Improve doc and demo for dask. (#4907)

* Add a readme with link to doc.
* Add more comments in the demonstrations code.
* Workaround https://github.com/dask/distributed/issues/3081 .

demo/dask/gpu_training.py

@@ -29,13 +29,16 @@ def main(client):
     bst = output['booster']
     history = output['history']
 
+    # you can pass output directly into `predict` too.
     prediction = xgb.dask.predict(client, bst, dtrain)
     print('Evaluation history:', history)
     return prediction
 
 
 if __name__ == '__main__':
-    # or use any other clusters
-    cluster = LocalCUDACluster(n_workers=4, threads_per_worker=1)
-    client = Client(cluster)
-    main(client)
+    # `LocalCUDACluster` is used for assigning GPU to XGBoost processes.  Here
+    # `n_workers` represents the number of GPUs since we use one GPU per worker
+    # process.
+    with LocalCUDACluster(n_workers=2, threads_per_worker=1) as cluster:
+        with Client(cluster) as client:
+            main(client)