Doc modernization (#3474)

* Change doc build to reST exclusively

* Rewrite Intro doc in reST; create toctree

* Update parameter and contribute

* Convert tutorials to reST

* Convert Python tutorials to reST

* Convert CLI and Julia docs to reST

* Enable markdown for R vignettes

* Done migrating to reST

* Add guzzle_sphinx_theme to requirements

* Add breathe to requirements

* Fix search bar

* Add link to user forum

doc/tutorials/external_memory.rst

@@ -0,0 +1,51 @@
+############################################
+Using XGBoost External Memory Version (beta)
+############################################
+There is no big difference between using external memory version and in-memory version.
+The only difference is the filename format.
+
+The external memory version takes in the following filename format:
+
+.. code-block:: none
+
+  filename#cacheprefix
+
+The ``filename`` is the normal path to libsvm file you want to load in, and ``cacheprefix`` is a
+path to a cache file that XGBoost will use for external memory cache.
+
+The following code was extracted from `demo/guide-python/external_memory.py <https://github.com/dmlc/xgboost/blob/master/demo/guide-python/external_memory.py>`_:
+
+.. code-block:: python
+
+  dtrain = xgb.DMatrix('../data/agaricus.txt.train#dtrain.cache')
+
+You can find that there is additional ``#dtrain.cache`` following the libsvm file, this is the name of cache file.
+For CLI version, simply add the cache suffix, e.g. ``"../data/agaricus.txt.train#dtrain.cache"``.
+
+****************
+Performance Note
+****************
+* the parameter ``nthread`` should be set to number of **physical** cores
+
+  - Most modern CPUs use hyperthreading, which means a 4 core CPU may carry 8 threads
+  - Set ``nthread`` to be 4 for maximum performance in such case
+
+*******************
+Distributed Version
+*******************
+The external memory mode naturally works on distributed version, you can simply set path like
+
+.. code-block:: none
+
+  data = "hdfs://path-to-data/#dtrain.cache"
+
+XGBoost will cache the data to the local position. When you run on YARN, the current folder is temporal
+so that you can directly use ``dtrain.cache`` to cache to current folder.
+
+**********
+Usage Note
+**********
+* This is a experimental version
+* Currently only importing from libsvm format is supported
+
+  - Contribution of ingestion from other common external memory data source is welcomed