Support of scikit-learn with ray joblib backend (#6925)

doc/source/joblib.rst

@@ -0,0 +1,69 @@
+sklearn Ray Backend API (Experimental)
+=======================================
+
+.. warning::
+
+  Support for running scikit-learn on Ray is an experimental feature,
+  so it may be changed at any time without warning. If you encounter any
+  bugs/shortcomings/incompatibilities, please file an `issue on GitHub`_.
+  Contributions are always welcome!
+
+.. _`issue on GitHub`: https://github.com/ray-project/ray/issues
+
+Ray supports running distributed `scikit-learn`_ programs by 
+implementing a Ray backend for `joblib`_ using `Ray Actors <actors.html>`__ 
+instead of local processes. This makes it easy to scale existing applications
+that use scikit-learn from a single node to a cluster.
+
+.. _`joblib`: https://joblib.readthedocs.io
+.. _`scikit-learn`: https://scikit-learn.org
+
+Quickstart
+----------
+
+To get started, first `install Ray <installation.html>`__, then use 
+``from ray.experimental.joblib import register_ray`` and run ``register_ray()``.
+This will register Ray as a joblib backend for scikit-learn to use.
+Then run your original scikit-learn code inside 
+``with joblib.parallel_backend('ray')``. This will start a local Ray cluster. 
+See the `Run on a Cluster`_ section below for instructions to run on 
+a multi-node Ray cluster instead.
+
+.. code-block:: python
+
+  import numpy as np
+  from sklearn.datasets import load_digits
+  from sklearn.model_selection import RandomizedSearchCV
+  from sklearn.svm import SVC
+  digits = load_digits()
+  param_space = {
+      'C': np.logspace(-6, 6, 30),
+      'gamma': np.logspace(-8, 8, 30),
+      'tol': np.logspace(-4, -1, 30),
+      'class_weight': [None, 'balanced'],
+  }
+  model = SVC(kernel='rbf')
+  search = RandomizedSearchCV(model, param_space, cv=5, n_iter=300, verbose=10)
+
+  import joblib
+  from ray.experimental.joblib import register_ray
+  register_ray()
+  with joblib.parallel_backend('ray'):
+      search.fit(digits.data, digits.target)
+
+Run on a Cluster
+----------------
+
+This section assumes that you have a running Ray cluster. To start a Ray cluster,
+please refer to the `cluster setup <cluster-index.html>`__ instructions.
+
+To connect a scikit-learn to a running Ray cluster, you have to specify the address of the
+head node by setting the ``RAY_ADDRESS`` environment variable.
+
+You can also start Ray manually by calling ``ray.init()`` (with any of its supported
+configuration options) before calling ``with joblib.parallel_backend('ray')``.
+
+.. warning::
+    
+    If you do not set the ``RAY_ADDRESS`` environment variable and do not provide
+    ``address`` in ``ray.init(address=<address>)`` then scikit-learn will run on a SINGLE node!