Clarify the behavior of use_rmm. (#6808)

* Clarify the `use_rmm` flag in document and demo.

demo/rmm_plugin/rmm_singlegpu.py

@@ -4,13 +4,18 @@ 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)
+# Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md
+# 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)
 
-params = {'max_depth': 8, 'eta': 0.01, 'objective': 'multi:softprob', 'num_class': 3,
-          'tree_method': 'gpu_hist'}
+params = {
+    "max_depth": 8,
+    "eta": 0.01,
+    "objective": "multi:softprob",
+    "num_class": 3,
+    "tree_method": "gpu_hist",
+}
 # XGBoost will automatically use the RMM pool allocator
-bst = xgb.train(params, dtrain, num_boost_round=100, evals=[(dtrain, 'train')])
+bst = xgb.train(params, dtrain, num_boost_round=100, evals=[(dtrain, "train")])