Release 1.4.0

* Add scripts for packaging R package with GPU-enabled libxgboost.so

* [CI] Automatically build R package tarball

* Add comments

* Don't build tarball for pull requests

* Update the installation doc

Jenkinsfile

@@ -65,6 +65,7 @@ pipeline {
             'build-gpu-cuda10.1': { BuildCUDA(cuda_version: '10.1') },
             'build-gpu-cuda10.2': { BuildCUDA(cuda_version: '10.2', build_rmm: true) },
             'build-gpu-cuda11.0': { BuildCUDA(cuda_version: '11.0') },
+            'build-gpu-rpkg': { BuildRPackageWithCUDA(cuda_version: '10.0') },
             'build-jvm-packages-gpu-cuda10.0': { BuildJVMPackagesWithCUDA(spark_version: '3.0.0', cuda_version: '10.0') },
             'build-jvm-packages': { BuildJVMPackages(spark_version: '3.0.0') },
             'build-jvm-doc': { BuildJVMDoc() }
@@ -264,6 +265,24 @@ def BuildCUDA(args) {
   }
 }
 
+def BuildRPackageWithCUDA(args) {
+  node('linux && cpu_build') {
+    unstash name: 'srcs'
+    def container_type = 'gpu_build_r_centos6'
+    def docker_binary = "docker"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=10.0"
+    if (env.BRANCH_NAME == 'master' || env.BRANCH_NAME.startsWith('release')) {
+      sh """
+      ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/build_r_pkg_with_cuda.sh ${commit_id}
+      """
+      echo 'Uploading R tarball...'
+      path = ("${BRANCH_NAME}" == 'master') ? '' : "${BRANCH_NAME}/"
+      s3Upload bucket: 'xgboost-nightly-builds', path: path, acl: 'PublicRead', includePathPattern:'xgboost_r_gpu_linux_*.tar.gz'
+    }
+    deleteDir()
+  }
+}
+
 def BuildJVMPackagesWithCUDA(args) {
   node('linux && mgpu') {
     unstash name: 'srcs'

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@

doc/build.rst

@@ -2,18 +2,15 @@
 Installation Guide
 ##################
 
-.. note:: Pre-built binary wheel for Python
+.. note:: Pre-built binary wheel for Python: now with GPU support
 
-  If you are planning to use Python, consider installing XGBoost from a pre-built binary wheel, available from Python Package Index (PyPI). You may download and install it by running
+  If you are planning to use Python, consider installing XGBoost from a pre-built binary wheel, to avoid the trouble of building XGBoost from the source. You may download and install it by running
 
   .. code-block:: bash
 
-    # Ensure that you are downloading one of the following:
-    #   * xgboost-{version}-py2.py3-none-manylinux1_x86_64.whl
-    #   * xgboost-{version}-py2.py3-none-win_amd64.whl
     pip3 install xgboost
 
-  * The binary wheel will support GPU algorithms (`gpu_hist`) on machines with NVIDIA GPUs. Please note that **training with multiple GPUs is only supported for Linux platform**. See :doc:`gpu/index`.
+  * The binary wheel will support the GPU algorithm (``gpu_hist``) on machines with NVIDIA GPUs. Please note that **training with multiple GPUs is only supported for Linux platform**. See :doc:`gpu/index`.
   * Currently, we provide binary wheels for 64-bit Linux, macOS and Windows.
   * Nightly builds are available. You can go to `this page
     <https://s3-us-west-2.amazonaws.com/xgboost-nightly-builds/list.html>`_, find the
@@ -23,6 +20,21 @@ Installation Guide
 
       pip install <url to the wheel>
 
+.. note:: (EXPERIMENTAL) Pre-built binary package for R: now with GPU support
+
+  If you are planning to use R, consider installing ``{xgboost}`` from a pre-built binary package, to avoid the trouble of building XGBoost from the source. The binary package will let you use the GPU algorithm (``gpu_hist``) out of the box, as long as your machine has NVIDIA GPUs.
+
+  Download the binary package from the Releases page. The file name will be of the form ``xgboost_r_gpu_linux_[version].tar.gz``. Then install XGBoost by running:
+
+  .. code-block:: bash
+
+    # Install dependencies
+    R -q -e "install.packages(c('data.table', 'magrittr', 'jsonlite', 'remotes'))"
+    # Install XGBoost
+    R CMD INSTALL ./xgboost_r_gpu_linux.tar.gz
+
+  Currently, we provide the binary package for 64-bit Linux.
+
 
 ****************************
 Building XGBoost from source

include/xgboost/c_api.h

@@ -740,15 +740,17 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
  *
  * \param handle Booster handle
  * \param dmat   DMatrix handle
- * \param c_json_config String encoded predict configuration in JSON format.
+ * \param c_json_config String encoded predict configuration in JSON format, with
+ *                      following available fields in the JSON object:
  *
- *    "type": [0, 5]
+ *    "type": [0, 6]
  *      0: normal prediction
  *      1: output margin
  *      2: predict contribution
- *      3: predict approxmated contribution
+ *      3: predict approximated contribution
  *      4: predict feature interaction
- *      5: predict leaf
+ *      5: predict approximated feature interaction
+ *      6: predict leaf
  *    "training": bool
  *      Whether the prediction function is used as part of a training loop.  **Not used
  *      for inplace prediction**.
@@ -764,7 +766,7 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
  *    "iteration_begin": int
  *      Beginning iteration of prediction.
  *    "iteration_end": int
- *      End iteration of prediction.  Set to 0 this will become the size of tree model.
+ *      End iteration of prediction.  Set to 0 this will become the size of tree model (all the trees).
  *    "strict_shape": bool
  *      Whether should we reshape the output with stricter rules.  If set to true,
  *      normal/margin/contrib/interaction predict will output consistent shape

include/xgboost/learner.h

@@ -36,7 +36,8 @@ enum class PredictionType : std::uint8_t {  // NOLINT
   kContribution = 2,
   kApproxContribution = 3,
   kInteraction = 4,
-  kLeaf = 5
+  kApproxInteraction = 5,
+  kLeaf = 6
 };
 
 /*! \brief entry to to easily hold returning information */

jvm-packages/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>ml.dmlc</groupId>
     <artifactId>xgboost-jvm_2.12</artifactId>
-    <version>1.4.0-SNAPSHOT</version>
+    <version>1.4.0</version>
     <packaging>pom</packaging>
     <name>XGBoost JVM Package</name>
     <description>JVM Package for XGBoost</description>

jvm-packages/xgboost4j-example/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.4.0-SNAPSHOT</version>
+        <version>1.4.0</version>
     </parent>
     <artifactId>xgboost4j-example_2.12</artifactId>
-    <version>1.4.0-SNAPSHOT</version>
+    <version>1.4.0</version>
     <packaging>jar</packaging>
     <build>
         <plugins>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
-            <version>1.4.0-SNAPSHOT</version>
+            <version>1.4.0</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>
@@ -37,7 +37,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
-            <version>1.4.0-SNAPSHOT</version>
+            <version>1.4.0</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>1.4.0-SNAPSHOT</version>
+        <version>1.4.0</version>
     </parent>
     <artifactId>xgboost4j-flink_2.12</artifactId>
-    <version>1.4.0-SNAPSHOT</version>
+    <version>1.4.0</version>
     <build>
         <plugins>
             <plugin>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-            <version>1.4.0-SNAPSHOT</version>
+            <version>1.4.0</version>
         </dependency>
         <dependency>
             <groupId>org.apache.commons</groupId>

jvm-packages/xgboost4j-gpu/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.4.0-SNAPSHOT</version>
+        <version>1.4.0</version>
     </parent>
     <artifactId>xgboost4j-gpu_2.12</artifactId>
-    <version>1.4.0-SNAPSHOT</version>
+    <version>1.4.0</version>
     <packaging>jar</packaging>
 
     <dependencies>

jvm-packages/xgboost4j-spark-gpu/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.4.0-SNAPSHOT</version>
+        <version>1.4.0</version>
     </parent>
     <artifactId>xgboost4j-spark-gpu_2.12</artifactId>
     <build>
@@ -24,7 +24,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
-            <version>1.4.0-SNAPSHOT</version>
+            <version>1.4.0</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>

jvm-packages/xgboost4j-spark/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.4.0-SNAPSHOT</version>
+        <version>1.4.0</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>1.4.0-SNAPSHOT</version>
+            <version>1.4.0</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>

jvm-packages/xgboost4j/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.4.0-SNAPSHOT</version>
+        <version>1.4.0</version>
     </parent>
     <artifactId>xgboost4j_2.12</artifactId>
-    <version>1.4.0-SNAPSHOT</version>
+    <version>1.4.0</version>
     <packaging>jar</packaging>
 
     <dependencies>

python-package/xgboost/VERSION

@@ -1 +1 @@
-1.4.0-SNAPSHOT
+1.4.0

python-package/xgboost/core.py

@@ -1648,7 +1648,9 @@ class Booster(object):
             prediction. Note the final column is the bias term.
 
         approx_contribs :
-            Approximate the contributions of each feature
+            Approximate the contributions of each feature.  Used when ``pred_contribs`` or
+            ``pred_interactions`` is set to True.  Changing the default of this parameter
+            (False) is not recommended.
 
         pred_interactions :
             When this is True the output will be a matrix of size (nsample,
@@ -1720,9 +1722,9 @@ class Booster(object):
         if pred_contribs:
             assign_type(2 if not approx_contribs else 3)
         if pred_interactions:
-            assign_type(4)
+            assign_type(4 if not approx_contribs else 5)
         if pred_leaf:
-            assign_type(5)
+            assign_type(6)
         preds = ctypes.POINTER(ctypes.c_float)()
         shape = ctypes.POINTER(c_bst_ulong)()
         dims = c_bst_ulong()

python-package/xgboost/dask.py

@@ -21,6 +21,7 @@ from contextlib import contextmanager
 from collections import defaultdict
 from collections.abc import Sequence
 from threading import Thread
+from functools import partial, update_wrapper
 from typing import TYPE_CHECKING, List, Tuple, Callable, Optional, Any, Union, Dict, Set
 from typing import Awaitable, Generator, TypeVar
 
@@ -967,7 +968,7 @@ def _can_output_df(is_df: bool, output_shape: Tuple) -> bool:
     return is_df and len(output_shape) <= 2
 
 
-async def _direct_predict_impl(
+async def _direct_predict_impl(  # pylint: disable=too-many-branches
     mapped_predict: Callable,
     booster: "distributed.Future",
     data: _DaskCollection,
@@ -1022,6 +1023,14 @@ async def _direct_predict_impl(
                 new_axis = list(range(len(output_shape) - 2))
             else:
                 new_axis = [i + 2 for i in range(len(output_shape) - 2)]
+        if len(output_shape) == 2:
+            # Somehow dask fail to infer output shape change for 2-dim prediction, and
+            #  `chunks = (None, output_shape[1])` doesn't work due to None is not
+            #  supported in map_blocks.
+            chunks = list(data.chunks)
+            chunks[1] = (output_shape[1], )
+        else:
+            chunks = None
         predictions = da.map_blocks(
             mapped_predict,
             booster,
@@ -1029,6 +1038,8 @@ async def _direct_predict_impl(
             False,
             columns,
             base_margin_array,
+
+            chunks=chunks,
             drop_axis=drop_axis,
             new_axis=new_axis,
             dtype=numpy.float32,
@@ -1776,20 +1787,20 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
         self,
         X: _DaskCollection,
         validate_features: bool,
-        output_margin: bool,
         base_margin: Optional[_DaskCollection],
         iteration_range: Optional[Tuple[int, int]],
     ) -> _DaskCollection:
-        if iteration_range is None:
-            iteration_range = (0, 0)
         predts = await super()._predict_async(
             data=X,
-            output_margin=output_margin,
+            output_margin=self.objective == "multi:softmax",
             validate_features=validate_features,
             base_margin=base_margin,
             iteration_range=iteration_range,
         )
-        return _cls_predict_proba(self.objective, predts, da.vstack)
+        vstack = update_wrapper(
+            partial(da.vstack, allow_unknown_chunksizes=True), da.vstack
+        )
+        return _cls_predict_proba(getattr(self, "n_classes_", None), predts, vstack)
 
     # pylint: disable=missing-function-docstring
     def predict_proba(
@@ -1797,7 +1808,6 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
         X: _DaskCollection,
         ntree_limit: Optional[int] = None,
         validate_features: bool = True,
-        output_margin: bool = False,
         base_margin: Optional[_DaskCollection] = None,
         iteration_range: Optional[Tuple[int, int]] = None,
     ) -> Any:
@@ -1808,7 +1818,6 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
             self._predict_proba_async,
             X=X,
             validate_features=validate_features,
-            output_margin=output_margin,
             base_margin=base_margin,
             iteration_range=iteration_range,
         )

python-package/xgboost/sklearn.py

@@ -4,7 +4,7 @@
 import copy
 import warnings
 import json
-from typing import Union, Optional, List, Dict, Callable, Tuple, Any
+from typing import Union, Optional, List, Dict, Callable, Tuple, Any, TypeVar
 import numpy as np
 from .core import Booster, DMatrix, XGBoostError
 from .core import _deprecate_positional_args, _convert_ntree_limit
@@ -561,6 +561,8 @@ class XGBModel(XGBModelBase):
         self._Booster.load_model(fname)
         meta = self._Booster.attr('scikit_learn')
         if meta is None:
+            # FIXME(jiaming): This doesn't have to be a problem as most of the needed
+            # information like num_class and objective is in Learner class.
             warnings.warn(
                 'Loading a native XGBoost model with Scikit-Learn interface.')
             return
@@ -571,6 +573,8 @@ class XGBModel(XGBModelBase):
                 self._le = XGBoostLabelEncoder()
                 self._le.from_json(v)
                 continue
+            # FIXME(jiaming): This can be removed once label encoder is gone since we can
+            # generate it from `np.arange(self.n_classes_)`
             if k == 'classes_':
                 self.classes_ = np.array(v)
                 continue
@@ -1024,17 +1028,14 @@ class XGBModel(XGBModelBase):
         return np.array(json.loads(b.get_dump(dump_format='json')[0])['bias'])
 
 
-def _cls_predict_proba(
+PredtT = TypeVar("PredtT")
-    objective: Union[str, Callable], prediction: Any, vstack: Callable
+
-) -> Any:
+
-    if objective == 'multi:softmax':
+def _cls_predict_proba(n_classes: int, prediction: PredtT, vstack: Callable) -> PredtT:
-        raise ValueError('multi:softmax objective does not support predict_proba,'
+    assert len(prediction.shape) <= 2
-                         ' use `multi:softprob` or `binary:logistic` instead.')
+    if len(prediction.shape) == 2 and prediction.shape[1] == n_classes:
-    if objective == 'multi:softprob' or callable(objective):
-        # Return prediction directly if if objective is defined by user since we don't
-        # know how to perform the transformation
         return prediction
-    # Lastly the binary logistic function
+    # binary logistic function
     classone_probs = prediction
     classzero_probs = 1.0 - classone_probs
     return vstack((classzero_probs, classone_probs)).transpose()
@@ -1218,8 +1219,10 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
             return class_probs
 
         if len(class_probs.shape) > 1:
+            # turns softprob into softmax
             column_indexes = np.argmax(class_probs, axis=1)
         else:
+            # turns soft logit into class label
             column_indexes = np.repeat(0, class_probs.shape[0])
             column_indexes[class_probs > 0.5] = 1
 
@@ -1262,15 +1265,23 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
             a numpy array of shape array-like of shape (n_samples, n_classes) with the
             probability of each data example being of a given class.
         """
+        # custom obj:      Do nothing as we don't know what to do.
+        # softprob:        Do nothing, output is proba.
+        # softmax:         Use output margin to remove the argmax in PredTransform.
+        # binary:logistic: Expand the prob vector into 2-class matrix after predict.
+        # binary:logitraw: Unsupported by predict_proba()
         class_probs = super().predict(
             X=X,
-            output_margin=False,
+            output_margin=self.objective == "multi:softmax",
             ntree_limit=ntree_limit,
             validate_features=validate_features,
             base_margin=base_margin,
             iteration_range=iteration_range
         )
-        return _cls_predict_proba(self.objective, class_probs, np.vstack)
+        # If model is loaded from a raw booster there's no `n_classes_`
+        return _cls_predict_proba(
+            getattr(self, "n_classes_", None), class_probs, np.vstack
+        )
 
     def evals_result(self):
         """Return the evaluation results.

src/c_api/c_api.cc

@@ -651,13 +651,17 @@ XGB_DLL int XGBoosterPredictFromDMatrix(BoosterHandle handle,
   auto type = PredictionType(get<Integer const>(config["type"]));
   auto iteration_begin = get<Integer const>(config["iteration_begin"]);
   auto iteration_end = get<Integer const>(config["iteration_end"]);
-  learner->Predict(
+  bool approximate = type == PredictionType::kApproxContribution ||
-      *static_cast<std::shared_ptr<DMatrix> *>(dmat),
+                     type == PredictionType::kApproxInteraction;
-      type == PredictionType::kMargin, &entry.predictions, iteration_begin,
+  bool contribs = type == PredictionType::kContribution ||
-      iteration_end, get<Boolean const>(config["training"]),
+                  type == PredictionType::kApproxContribution;
-      type == PredictionType::kLeaf, type == PredictionType::kContribution,
+  bool interactions = type == PredictionType::kInteraction ||
-      type == PredictionType::kApproxContribution,
+                      type == PredictionType::kApproxInteraction;
-      type == PredictionType::kInteraction);
+  bool training = get<Boolean const>(config["training"]);
+  learner->Predict(p_m, type == PredictionType::kMargin, &entry.predictions,
+                   iteration_begin, iteration_end, training,
+                   type == PredictionType::kLeaf, contribs, approximate,
+                   interactions);
   *out_result = dmlc::BeginPtr(entry.predictions.ConstHostVector());
   auto &shape = learner->GetThreadLocal().prediction_shape;
   auto chunksize = p_m->Info().num_row_ == 0 ? 0 : entry.predictions.Size() / p_m->Info().num_row_;

src/c_api/c_api_utils.h

@@ -56,7 +56,6 @@ inline void CalcPredictShape(bool strict_shape, PredictionType type, size_t rows
   }
   case PredictionType::kApproxContribution:
   case PredictionType::kContribution: {
-    auto groups = chunksize / (cols + 1);
     if (groups == 1 && !strict_shape) {
       *out_dim = 2;
       shape.resize(*out_dim);
@@ -71,6 +70,7 @@ inline void CalcPredictShape(bool strict_shape, PredictionType type, size_t rows
     }
     break;
   }
+  case PredictionType::kApproxInteraction:
   case PredictionType::kInteraction: {
     if (groups == 1 && !strict_shape) {
       *out_dim = 3;

src/common/device_helpers.cuh

@@ -1290,6 +1290,21 @@ void InclusiveScan(InputIteratorT d_in, OutputIteratorT d_out, ScanOpT scan_op,
                                            num_items, nullptr, false)));
 }
 
+template <typename InIt, typename OutIt, typename Predicate>
+void CopyIf(InIt in_first, InIt in_second, OutIt out_first, Predicate pred) {
+  // We loop over batches because thrust::copy_if cant deal with sizes > 2^31
+  // See thrust issue #1302, #6822
+  size_t max_copy_size = std::numeric_limits<int>::max() / 2;
+  size_t length = std::distance(in_first, in_second);
+  XGBCachingDeviceAllocator<char> alloc;
+  for (size_t offset = 0; offset < length; offset += max_copy_size) {
+    auto begin_input = in_first + offset;
+    auto end_input = in_first + std::min(offset + max_copy_size, length);
+    out_first = thrust::copy_if(thrust::cuda::par(alloc), begin_input,
+                                end_input, out_first, pred);
+  }
+}
+
 template <typename InputIteratorT, typename OutputIteratorT, typename OffsetT>
 void InclusiveSum(InputIteratorT d_in, OutputIteratorT d_out, OffsetT num_items) {
   InclusiveScan(d_in, d_out, cub::Sum(), num_items);
@@ -1311,14 +1326,14 @@ void ArgSort(xgboost::common::Span<U> keys, xgboost::common::Span<IdxT> sorted_i
 
   if (accending) {
     void *d_temp_storage = nullptr;
-    cub::DispatchRadixSort<false, KeyT, ValueT, size_t>::Dispatch(
+    safe_cuda((cub::DispatchRadixSort<false, KeyT, ValueT, size_t>::Dispatch(
         d_temp_storage, bytes, d_keys, d_values, sorted_idx.size(), 0,
-        sizeof(KeyT) * 8, false, nullptr, false);
+        sizeof(KeyT) * 8, false, nullptr, false)));
     dh::TemporaryArray<char> storage(bytes);
     d_temp_storage = storage.data().get();
-    cub::DispatchRadixSort<false, KeyT, ValueT, size_t>::Dispatch(
+    safe_cuda((cub::DispatchRadixSort<false, KeyT, ValueT, size_t>::Dispatch(
         d_temp_storage, bytes, d_keys, d_values, sorted_idx.size(), 0,
-        sizeof(KeyT) * 8, false, nullptr, false);
+        sizeof(KeyT) * 8, false, nullptr, false)));
   } else {
     void *d_temp_storage = nullptr;
     safe_cuda((cub::DispatchRadixSort<true, KeyT, ValueT, size_t>::Dispatch(

src/common/hist_util.cu

@@ -93,6 +93,11 @@ size_t SketchBatchNumElements(size_t sketch_batch_num_elements,
                               bst_row_t num_rows, bst_feature_t columns,
                               size_t nnz, int device,
                               size_t num_cuts, bool has_weight) {
+#if defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
+  // device available memory is not accurate when rmm is used.
+  return nnz;
+#endif  // defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
+
   if (sketch_batch_num_elements == 0) {
     auto required_memory = RequiredMemory(num_rows, columns, nnz, num_cuts, has_weight);
     // use up to 80% of available space

src/common/hist_util.cuh

@@ -118,9 +118,8 @@ void MakeEntriesFromAdapter(AdapterBatch const& batch, BatchIter batch_iter,
   size_t num_valid = column_sizes_scan->back();
   // Copy current subset of valid elements into temporary storage and sort
   sorted_entries->resize(num_valid);
-  dh::XGBCachingDeviceAllocator<char> alloc;
+  dh::CopyIf(entry_iter + range.begin(), entry_iter + range.end(),
-  thrust::copy_if(thrust::cuda::par(alloc), entry_iter + range.begin(),
+             sorted_entries->begin(), is_valid);
-                  entry_iter + range.end(), sorted_entries->begin(), is_valid);
 }
 
 void SortByWeight(dh::device_vector<float>* weights,

src/data/simple_dmatrix.cu

@@ -55,18 +55,9 @@ void CopyDataToDMatrix(AdapterT* adapter, common::Span<Entry> data,
   COOToEntryOp<decltype(batch)> transform_op{batch};
   thrust::transform_iterator<decltype(transform_op), decltype(counting)>
       transform_iter(counting, transform_op);
-  // We loop over batches because thrust::copy_if cant deal with sizes > 2^31
-  // See thrust issue #1302
-  size_t max_copy_size = std::numeric_limits<int>::max() / 2;
   auto begin_output = thrust::device_pointer_cast(data.data());
-  for (size_t offset = 0; offset < batch.Size(); offset += max_copy_size) {
+  dh::CopyIf(transform_iter, transform_iter + batch.Size(), begin_output,
-    auto begin_input = transform_iter + offset;
+             IsValidFunctor(missing));
-    auto end_input =
-        transform_iter + std::min(offset + max_copy_size, batch.Size());
-    begin_output =
-        thrust::copy_if(thrust::cuda::par(alloc), begin_input, end_input,
-                        begin_output, IsValidFunctor(missing));
-  }
 }
 
 // Does not currently support metainfo as no on-device data source contains this

src/gbm/gbtree.cc

@@ -575,6 +575,20 @@ void GPUDartPredictInc(common::Span<float> out_predts,
 }
 #endif
 
+void GPUDartInplacePredictInc(common::Span<float> out_predts,
+                              common::Span<float> predts, float tree_w,
+                              size_t n_rows, float base_score,
+                              bst_group_t n_groups,
+                              bst_group_t group)
+#if defined(XGBOOST_USE_CUDA)
+;  // NOLINT
+#else
+{
+  common::AssertGPUSupport();
+}
+#endif
+
+
 class Dart : public GBTree {
  public:
   explicit Dart(LearnerModelParam const* booster_config) :
@@ -728,13 +742,14 @@ class Dart : public GBTree {
       gpu_predictor_.get()
 #endif  // defined(XGBOOST_USE_CUDA)
     };
+    Predictor const * predictor {nullptr};
 
     MetaInfo info;
     StringView msg{"Unsupported data type for inplace predict."};
     int32_t device = GenericParameter::kCpuId;
+    PredictionCacheEntry predts;
     // Inplace predict is not used for training, so no need to drop tree.
     for (size_t i = tree_begin; i < tree_end; ++i) {
-      PredictionCacheEntry predts;
       if (tparam_.predictor == PredictorType::kAuto) {
         // Try both predictor implementations
         bool success = false;
@@ -742,6 +757,7 @@ class Dart : public GBTree {
           if (p && p->InplacePredict(x, nullptr, model_, missing, &predts, i,
                                      i + 1)) {
             success = true;
+            predictor = p;
 #if defined(XGBOOST_USE_CUDA)
             device = predts.predictions.DeviceIdx();
 #endif  // defined(XGBOOST_USE_CUDA)
@@ -750,46 +766,53 @@ class Dart : public GBTree {
         }
         CHECK(success) << msg;
       } else {
-        // No base margin for each tree
+        // No base margin from meta info for each tree
-        bool success = this->GetPredictor()->InplacePredict(
+        predictor = this->GetPredictor().get();
-            x, nullptr, model_, missing, &predts, i, i + 1);
+        bool success = predictor->InplacePredict(x, nullptr, model_, missing,
+                                                 &predts, i, i + 1);
         device = predts.predictions.DeviceIdx();
         CHECK(success) << msg;
       }
 
       auto w = this->weight_drop_.at(i);
-      auto &h_predts = predts.predictions.HostVector();
+      size_t n_groups = model_.learner_model_param->num_output_group;
-      auto &h_out_predts = out_preds->predictions.HostVector();
+      auto n_rows = predts.predictions.Size() / n_groups;
 
       if (i == tree_begin) {
-        auto n_rows =
+        // base margin is added here.
-            h_predts.size() / model_.learner_model_param->num_output_group;
         if (p_m) {
           p_m->Info().num_row_ = n_rows;
-          cpu_predictor_->InitOutPredictions(p_m->Info(),
+          predictor->InitOutPredictions(p_m->Info(), &out_preds->predictions,
-                                             &out_preds->predictions, model_);
+                                        model_);
         } else {
           info.num_row_ = n_rows;
-          cpu_predictor_->InitOutPredictions(info, &out_preds->predictions,
+          predictor->InitOutPredictions(info, &out_preds->predictions, model_);
-                                             model_);
         }
       }
 
       // Multiple the tree weight
-      CHECK_EQ(h_predts.size(), h_out_predts.size());
+      CHECK_EQ(predts.predictions.Size(), out_preds->predictions.Size());
+      auto group = model_.tree_info.at(i);
 
+      if (device == GenericParameter::kCpuId) {
+        auto &h_predts = predts.predictions.HostVector();
+        auto &h_out_predts = out_preds->predictions.HostVector();
 #pragma omp parallel for
-      for (omp_ulong i = 0; i < h_out_predts.size(); ++i) {
+        for (omp_ulong ridx = 0; ridx < n_rows; ++ridx) {
-        // Need to remove the base margin from indiviual tree.
+          const size_t offset = ridx * n_groups + group;
-        h_out_predts[i] +=
+          // Need to remove the base margin from indiviual tree.
-            (h_predts[i] - model_.learner_model_param->base_score) * w;
+          h_out_predts[offset] +=
+              (h_predts[offset] - model_.learner_model_param->base_score) * w;
+        }
+      } else {
+        out_preds->predictions.SetDevice(device);
+        predts.predictions.SetDevice(device);
+        GPUDartInplacePredictInc(out_preds->predictions.DeviceSpan(),
+                                 predts.predictions.DeviceSpan(), w, n_rows,
+                                 model_.learner_model_param->base_score,
+                                 n_groups, group);
       }
     }
-
-    if (device != GenericParameter::kCpuId) {
-      out_preds->predictions.SetDevice(device);
-      out_preds->predictions.DeviceSpan();
-    }
   }
 
   void PredictInstance(const SparsePage::Inst &inst,

src/gbm/gbtree.cu

@@ -14,5 +14,15 @@ void GPUDartPredictInc(common::Span<float> out_predts,
     out_predts[offset] += (predts[offset] * tree_w);
   });
 }
+
+void GPUDartInplacePredictInc(common::Span<float> out_predts,
+                              common::Span<float> predts, float tree_w,
+                              size_t n_rows, float base_score,
+                              bst_group_t n_groups, bst_group_t group) {
+  dh::LaunchN(dh::CurrentDevice(), n_rows, [=] XGBOOST_DEVICE(size_t ridx) {
+    const size_t offset = ridx * n_groups + group;
+    out_predts[offset] += (predts[offset] - base_score) * tree_w;
+  });
+}
 }  // namespace gbm
 }  // namespace xgboost

src/metric/auc.cu

@@ -219,8 +219,6 @@ float GPUMultiClassAUCOVR(common::Span<float const> predts, MetaInfo const &info
   /**
    * Create sorted index for each class
    */
-  auto d_sorted_idx = dh::ToSpan(cache->sorted_idx);
-  dh::Iota(d_sorted_idx, device);
   auto d_predts_t = dh::ToSpan(cache->predts_t);
   Transpose(predts, d_predts_t, n_samples, n_classes, device);
 
@@ -231,6 +229,7 @@ float GPUMultiClassAUCOVR(common::Span<float const> predts, MetaInfo const &info
   });
   // no out-of-place sort for thrust, cub sort doesn't accept general iterator. So can't
   // use transform iterator in sorting.
+  auto d_sorted_idx = dh::ToSpan(cache->sorted_idx);
   dh::SegmentedArgSort<false>(d_predts_t, d_class_ptr, d_sorted_idx);
 
   /**
@@ -447,10 +446,9 @@ GPURankingAUC(common::Span<float const> predts, MetaInfo const &info,
   /**
    * Sort the labels
    */
-  auto d_sorted_idx = dh::ToSpan(cache->sorted_idx);
   auto d_labels = info.labels_.ConstDeviceSpan();
 
-  dh::Iota(d_sorted_idx, device);
+  auto d_sorted_idx = dh::ToSpan(cache->sorted_idx);
   dh::SegmentedArgSort<false>(d_labels, d_group_ptr, d_sorted_idx);
 
   auto d_weights = info.weights_.ConstDeviceSpan();

tests/ci_build/Dockerfile.gpu_build_r_centos6

@@ -0,0 +1,112 @@
+ARG CUDA_VERSION_ARG
+FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-centos6
+ARG CUDA_VERSION_ARG
+
+# Environment
+ENV DEBIAN_FRONTEND noninteractive
+ENV DEVTOOLSET_URL_ROOT http://vault.centos.org/6.9/sclo/x86_64/rh/devtoolset-4/
+
+COPY CentOS-Base.repo /etc/yum.repos.d/
+
+# Install all basic requirements
+RUN \
+    yum install -y epel-release && \
+    yum -y update && \
+    yum install -y tar unzip wget xz git patchelf readline-devel libX11-devel libXt-devel \
+                   xorg-x11-server-devel openssl-devel texlive-* && \
+    yum install -y $DEVTOOLSET_URL_ROOT/devtoolset-4-gcc-5.3.1-6.1.el6.x86_64.rpm \
+                   $DEVTOOLSET_URL_ROOT/devtoolset-4-gcc-gfortran-5.3.1-6.1.el6.x86_64.rpm \
+                   $DEVTOOLSET_URL_ROOT/devtoolset-4-libquadmath-devel-5.3.1-6.1.el6.x86_64.rpm \
+                   $DEVTOOLSET_URL_ROOT/devtoolset-4-gcc-c++-5.3.1-6.1.el6.x86_64.rpm \
+                   $DEVTOOLSET_URL_ROOT/devtoolset-4-binutils-2.25.1-8.el6.x86_64.rpm \
+                   $DEVTOOLSET_URL_ROOT/devtoolset-4-runtime-4.1-3.sc1.el6.x86_64.rpm \
+                   $DEVTOOLSET_URL_ROOT/devtoolset-4-libstdc++-devel-5.3.1-6.1.el6.x86_64.rpm
+
+ENV PATH=/opt/python/bin:/usr/local/ninja:/opt/software/packages/bin:/opt/R/3.3.0/bin:$PATH
+ENV LD_LIBRARY_PATH=/opt/software/packages/lib:/opt/R/3.3.0/lib64:$LD_LIBRARY_PATH
+ENV CC=/opt/rh/devtoolset-4/root/usr/bin/gcc
+ENV CXX=/opt/rh/devtoolset-4/root/usr/bin/c++
+ENV CPP=/opt/rh/devtoolset-4/root/usr/bin/cpp
+ENV F77=/opt/rh/devtoolset-4/root/usr/bin/gfortran
+
+# A few packages have to be built from the source because CentOS 6 is a very old distribution and
+# the system packages are not sufficiently up-to-date to build R 3.3.0. We'll want to update to
+# CentOS 7 after the 1.4.0 release. Tracking issue: dmlc/xgboost#6791.
+#
+# Why choose an old Linux distro? This is so that the resulting xgboost.so is compatible with a
+# wide range of Linux OSes currently in operation. See https://www.python.org/dev/peps/pep-0571/
+RUN \
+    wget https://zlib.net/fossils/zlib-1.2.5.tar.gz  && \
+    wget https://sourceware.org/pub/bzip2/bzip2-1.0.6.tar.gz  && \
+    wget http://tukaani.org/xz/xz-5.2.2.tar.gz  && \
+    wget https://ftp.pcre.org/pub/pcre/pcre-8.40.tar.gz  && \
+    wget https://www.openssl.org/source/old/1.0.0/openssl-1.0.0k.tar.gz  && \
+    wget --no-check-certificate https://curl.se/download/curl-7.47.1.tar.gz  && \
+    tar xf zlib-1.2.5.tar.gz  && \
+    tar xf bzip2-1.0.6.tar.gz  && \
+    tar xf xz-5.2.2.tar.gz  && \
+    tar xf pcre-8.40.tar.gz  && \
+    tar xf openssl-1.0.0k.tar.gz  && \
+    tar xf curl-7.47.1.tar.gz  && \
+    cd zlib-1.2.5  && \
+    ./configure --prefix=/opt/software/packages  && \
+    make -j$(nproc)  && \
+    make install  && \
+    cd ../bzip2-1.0.6  && \
+    sed -i 's/CFLAGS=-Wall/CFLAGS=-fPIC -Wall/g' Makefile  && \
+    make -f Makefile-libbz2_so  && \
+    make clean  && \
+    make -j$(nproc)  && \
+    make -n install PREFIX=/opt/software/packages  && \
+    make install PREFIX=/opt/software/packages  && \
+    cd ../xz-5.2.2  && \
+    ./configure --prefix=/opt/software/packages  && \
+    make -j$(nproc)  && \
+    make install  && \
+    cd ../pcre-8.40  && \
+    ./configure --enable-utf8 --prefix=/opt/software/packages  && \
+    make -j$(nproc)  && \
+    make install  && \
+    cd ../curl-7.47.1  && \
+    ./configure --prefix=/opt/software/packages --with-ssl && \
+    make -j$(nproc)  && \
+    make install  && \
+    export CFLAGS="-I/opt/software/packages/include"   && \
+    export LDFLAGS="-L/opt/software/packages/lib"  && \
+    cd .. && \
+    # R 3.3.0
+    wget -nv -nc https://cran.r-project.org/src/base/R-3/R-3.3.0.tar.gz  && \
+    tar xf R-3.3.0.tar.gz  && \
+    cd R-3.3.0  && \
+    ./configure --prefix=/opt/R/3.3.0 --enable-R-shlib  && \
+    make -j$(nproc)  && \
+    make install  && \
+    # Python
+    wget -nv -nc -O Miniconda3.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
+    bash Miniconda3.sh -b -p /opt/python && \
+    /opt/python/bin/python -m pip install auditwheel && \
+    # CMake
+    wget -nv -nc https://cmake.org/files/v3.13/cmake-3.13.0-Linux-x86_64.sh --no-check-certificate && \
+    bash cmake-3.13.0-Linux-x86_64.sh --skip-license --prefix=/usr && \
+    # Ninja
+    mkdir -p /usr/local && \
+    cd /usr/local/ && \
+    wget -nv -nc https://github.com/ninja-build/ninja/archive/v1.10.0.tar.gz --no-check-certificate && \
+    tar xf v1.10.0.tar.gz && mv ninja-1.10.0 ninja && rm -v v1.10.0.tar.gz && \
+    cd ninja && \
+    /opt/python/bin/python ./configure.py --bootstrap
+
+ENV GOSU_VERSION 1.10
+
+# Install lightweight sudo (not bound to TTY)
+RUN set -ex; \
+    wget -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$GOSU_VERSION/gosu-amd64" && \
+    chmod +x /usr/local/bin/gosu && \
+    gosu nobody true
+
+# Default entry-point to use if running locally
+# It will preserve attributes of created files
+COPY entrypoint.sh /scripts/
+
+WORKDIR /workspace
+ENTRYPOINT ["/scripts/entrypoint.sh"]

tests/ci_build/build_r_pkg_with_cuda.sh

@@ -0,0 +1,33 @@
+#!/bin/bash
+set -e
+set -x
+
+if [ "$#" -ne 1 ]
+then
+  echo "Build the R package tarball with CUDA code. Usage: $0 [commit hash]"
+  exit 1
+fi
+
+commit_hash="$1"
+
+make Rpack
+mv xgboost/ xgboost_rpack/
+
+mkdir build
+cd build
+cmake .. -GNinja -DUSE_CUDA=ON -DR_LIB=ON
+ninja
+cd ..
+
+rm xgboost
+# This super wacky hack is found in cmake/RPackageInstall.cmake.in and
+# cmake/RPackageInstallTargetSetup.cmake. This hack lets us bypass the normal build process of R
+# and have R use xgboost.so that we've already built.
+rm -v xgboost_rpack/configure
+rm -rfv xgboost_rpack/src
+mkdir -p xgboost_rpack/src
+cp -v lib/xgboost.so xgboost_rpack/src/
+echo 'all:' > xgboost_rpack/src/Makefile
+echo 'all:' > xgboost_rpack/src/Makefile.win
+mv xgboost_rpack/ xgboost/
+tar cvzf xgboost_r_gpu_linux_${commit_hash}.tar.gz xgboost/

tests/cpp/common/test_hist_util.cu

@@ -45,6 +45,10 @@ TEST(HistUtil, DeviceSketch) {
 }
 
 TEST(HistUtil, SketchBatchNumElements) {
+#if defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
+  LOG(WARNING) << "Test not runnable with RMM enabled.";
+  return;
+#endif  // defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
   size_t constexpr kCols = 10000;
   int device;
   dh::safe_cuda(cudaGetDevice(&device));

tests/python-gpu/test_gpu_prediction.py

@@ -332,27 +332,44 @@ class TestGPUPredict:
         rmse = mean_squared_error(y_true=y, y_pred=pred, squared=False)
         np.testing.assert_almost_equal(rmse, eval_history['train']['rmse'][-1], decimal=5)
 
-    def test_predict_dart(self):
+    @pytest.mark.parametrize("n_classes", [2, 3])
+    def test_predict_dart(self, n_classes):
+        from sklearn.datasets import make_classification
         import cupy as cp
-        rng = cp.random.RandomState(1994)
         n_samples = 1000
-        X = rng.randn(n_samples, 10)
+        X_, y_ = make_classification(
-        y = rng.randn(n_samples)
+            n_samples=n_samples, n_informative=5, n_classes=n_classes
+        )
+        X, y = cp.array(X_), cp.array(y_)
+
         Xy = xgb.DMatrix(X, y)
-        booster = xgb.train(
+        if n_classes == 2:
-            {
+            params = {
                 "tree_method": "gpu_hist",
                 "booster": "dart",
                 "rate_drop": 0.5,
-            },
+                "objective": "binary:logistic"
-            Xy,
+            }
-            num_boost_round=32
+        else:
-        )
+            params = {
+                "tree_method": "gpu_hist",
+                "booster": "dart",
+                "rate_drop": 0.5,
+                "objective": "multi:softprob",
+                "num_class": n_classes
+            }
+
+        booster = xgb.train(params, Xy, num_boost_round=32)
         # predictor=auto
         inplace = booster.inplace_predict(X)
         copied = booster.predict(Xy)
+        cpu_inplace = booster.inplace_predict(X_)
+        booster.set_param({"predictor": "cpu_predictor"})
+        cpu_copied = booster.predict(Xy)
 
         copied = cp.array(copied)
+        cp.testing.assert_allclose(cpu_inplace, copied, atol=1e-6)
+        cp.testing.assert_allclose(cpu_copied, copied, atol=1e-6)
         cp.testing.assert_allclose(inplace, copied, atol=1e-6)
 
         booster.set_param({"predictor": "gpu_predictor"})

tests/python-gpu/test_gpu_with_dask.py

@@ -173,13 +173,13 @@ def run_gpu_hist(
     assert tm.non_increasing(history["train"][dataset.metric])
 
 
+@pytest.mark.skipif(**tm.no_cudf())
 def test_boost_from_prediction(local_cuda_cluster: LocalCUDACluster) -> None:
-    import cudf
     from sklearn.datasets import load_breast_cancer
     with Client(local_cuda_cluster) as client:
         X_, y_ = load_breast_cancer(return_X_y=True)
-        X = dd.from_array(X_, chunksize=100).map_partitions(cudf.from_pandas)
+        X = dd.from_array(X_, chunksize=100)
-        y = dd.from_array(y_, chunksize=100).map_partitions(cudf.from_pandas)
+        y = dd.from_array(y_, chunksize=100)
         run_boost_from_prediction(X, y, "gpu_hist", client)
 
 
@@ -202,6 +202,7 @@ class TestDistributedGPU:
     @settings(deadline=duration(seconds=120), suppress_health_check=suppress)
     @pytest.mark.skipif(**tm.no_dask())
     @pytest.mark.skipif(**tm.no_dask_cuda())
+    @pytest.mark.skipif(**tm.no_cupy())
     @pytest.mark.parametrize(
         "local_cuda_cluster", [{"n_workers": 2}], indirect=["local_cuda_cluster"]
     )
@@ -276,7 +277,7 @@ class TestDistributedGPU:
             X = dask_cudf.from_dask_dataframe(dd.from_dask_array(X_))
             y = dask_cudf.from_dask_dataframe(dd.from_dask_array(y_))
             w = dask_cudf.from_dask_dataframe(dd.from_dask_array(w_))
-            run_dask_classifier(X, y, w, model, client)
+            run_dask_classifier(X, y, w, model, client, 10)
 
     @pytest.mark.skipif(**tm.no_dask())
     @pytest.mark.skipif(**tm.no_dask_cuda())
@@ -454,6 +455,7 @@ async def run_from_dask_array_asyncio(scheduler_address: str) -> dxgb.TrainRetur
 
 @pytest.mark.skipif(**tm.no_dask())
 @pytest.mark.skipif(**tm.no_dask_cuda())
+@pytest.mark.skipif(**tm.no_cupy())
 @pytest.mark.mgpu
 def test_with_asyncio(local_cuda_cluster: LocalCUDACluster) -> None:
     with Client(local_cuda_cluster) as client:

tests/python/test_predict.py

@@ -98,6 +98,27 @@ def test_predict_shape():
     assert len(contrib.shape) == 3
     assert contrib.shape[1] == 1
 
+    contrib = reg.get_booster().predict(
+        xgb.DMatrix(X), pred_contribs=True, approx_contribs=True
+    )
+    assert len(contrib.shape) == 2
+    assert contrib.shape[1] == X.shape[1] + 1
+
+    interaction = reg.get_booster().predict(
+        xgb.DMatrix(X), pred_interactions=True, approx_contribs=True
+    )
+    assert len(interaction.shape) == 3
+    assert interaction.shape[1] == X.shape[1] + 1
+    assert interaction.shape[2] == X.shape[1] + 1
+
+    interaction = reg.get_booster().predict(
+        xgb.DMatrix(X), pred_interactions=True, approx_contribs=True, strict_shape=True
+    )
+    assert len(interaction.shape) == 4
+    assert interaction.shape[1] == 1
+    assert interaction.shape[2] == X.shape[1] + 1
+    assert interaction.shape[3] == X.shape[1] + 1
+
 
 class TestInplacePredict:
     '''Tests for running inplace prediction'''

tests/python/test_with_dask.py

@@ -318,14 +318,17 @@ def run_dask_classifier(
     w: xgb.dask._DaskCollection,
     model: str,
     client: "Client",
+    n_classes,
 ) -> None:
+    metric = "merror" if n_classes > 2 else "logloss"
+
     if model == "boosting":
         classifier = xgb.dask.DaskXGBClassifier(
-            verbosity=1, n_estimators=2, eval_metric="merror"
+            verbosity=1, n_estimators=2, eval_metric=metric
         )
     else:
         classifier = xgb.dask.DaskXGBRFClassifier(
-            verbosity=1, n_estimators=2, eval_metric="merror"
+            verbosity=1, n_estimators=2, eval_metric=metric
         )
 
     assert classifier._estimator_type == "classifier"
@@ -343,7 +346,7 @@ def run_dask_classifier(
     assert isinstance(history, dict)
 
     assert list(history.keys())[0] == "validation_0"
-    assert list(history["validation_0"].keys())[0] == "merror"
+    assert list(history["validation_0"].keys())[0] == metric
     assert len(list(history["validation_0"])) == 1
     forest = int(
         json.loads(classifier.get_booster().save_config())["learner"][
@@ -351,34 +354,35 @@ def run_dask_classifier(
         ]["gbtree_train_param"]["num_parallel_tree"]
     )
     if model == "boosting":
-        assert len(history["validation_0"]["merror"]) == 2
+        assert len(history["validation_0"][metric]) == 2
         assert forest == 1
     else:
-        assert len(history["validation_0"]["merror"]) == 1
+        assert len(history["validation_0"][metric]) == 1
         assert forest == 2
 
     # Test .predict_proba()
     probas = classifier.predict_proba(X).compute()
-    assert classifier.n_classes_ == 10
+    assert classifier.n_classes_ == n_classes
     assert probas.ndim == 2
     assert probas.shape[0] == kRows
-    assert probas.shape[1] == 10
+    assert probas.shape[1] == n_classes
 
-    cls_booster = classifier.get_booster()
+    if n_classes > 2:
-    single_node_proba = cls_booster.inplace_predict(X.compute())
+        cls_booster = classifier.get_booster()
+        single_node_proba = cls_booster.inplace_predict(X.compute())
 
-    # test shared by CPU and GPU
+        # test shared by CPU and GPU
-    if isinstance(single_node_proba, np.ndarray):
+        if isinstance(single_node_proba, np.ndarray):
-        np.testing.assert_allclose(single_node_proba, probas)
+            np.testing.assert_allclose(single_node_proba, probas)
-    else:
+        else:
-        import cupy
+            import cupy
-        cupy.testing.assert_allclose(single_node_proba, probas)
+            cupy.testing.assert_allclose(single_node_proba, probas)
 
     # Test with dataframe, not shared with GPU as cupy doesn't work well with da.unique.
-    if isinstance(X, da.Array):
+    if isinstance(X, da.Array) and n_classes > 2:
         X_d: dd.DataFrame = X.to_dask_dataframe()
 
-        assert classifier.n_classes_ == 10
+        assert classifier.n_classes_ == n_classes
         prediction_df = classifier.predict(X_d).compute()
 
         assert prediction_df.ndim == 1
@@ -393,7 +397,12 @@ def run_dask_classifier(
 def test_dask_classifier(model: str, client: "Client") -> None:
     X, y, w = generate_array(with_weights=True)
     y = (y * 10).astype(np.int32)
-    run_dask_classifier(X, y, w, model, client)
+    run_dask_classifier(X, y, w, model, client, 10)
+
+    y_bin = y.copy()
+    y_bin[y > 5] = 1.0
+    y_bin[y <= 5] = 0.0
+    run_dask_classifier(X, y_bin, w, model, client, 2)
 
 
 @pytest.mark.skipif(**tm.no_sklearn())