[DIST] Add Distributed XGBoost on AWS Tutorial

demo/distributed-training/README.md

@@ -10,43 +10,14 @@ Build XGBoost with Distributed Filesystem Support
 To use distributed xgboost, you only need to turn the options on to build
 with distributed filesystems(HDFS or S3) in ```xgboost/make/config.mk```.
 
-How to Use
-----------
-* Input data format: LIBSVM format. The example here uses generated data in ../data folder.
-* Put the data into some distribute filesytem (S3 or HDFS)
-* Use tracker script in dmlc-core/tracker to submit the jobs
-* Like all other DMLC tools, xgboost support taking a path to a folder as input argument
-  - All the files in the folder will be used as input
-* Quick start in Hadoop YARN: run ```bash run_yarn.sh <n_hadoop_workers> <n_thread_per_worker> <path_in_HDFS>```
 
-Example
--------
-* [run_yarn.sh](run_yarn.sh) shows how to submit job to Hadoop via YARN.
-
-Single machine vs Distributed Version
--------------------------------------
-If you have used xgboost (single machine version) before, this section will show you how to run xgboost on hadoop with a slight modification on conf file.
-* IO: instead of reading and writing file locally, we now use HDFS, put ```hdfs://``` prefix to the address of file you like to access
-* File cache: ```dmlc_yarn.py``` also provide several ways to cache necesary files, including binary file (xgboost), conf file
-  - ```dmlc_yarn.py``` will automatically cache files in the command line. For example, ```dmlc_yarn.py -n 3 $localPath/xgboost.dmlc mushroom.hadoop.conf``` will cache "xgboost.dmlc" and "mushroom.hadoop.conf".
-  - You could also use "-f" to manually cache one or more files, like ```-f file1 -f file2```
-  - The local path of cached files in command is "./".
-* More details of submission can be referred to the usage of ```dmlc_yarn.py```.
-* The model saved by hadoop version is compatible with single machine version.
-
-Notes
------
-* The code is optimized with multi-threading, so you will want to run xgboost with more vcores for best performance.
-  - You will want to set <n_thread_per_worker> to be number of cores you have on each machine.
+Step by Step Tutorial on AWS
+----------------------------
+Checkout [this tutorial](https://xgboost.readthedocs.org/en/latest/tutorial/aws_yarn.html) for running distributed xgboost.
 
 
-External Memory Version
------------------------
-XGBoost supports external memory, this will make each process cache data into local disk during computation, without taking up all the memory for storing the data.
-See [external memory](https://github.com/dmlc/xgboost/tree/master/doc/external_memory.md) for syntax using external memory.
-
-You only need to add cacheprefix to the input file to enable external memory mode. For example set training data as
-```
-data=hdfs:///path-to-my-data/#dtrain.cache
-```
-This will make xgboost more memory efficient, allows you to run xgboost on larger-scale dataset.
+Model Analysis
+--------------
+XGBoost is exchangable across all bindings and platforms.
+This means you can use python or R to analyze the learnt model and do prediction.
+For example, you can use the [plot_model.ipynb](plot_model.ipynb) to visualize the learnt model.