+                            num_boost_round=kRounds,
+                            dtrain=data)
+    booster.save_model(booster_bin('ltr'))
+    booster.save_model(booster_json('ltr'))
+
+    ranker = xgboost.sklearn.XGBRanker(n_estimators=kRounds,
+                                       tree_method='hist',
+                                       objective='rank:ndcg',
+                                       max_depth=kMaxDepth,
+                                       num_parallel_tree=kForests)
+    ranker.fit(X, y, g, sample_weight=w)
+    ranker.save_model(skl_bin('ltr'))
+    ranker.save_model(skl_json('ltr'))
+
+
+def write_versions():
+    versions = {'numpy': np.__version__,
+                'xgboost': version}
+    with open(os.path.join(target_dir, 'version'), 'w') as fd:
+        fd.write(str(versions))
+
+
+if __name__ == '__main__':
+    if not os.path.exists(target_dir):
+        os.mkdir(target_dir)
+
+    generate_regression_model()
+    generate_logistic_model()
+    generate_classification_model()
+    generate_ranking_model()
+    write_versions()

tests/python/test_basic.py

@@ -39,7 +39,7 @@ class TestBasic(unittest.TestCase):
     def test_basic(self):
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
         dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
-        param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
+        param = {'max_depth': 2, 'eta': 1,
                  'objective': 'binary:logistic'}
         # specify validations set to watch performance
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]

tests/python/test_basic_models.py

@@ -284,16 +284,31 @@ class TestModels(unittest.TestCase):
         self.assertRaises(ValueError, bst.predict, dm1)
         bst.predict(dm2)  # success
 
+    def test_model_binary_io(self):
+        model_path = 'test_model_binary_io.bin'
+        parameters = {'tree_method': 'hist', 'booster': 'gbtree',
+                      'scale_pos_weight': '0.5'}
+        X = np.random.random((10, 3))
+        y = np.random.random((10,))
+        dtrain = xgb.DMatrix(X, y)
+        bst = xgb.train(parameters, dtrain, num_boost_round=2)
+        bst.save_model(model_path)
+        bst = xgb.Booster(model_file=model_path)
+        os.remove(model_path)
+        config = json.loads(bst.save_config())
+        assert float(config['learner']['objective'][
+            'reg_loss_param']['scale_pos_weight']) == 0.5
+
     def test_model_json_io(self):
-        model_path = './model.json'
+        model_path = 'test_model_json_io.json'
         parameters = {'tree_method': 'hist', 'booster': 'gbtree'}
         j_model = json_model(model_path, parameters)
         assert isinstance(j_model['learner'], dict)
 
-        bst = xgb.Booster(model_file='./model.json')
+        bst = xgb.Booster(model_file=model_path)
 
         bst.save_model(fname=model_path)
-        with open('./model.json', 'r') as fd:
+        with open(model_path, 'r') as fd:
             j_model = json.load(fd)
         assert isinstance(j_model['learner'], dict)
 
@@ -302,7 +317,7 @@ class TestModels(unittest.TestCase):
     @pytest.mark.skipif(**tm.no_json_schema())
     def test_json_schema(self):
         import jsonschema
-        model_path = './model.json'
+        model_path = 'test_json_schema.json'
         path = os.path.dirname(
             os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
         doc = os.path.join(path, 'doc', 'model.schema')

tests/python/test_model_compatibility.py

@@ -0,0 +1,130 @@
+import xgboost
+import os
+import generate_models as gm
+import json
+import zipfile
+import pytest
+
+
+def run_model_param_check(config):
+    assert config['learner']['learner_model_param']['num_feature'] == str(4)
+    assert config['learner']['learner_train_param']['booster'] == 'gbtree'
+
+
+def run_booster_check(booster, name):
+    config = json.loads(booster.save_config())
+    run_model_param_check(config)
+    if name.find('cls') != -1:
+        assert (len(booster.get_dump()) == gm.kForests * gm.kRounds *
+                gm.kClasses)
+        assert float(
+            config['learner']['learner_model_param']['base_score']) == 0.5
+        assert config['learner']['learner_train_param'][
+            'objective'] == 'multi:softmax'
+    elif name.find('logit') != -1:
+        assert len(booster.get_dump()) == gm.kForests * gm.kRounds
+        assert config['learner']['learner_model_param']['num_class'] == str(0)
+        assert config['learner']['learner_train_param'][
+            'objective'] == 'binary:logistic'
+    elif name.find('ltr') != -1:
+        assert config['learner']['learner_train_param'][
+            'objective'] == 'rank:ndcg'
+    else:
+        assert name.find('reg') != -1
+        assert len(booster.get_dump()) == gm.kForests * gm.kRounds
+        assert float(
+            config['learner']['learner_model_param']['base_score']) == 0.5
+        assert config['learner']['learner_train_param'][
+            'objective'] == 'reg:squarederror'
+
+
+def run_scikit_model_check(name, path):
+    if name.find('reg') != -1:
+        reg = xgboost.XGBRegressor()
+        reg.load_model(path)
+        config = json.loads(reg.get_booster().save_config())
+        if name.find('0.90') != -1:
+            assert config['learner']['learner_train_param'][
+                'objective'] == 'reg:linear'
+        else:
+            assert config['learner']['learner_train_param'][
+                'objective'] == 'reg:squarederror'
+        assert (len(reg.get_booster().get_dump()) ==
+                gm.kRounds * gm.kForests)
+        run_model_param_check(config)
+    elif name.find('cls') != -1:
+        cls = xgboost.XGBClassifier()
+        cls.load_model(path)
+        if name.find('0.90') == -1:
+            assert len(cls.classes_) == gm.kClasses
+            assert len(cls._le.classes_) == gm.kClasses
+            assert cls.n_classes_ == gm.kClasses
+        assert (len(cls.get_booster().get_dump()) ==
+                gm.kRounds * gm.kForests * gm.kClasses), path
+        config = json.loads(cls.get_booster().save_config())
+        assert config['learner']['learner_train_param'][
+            'objective'] == 'multi:softprob', path
+        run_model_param_check(config)
+    elif name.find('ltr') != -1:
+        ltr = xgboost.XGBRanker()
+        ltr.load_model(path)
+        assert (len(ltr.get_booster().get_dump()) ==
+                gm.kRounds * gm.kForests)
+        config = json.loads(ltr.get_booster().save_config())
+        assert config['learner']['learner_train_param'][
+            'objective'] == 'rank:ndcg'
+        run_model_param_check(config)
+    elif name.find('logit') != -1:
+        logit = xgboost.XGBClassifier()
+        logit.load_model(path)
+        assert (len(logit.get_booster().get_dump()) ==
+                gm.kRounds * gm.kForests)
+        config = json.loads(logit.get_booster().save_config())
+        assert config['learner']['learner_train_param'][
+            'objective'] == 'binary:logistic'
+    else:
+        assert False
+
+
+@pytest.mark.ci
+def test_model_compatibility():
+    '''Test model compatibility, can only be run on CI as others don't
+    have the credentials.
+
+    '''
+    path = os.path.dirname(os.path.abspath(__file__))
+    path = os.path.join(path, 'models')
+    try:
+        import boto3
+        import botocore
+    except ImportError:
+        pytest.skip(
+            'Skiping compatibility tests as boto3 is not installed.')
+
+    try:
+        s3_bucket = boto3.resource('s3').Bucket('xgboost-ci-jenkins-artifacts')
+        zip_path = 'xgboost_model_compatibility_test.zip'
+        s3_bucket.download_file(zip_path, zip_path)
+    except botocore.exceptions.NoCredentialsError:
+        pytest.skip(
+            'Skiping compatibility tests as running on non-CI environment.')
+
+    with zipfile.ZipFile(zip_path, 'r') as z:
+        z.extractall(path)
+
+    models = [
+        os.path.join(root, f) for root, subdir, files in os.walk(path)
+        for f in files
+        if f != 'version'
+    ]
+    assert models
+
+    for path in models:
+        name = os.path.basename(path)
+        if name.startswith('xgboost-'):
+            booster = xgboost.Booster(model_file=path)
+            run_booster_check(booster, name)
+        elif name.startswith('xgboost_scikit'):
+            run_scikit_model_check(name, path)
+        else:
+            assert False

tests/python/test_ranking.py

@@ -115,7 +115,6 @@ class TestRanking(unittest.TestCase):
         # model training parameters
         cls.params = {'objective': 'rank:pairwise',
                       'booster': 'gbtree',
-                      'silent': 0,
                       'eval_metric': ['ndcg']
                       }
 
@@ -153,7 +152,8 @@ class TestRanking(unittest.TestCase):
         Test cross-validation with a group specified
         """
         cv = xgboost.cv(self.params, self.dtrain, num_boost_round=2500,
-                    early_stopping_rounds=10, shuffle=False, nfold=10, as_pandas=False)
+                        early_stopping_rounds=10, shuffle=False, nfold=10,
+                        as_pandas=False)
         assert isinstance(cv, dict)
         assert len(cv) == 4
 

tests/python/test_with_dask.py

@@ -10,18 +10,20 @@ if sys.platform.startswith("win"):
 pytestmark = pytest.mark.skipif(**tm.no_dask())
 
 try:
-    from distributed.utils_test import client, loop, cluster_fixture
+    from distributed import LocalCluster, Client
     import dask.dataframe as dd
     import dask.array as da
     from xgboost.dask import DaskDMatrix
 except ImportError:
-    client = None
-    loop = None
-    cluster_fixture = None
-    pass
+    LocalCluster = None
+    Client = None
+    dd = None
+    da = None
+    DaskDMatrix = None
 
 kRows = 1000
 kCols = 10
+kWorkers = 5
 
 
 def generate_array():
@@ -31,7 +33,9 @@ def generate_array():
     return X, y
 
 
-def test_from_dask_dataframe(client):
+def test_from_dask_dataframe():
+    with LocalCluster(n_workers=5) as cluster:
+        with Client(cluster) as client:
             X, y = generate_array()
 
             X = dd.from_dask_array(X)
@@ -51,11 +55,13 @@ def test_from_dask_dataframe(client):
                 # evals_result is not supported in dask interface.
                 xgb.dask.train(
                     client, {}, dtrain, num_boost_round=2, evals_result={})
-
-    prediction = prediction.compute()  # force prediction to be computed
+            # force prediction to be computed
+            prediction = prediction.compute()
 
 
-def test_from_dask_array(client):
+def test_from_dask_array():
+    with LocalCluster(n_workers=5) as cluster:
+        with Client(cluster) as client:
             X, y = generate_array()
             dtrain = DaskDMatrix(client, X, y)
             # results is {'booster': Booster, 'history': {...}}
@@ -65,11 +71,13 @@ def test_from_dask_array(client):
             assert prediction.shape[0] == kRows
 
             assert isinstance(prediction, da.Array)
-
-    prediction = prediction.compute()  # force prediction to be computed
+            # force prediction to be computed
+            prediction = prediction.compute()
 
 
-def test_regressor(client):
+def test_dask_regressor():
+    with LocalCluster(n_workers=5) as cluster:
+        with Client(cluster) as client:
             X, y = generate_array()
             regressor = xgb.dask.DaskXGBRegressor(verbosity=1, n_estimators=2)
             regressor.set_params(tree_method='hist')
@@ -89,10 +97,13 @@ def test_regressor(client):
             assert len(history['validation_0']['rmse']) == 2
 
 
-def test_classifier(client):
+def test_dask_classifier():
+    with LocalCluster(n_workers=5) as cluster:
+        with Client(cluster) as client:
             X, y = generate_array()
             y = (y * 10).astype(np.int32)
-    classifier = xgb.dask.DaskXGBClassifier(verbosity=1, n_estimators=2)
+            classifier = xgb.dask.DaskXGBClassifier(
+                verbosity=1, n_estimators=2)
             classifier.client = client
             classifier.fit(X, y,  eval_set=[(X, y)])
             prediction = classifier.predict(X)
@@ -164,11 +175,15 @@ def run_empty_dmatrix(client, parameters):
 # No test for Exact, as empty DMatrix handling are mostly for distributed
 # environment and Exact doesn't support it.
 
-def test_empty_dmatrix_hist(client):
+def test_empty_dmatrix_hist():
+    with LocalCluster(n_workers=5) as cluster:
+        with Client(cluster) as client:
             parameters = {'tree_method': 'hist'}
             run_empty_dmatrix(client, parameters)
 
 
-def test_empty_dmatrix_approx(client):
+def test_empty_dmatrix_approx():
+    with LocalCluster(n_workers=5) as cluster:
+        with Client(cluster) as client:
             parameters = {'tree_method': 'approx'}
             run_empty_dmatrix(client, parameters)

tests/python/test_with_sklearn.py

@@ -490,6 +490,13 @@ def test_kwargs():
     assert clf.get_params()['n_estimators'] == 1000
 
 
+def test_kwargs_error():
+    params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}
+    with pytest.raises(TypeError):
+        clf = xgb.XGBClassifier(n_jobs=1000, **params)
+        assert isinstance(clf, xgb.XGBClassifier)
+
+
 def test_kwargs_grid_search():
     from sklearn.model_selection import GridSearchCV
     from sklearn import datasets
@@ -510,13 +517,6 @@ def test_kwargs_grid_search():
     assert len(means) == len(set(means))
 
 
-def test_kwargs_error():
-    params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}
-    with pytest.raises(TypeError):
-        clf = xgb.XGBClassifier(n_jobs=1000, **params)
-        assert isinstance(clf, xgb.XGBClassifier)
-
-
 def test_sklearn_clone():
     from sklearn.base import clone
 
@@ -525,6 +525,17 @@ def test_sklearn_clone():
     clone(clf)
 
 
+def test_sklearn_get_default_params():
+    from sklearn.datasets import load_digits
+    digits_2class = load_digits(2)
+    X = digits_2class['data']
+    y = digits_2class['target']
+    cls = xgb.XGBClassifier()
+    assert cls.get_params()['base_score'] is None
+    cls.fit(X[:4, ...], y[:4, ...])
+    assert cls.get_params()['base_score'] is not None
+
+
 def test_validation_weights_xgbmodel():
     from sklearn.datasets import make_hastie_10_2