Rewrite Dask interface. (#4819)

demo/dask/README.md

@@ -1,20 +0,0 @@
-# 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
-```