Add use_rmm flag to global configuration (#6656)

* Ensure RMM is 0.18 or later

* Add use_rmm flag to global configuration

* Modify XGBCachingDeviceAllocatorImpl to skip CUB when use_rmm=True

* Update the demo

* [CI] Pin NumPy to 1.19.4, since NumPy 1.19.5 doesn't work with latest Shap

demo/rmm_plugin/rmm_mgpu_with_dask.py

@@ -5,13 +5,16 @@ from dask.distributed import Client
 from dask_cuda import LocalCUDACluster
 
 def main(client):
+    # Inform XGBoost that RMM is used for GPU memory allocation
+    xgb.set_config(use_rmm=True)
+
     X, y = make_classification(n_samples=10000, n_informative=5, n_classes=3)
     X = dask.array.from_array(X)
     y = dask.array.from_array(y)
     dtrain = xgb.dask.DaskDMatrix(client, X, label=y)
 
     params = {'max_depth': 8, 'eta': 0.01, 'objective': 'multi:softprob', 'num_class': 3,
-              'tree_method': 'gpu_hist'}
+              'tree_method': 'gpu_hist', 'eval_metric': 'merror'}
     output = xgb.dask.train(client, params, dtrain, num_boost_round=100,
                             evals=[(dtrain, 'train')])
     bst = output['booster']

demo/rmm_plugin/rmm_singlegpu.py

@@ -4,6 +4,8 @@ from sklearn.datasets import make_classification
 
 # Initialize RMM pool allocator
 rmm.reinitialize(pool_allocator=True)
+# Inform XGBoost that RMM is used for GPU memory allocation
+xgb.set_config(use_rmm=True)
 
 X, y = make_classification(n_samples=10000, n_informative=5, n_classes=3)
 dtrain = xgb.DMatrix(X, label=y)