diff --git a/src/gbm/gbtree.cc b/src/gbm/gbtree.cc
index 7a911a4b8..43048061e 100644
--- a/src/gbm/gbtree.cc
+++ b/src/gbm/gbtree.cc
@@ -57,6 +57,14 @@ void GBTree::Configure(const Args& cfg) {
 
   monitor_.Init("GBTree");
 
+  if (tparam_.tree_method == TreeMethod::kGPUHist &&
+      std::none_of(cfg.cbegin(), cfg.cend(),
+                   [](std::pair<std::string, std::string> const& arg) {
+                     return arg.first == "predictor";
+                   })) {
+    tparam_.predictor = "gpu_predictor";
+  }
+
   configured_ = true;
 }
 
@@ -88,7 +96,7 @@ void GBTree::PerformTreeMethodHeuristic(std::map<std::string, std::string> const
     // set, since only experts are expected to do so.
     return;
   }
-
+  // tparam_ is set before calling this function.
   if (tparam_.tree_method != TreeMethod::kAuto) {
     return;
   }
@@ -112,7 +120,6 @@ void GBTree::PerformTreeMethodHeuristic(std::map<std::string, std::string> const
     tparam_.tree_method = TreeMethod::kApprox;
   } else {
     tparam_.tree_method = TreeMethod::kExact;
-    tparam_.updater_seq = "grow_colmaker,prune";
   }
   LOG(DEBUG) << "Using tree method: " << static_cast<int>(tparam_.tree_method);
 }
@@ -131,8 +138,9 @@ void GBTree::ConfigureUpdaters(const std::map<std::string, std::string>& cfg) {
   /* Choose updaters according to tree_method parameters */
   switch (tparam_.tree_method) {
     case TreeMethod::kAuto:
-      // Use heuristic to choose between 'exact' and 'approx'
-      // This choice is deferred to PerformTreeMethodHeuristic().
+      // Use heuristic to choose between 'exact' and 'approx' This
+      // choice is carried out in PerformTreeMethodHeuristic() before
+      // calling this function.
       break;
     case TreeMethod::kApprox:
       tparam_.updater_seq = "grow_histmaker,prune";
diff --git a/src/learner.cc b/src/learner.cc
index 8e694882d..0eb175f3d 100644
--- a/src/learner.cc
+++ b/src/learner.cc
@@ -530,7 +530,7 @@ class LearnerImpl : public Learner {
   void PredictRaw(DMatrix* data, HostDeviceVector<bst_float>* out_preds,
                   unsigned ntree_limit = 0) const {
     CHECK(gbm_ != nullptr)
-        << "Predict must happen after Load or InitModel";
+        << "Predict must happen after Load or configuration";
     this->ValidateDMatrix(data);
     gbm_->PredictBatch(data, out_preds, ntree_limit);
   }
@@ -619,11 +619,11 @@ class LearnerImpl : public Learner {
 
   void ValidateDMatrix(DMatrix* p_fmat) const {
     MetaInfo const& info = p_fmat->Info();
-    auto const& weights = info.weights_.HostVector();
-    if (info.group_ptr_.size() != 0 && weights.size() != 0) {
-      CHECK(weights.size() == info.group_ptr_.size() - 1)
+    auto const& weights = info.weights_;
+    if (info.group_ptr_.size() != 0 && weights.Size() != 0) {
+      CHECK(weights.Size() == info.group_ptr_.size() - 1)
           << "\n"
-          << "weights size: " << weights.size()            << ", "
+          << "weights size: " << weights.Size()            << ", "
           << "groups size: "  << info.group_ptr_.size() -1 << ", "
           << "num rows: "     << p_fmat->Info().num_row_   << "\n"
           << "Number of weights should be equal to number of groups in ranking task.";