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[docs] Make walkthrough and starting Ray materials clear (#7099)

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* make starting ray a separate page

* concept

* Apply suggestions from code review

Co-Authored-By: Edward Oakes <ed.nmi.oakes@gmail.com>

* more fics

* Apply suggestions from code review

Co-Authored-By: Edward Oakes <ed.nmi.oakes@gmail.com>

Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
This commit is contained in:
Richard Liaw
2020-02-11 23:17:30 -08:00
committed by GitHub
co-authored by Edward Oakes
parent 305eaaabe9
commit fc9352c588
10 changed files with 215 additions and 29 deletions
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doc/source/walkthrough.rst
+22 -12
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@@ -1,14 +1,24 @@
Walkthrough
===========
Ray Core Walkthrough
====================
This walkthrough will overview the core concepts of Ray:
1. Using remote functions (tasks) [``ray.remote``]
2. Fetching results (object IDs) [``ray.put``, ``ray.get``, ``ray.wait``]
3. Using remote classes (actors) [``ray.remote``]
1. Starting Ray
2. Using remote functions (tasks) [``ray.remote``]
3. Fetching results (object IDs) [``ray.put``, ``ray.get``, ``ray.wait``]
4. Using remote classes (actors) [``ray.remote``]
With Ray, your code will work on a single machine and can be easily scaled to a
large cluster. To run this walkthrough, install Ray with ``pip install -U ray``.
With Ray, your code will work on a single machine and can be easily scaled to large cluster.
Installation
------------
To run this walkthrough, install Ray with ``pip install -U ray``. For the latest wheels (for a snapshot of ``master``), you can use these instructions at :ref:`install-nightlies`.
Starting Ray
------------
You can start Ray on a single machine by adding this to your python script.
.. code-block:: python
@@ -18,11 +28,11 @@ large cluster. To run this walkthrough, install Ray with ``pip install -U ray``.
# ray.init(address=<cluster-address>) instead.
ray.init()
See the `Configuration <configure.html>`__ documentation for the various ways to
configure Ray. To start a multi-node Ray cluster, see the `cluster setup page
<using-ray-on-a-cluster.html>`__. You can stop ray by calling
``ray.shutdown()``. To check if Ray is initialized, you can call
``ray.is_initialized()``.
...
Ray will then be able to utilize all cores of your machine. Find out how to configure the number of cores Ray will use at :ref:`configuring-ray`.
To start a multi-node Ray cluster, see the `cluster setup page <using-ray-on-a-cluster.html>`__.
Remote functions (Tasks)
------------------------