Revert "Support of scikit-learn with ray joblib backend (#6925)" (#6957)

This reverts commit a7ecda6017.

doc/source/joblib.rst

@@ -1,69 +0,0 @@
-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!