Doc.

doc/tutorials/dask.rst

@@ -37,7 +37,6 @@ illustrates the basic usage:
 
  output = xgb.dask.train(client,
  {'verbosity': 2,
- 'nthread': 1,
  'tree_method': 'hist'},
  dtrain,
  num_boost_round=4, evals=[(dtrain, 'train')])
@@ -76,6 +75,32 @@ Another set of API is a Scikit-Learn wrapper, which mimics the stateful Scikit-L
 interface with ``DaskXGBClassifier`` and ``DaskXGBRegressor``. See ``xgboost/demo/dask``
 for more examples.
 
+*******
+Threads
+*******
+
+XGBoost has built in support for parallel computation through threads by the setting
+``nthread`` parameter (``n_jobs`` for scikit-learn). If these parameters are set, they
+will override the configuration in Dask. For example:
+
+.. code-block:: python
+
+ with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
+
+There are 4 threads allocated for each dask worker. Then by default XGBoost will use 4
+threads in each process for both training and prediction. But if ``nthread`` parameter is
+set:
+
+.. code-block:: python
+
+ output = xgb.dask.train(client,
+ {'verbosity': 1,
+ 'nthread': 8,
+ 'tree_method': 'hist'},
+ dtrain,
+ num_boost_round=4, evals=[(dtrain, 'train')])
+
+XGBoost will use 8 threads in each training process.
 
 *****************************************************************************
 Why is the initialization of ``DaskDMatrix`` so slow and throws weird errors