properly

docs/source/_static/js/redirect.js

@@ -0,0 +1,14 @@
+var url = window.location.href;
+var newurl = "http://ml.dask.org" + url.split("/build/html")[1]
+
+htmlstr = "<div class=\"admonition note\">\
+    <p class=\"first admonition-title\">Note</p>\
+    <p class=\"last\">The documentation has moved to\
+                      <a href=" + newurl + ">ml.dask.org</a></p>\
+           </div>";
+
+if (url.indexOf("readthedocs") !== -1) {
+    $(document).ready(function () {
+        $(".section > h1").prepend(htmlstr);
+    })
+}

docs/source/conf.py

@@ -64,7 +64,7 @@
 numpydoc_class_members_toctree = False
 autodoc_default_flags = ["members", "inherited-members"]
 autosummary_generate = True
-# templates_path = ["templates"]
+templates_path = ["templates"]
 
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
@@ -221,9 +221,7 @@ def update_examples(app):
 
 
 def setup(app):
-    # app.connect("autodoc-process-docstring", generate_example_rst)
-    # app.connect("builder-inited", update_examples)
-    pass
+    app.add_javascript("js/custom.js")
 
 
 extlinks = {

docs/source/index.rst

@@ -79,7 +79,7 @@ algorithms easily.  This is employed for :doc:`linear models <glm>`, :doc:`pre-p
 Partner with other distributed libraries
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Other machine learning libraries like XGBoost and TensorFlow already have
+Other ML libraries like XGBoost and TensorFlow already have
 distributed solutions that work quite well.  Dask-ML makes no attempt to
 re-implement these systems.  Instead, Dask-ML makes it easy to use normal Dask
 workflows to prepare and set up data, then it deploys XGBoost or Tensorflow