Add native support for Dask (#4473)

* Add native support for Dask

* Add multi-GPU demo

* Add sklearn example

demo/dask/README.md

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+# Dask Integration
+
+[Dask](https://dask.org/) is a parallel computing library built on Python. Dask allows easy management of distributed workers and excels handling large distributed data science workflows.
+
+The simple demo shows how to train and make predictions for an xgboost model on a distributed dask environment. We make use of first-class support in xgboost for launching dask workers. Workers launched in this manner are automatically connected via xgboosts underlying communication framework, Rabit. The calls to `xgb.train()` and `xgb.predict()` occur in parallel on each worker and are synchronized.
+
+The GPU demo shows how to configure and use GPUs on the local machine for training on a large dataset.
+
+## Requirements
+Dask is trivial to install using either pip or conda. [See here for official install documentation](https://docs.dask.org/en/latest/install.html).
+
+The GPU demo requires [GPUtil](https://github.com/anderskm/gputil) for detecting system GPUs.
+
+Install via `pip install gputil` 
+
+## Running the scripts
+```bash
+python dask_simple_demo.py
+python dask_gpu_demo.py
+```