Serialise booster after training to reset state (#5484)

* Serialise booster after training to reset state

* Prevent process_type being set on load

* Check for correct updater sequence

tests/cpp/gbm/test_gbtree.cc

@@ -63,6 +63,10 @@ TEST(GBTree, WrongUpdater) {
   // Hist can not be used for updating tree.
   learner->SetParams(Args{{"tree_method", "hist"}, {"process_type", "update"}});
   ASSERT_THROW(learner->UpdateOneIter(0, p_dmat), dmlc::Error);
+  // Prune can not be used for learning new tree.
+  learner->SetParams(
+      Args{{"tree_method", "prune"}, {"process_type", "default"}});
+  ASSERT_THROW(learner->UpdateOneIter(0, p_dmat), dmlc::Error);
 }
 
 #ifdef XGBOOST_USE_CUDA

tests/distributed/test_basic.py

@@ -20,9 +20,8 @@ num_round = 20
 bst = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=2)
 
 # Save the model, only ask process 0 to save the model.
-if xgb.rabit.get_rank() == 0:
-    bst.save_model("test.model")
-    xgb.rabit.tracker_print("Finished training\n")
+bst.save_model("test.model{}".format(xgb.rabit.get_rank()))
+xgb.rabit.tracker_print("Finished training\n")
 
 # Notify the tracker all training has been successful
 # This is only needed in distributed training.

tests/distributed/test_issue3402.py

@@ -70,9 +70,8 @@ watchlist  = [(dtrain,'train')]
 num_round = 2
 bst = xgb.train(param, dtrain, num_round, watchlist)
 
-if xgb.rabit.get_rank() == 0:
-  bst.save_model("test_issue3402.model")
-  xgb.rabit.tracker_print("Finished training\n")
+bst.save_model("test_issue3402.model{}".format(xgb.rabit.get_rank()))
+xgb.rabit.tracker_print("Finished training\n")
 
 # Notify the tracker all training has been successful
 # This is only needed in distributed training.