Add documentation for Dask-on-Ray (#10452)

* Dask on ray

* Dask on ray

* line

* Add links

doc/source/dask-on-ray.rst

@@ -0,0 +1,43 @@
+Dask on Ray
+===========
+
+Ray offers an experimental scheduler backend for Dask.
+With this plugin, you can use familiar Dask APIs such as Dask DataFrames, and the computation will be executed by the Ray system.
+
+The Ray plugin can be used with any Dask `.compute() <https://docs.dask.org/en/latest/api.html#dask.compute>`__ call.
+Note that for execution on a Ray cluster, you should *not* use the `Dask.distributed <https://distributed.dask.org/en/latest/quickstart.html>`__ client.
+Just follow the instructions for :ref:`using Ray on a cluster <using-ray-on-a-cluster>` to modify the ``ray.init()`` call.
+Here's an example:
+
+.. code-block:: python
+
+    import ray
+    from ray.experimental.dask import ray_dask_get
+    import dask.delayed
+    from time import sleep
+
+    # Start Ray.
+    # Tip: If you're connecting to an existing cluster, use ray.init(address="auto").
+    ray.init()
+
+
+    def inc(x):
+        sleep(1)
+        return x + 1
+
+    def add(x, y):
+        sleep(1)
+        return x + y
+
+    x = dask.delayed(inc)(1)
+    y = dask.delayed(inc)(2)
+    z = dask.delayed(add)(x, y)
+    # The Dask scheduler submits the recorded task graph to Ray.
+    z.compute(scheduler=ray_dask_get)
+
+Why use this feature?
+
+    1. If you'd like to use Dask and Ray libraries in the same application.
+    2. To take advantage of Ray-specific features such as the :ref:`cluster launcher <ref-automatic-cluster>` and :ref:`shared-memory store <memory>`.
+
+Note that Dask-on-Ray is an ongoing project and is not expected to achieve the same performance as using Ray directly.

doc/source/index.rst

@@ -207,6 +207,7 @@ Academic Papers
    joblib.rst
    iter.rst
    pandas_on_ray.rst
+   dask-on-ray.rst
 
 .. toctree::
    :hidden:

doc/source/memory-management.rst

@@ -1,3 +1,5 @@
+.. _memory:
+
 Memory Management
 =================
 

doc/source/ray-overview/basics.rst

@@ -26,6 +26,8 @@ On top of **Ray Core** are several libraries for solving problems in machine lea
 
 Ray also has a number of other community contributed libraries:
 
+- :doc:`../dask-on-ray`
+- `Mars on Ray <https://github.com/mars-project/mars/pull/1508>`__
 - :doc:`../pandas_on_ray`
 - :doc:`../joblib`
-- :doc:`../multiprocessing`
+- :doc:`../multiprocessing`

doc/source/starting-ray.rst

@@ -43,6 +43,8 @@ To check if Ray is initialized, you can call ``ray.is_initialized()``:
 
 See the `Configuration <configure.html>`__ documentation for the various ways to configure Ray.
 
+.. _using-ray-on-a-cluster:
+
 Using Ray on a cluster
 ----------------------