Document the optimization.

doc/tutorials/dask.rst

@@ -112,8 +112,11 @@ is a ``DaskDMatrix`` or ``da.Array``.  When putting dask collection directly int
 ``predict`` function or using ``inplace_predict``, the output type depends on input data.
 See next section for details.
 
-Alternatively, XGBoost also implements the Scikit-Learn interface with ``DaskXGBClassifier``
-and ``DaskXGBRegressor``. See ``xgboost/demo/dask`` for more examples.
+Alternatively, XGBoost also implements the Scikit-Learn interface with
+``DaskXGBClassifier``, ``DaskXGBRegressor``, ``DaskXGBRanker`` and 2 random forest
+variances.  This wrapper is similar to the single node Scikit-Learn interface in xgboost,
+with dask collection as inputs and has an additional ``client`` attribute.  See
+``xgboost/demo/dask`` for more examples.
 
 
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@@ -160,6 +163,32 @@ if not using GPU, the number of threads used for prediction on each block matter
 now, xgboost uses single thread for each partition.  If the number of blocks on each
 workers is smaller than number of cores, then the CPU workers might not be fully utilized.
 
+One simple optimization for running consecutive predictions is using
+``distributed.Future``:
+
+.. code-block:: python
+
+  dataset = [X_0, X_1, X_2]
+  booster_f = client.scatter(booster, broadcast=True)
+  futures = []
+  for X in dataset:
+    # Here we pass in a future instead of concrete booster
+    shap_f = xgb.dask.predict(client, booster_f, X, pred_contribs=True)
+    futures.append(shap_f)
+
+  results = client.gather(futures)
+
+
+This is only available on functional interface, as the Scikit-Learn wrapper doesn't know
+how to maintain a valid future for booster.  To obtain the booster object from
+Scikit-Learn wrapper object:
+
+.. code-block:: python
+
+  cls = xgb.dask.DaskXGBClassifier()
+  cls.fit(X, y)
+
+  booster = cls.get_booster()
 
 
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