[Serve] Rename RayServe -> "Ray Serve" in Documentation (#8504)

doc/source/index.rst

@@ -280,12 +280,12 @@ Getting Involved
 
 .. toctree::
    :maxdepth: -1
-   :caption: RayServe
+   :caption: Ray Serve
 
-   rayserve/overview.rst
+   serve/overview.rst
-   rayserve/tutorials/tensorflow-tutorial.rst
+   serve/tutorials/tensorflow-tutorial.rst
-   rayserve/tutorials/pytorch-tutorial.rst
+   serve/tutorials/pytorch-tutorial.rst
-   rayserve/tutorials/sklearn-tutorial.rst
+   serve/tutorials/sklearn-tutorial.rst
 
 .. toctree::
    :maxdepth: -1

doc/source/serve/overview.rst

@@ -1,7 +1,7 @@
 .. _rayserve:
 
-RayServe: Scalable and Programmable Serving
+Ray Serve: Scalable and Programmable Serving
-===========================================
+============================================
 
 .. image:: logo.svg
     :align: center
@@ -13,21 +13,21 @@ RayServe: Scalable and Programmable Serving
 Overview
 --------
 
-RayServe is a scalable model-serving library built on Ray.
+Ray Serve is a scalable model-serving library built on Ray.
 
-For users RayServe is:
+For users Ray Serve is:
 
 - **Framework Agnostic**:Use the same toolkit to serve everything from deep learning models 
   built with frameworks like PyTorch or TensorFlow to scikit-learn models or arbitrary business logic.
 - **Python First**: Configure your model serving with pure Python code - no more YAMLs or 
   JSON configs.
 
-RayServe enables: 
+Ray Serve enables: 
 
 -  **A/B test models** with zero downtime by decoupling routing logic from response handling logic.
 - **Batching** built-in to help you meet your performance objectives.
 
-Since Ray is built on Ray, RayServe also allows you to **scale to many machines**
+Since Ray is built on Ray, Ray Serve also allows you to **scale to many machines**
 and allows you to leverage all of the other Ray frameworks so you can deploy and scale on any cloud.
 
 .. note:: 
@@ -37,7 +37,7 @@ and allows you to leverage all of the other Ray frameworks so you can deploy and
 
 Installation
 ~~~~~~~~~~~~
-RayServe supports Python versions 3.5 and higher. To install RayServe:
+Ray Serve supports Python versions 3.5 and higher. To install Ray Serve:
 
 .. code-block:: bash
 
@@ -45,8 +45,8 @@ RayServe supports Python versions 3.5 and higher. To install RayServe:
 
 
 
-RayServe in 90 Seconds
+Ray Serve in 90 Seconds
-~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~
 
 Serve a stateless function:
 
@@ -56,10 +56,10 @@ Serve a stateful class:
 
 .. literalinclude:: ../../../python/ray/serve/examples/doc/quickstart_class.py
 
-See :ref:`serve-key-concepts` for more information about working with RayServe.
+See :ref:`serve-key-concepts` for more information about working with Ray Serve.
 
-Why RayServe?
+Why Ray Serve?
-~~~~~~~~~~~~~
+~~~~~~~~~~~~~~
 
 There are generally two ways of serving machine learning applications, both with serious limitations:
 you can build using a **traditional webserver** - your own Flask app or you can use a cloud hosted solution.
@@ -68,24 +68,24 @@ The first approach is easy to get started with, but it's hard to scale each comp
 requires vendor lock-in (SageMaker), framework specific tooling (TFServing), and a general
 lack of flexibility.
 
-RayServe solves these problems by giving a user the ability to leverage the simplicity
+Ray Serve solves these problems by giving a user the ability to leverage the simplicity
 of deployment of a simple webserver but handles the complex routing, scaling, and testing logic
 necessary for production deployments.
 
-For more on the motivation behind RayServe, check out these `meetup slides <https://tinyurl.com/serve-meetup>`_.
+For more on the motivation behind Ray Serve, check out these `meetup slides <https://tinyurl.com/serve-meetup>`_.
 
 When should I use Ray Serve?
 ++++++++++++++++++++++++++++
 
-RayServe should be used when you need to deploy at least one model, preferrably many models.  
+Ray Serve should be used when you need to deploy at least one model, preferrably many models.  
-RayServe **won't work well** when you need to run batch prediction over a dataset. Given this use case, we recommend looking into `multiprocessing with Ray </multiprocessing.html>`_.
+Ray Serve **won't work well** when you need to run batch prediction over a dataset. Given this use case, we recommend looking into `multiprocessing with Ray </multiprocessing.html>`_.
 
 .. _serve-key-concepts:
 
 Key Concepts
 ------------
 
-RayServe focuses on **simplicity** and only has two core concepts: endpoints and backends.
+Ray Serve focuses on **simplicity** and only has two core concepts: endpoints and backends.
 
 To follow along, you'll need to make the necessary imports.
 
@@ -128,9 +128,9 @@ Once you define the function (or class) that will handle a request.
 You'd use a function when your response is stateless and a class when you
 might need to maintain some state (like a model). 
 For both functions and classes (that take as input Flask Requests), you'll need to 
-define them as backends to RayServe.
+define them as backends to Ray Serve.
 
-It's important to note that RayServe places these backends in individual workers, which are replicas of the model.
+It's important to note that Ray Serve places these backends in individual workers, which are replicas of the model.
 
 .. code-block:: python
   
@@ -229,7 +229,7 @@ It's trivial to also split traffic, simply specify the endpoint and the backends
 Batching
 ++++++++
 
-You can also have RayServe batch requests for performance. You'll configure this in the backend config.
+You can also have Ray Serve batch requests for performance. You'll configure this in the backend config.
 
 .. code-block:: python
 
@@ -298,7 +298,7 @@ Other Resources
 
 Frameworks
 ~~~~~~~~~~
-RayServe makes it easy to deploy models from all popular frameworks.
+Ray Serve makes it easy to deploy models from all popular frameworks.
 Learn more about how to deploy your model in the following tutorials:
 
 - :ref:`Tensorflow & Keras <serve-tensorflow-tutorial>`

doc/source/serve/tutorials/pytorch-tutorial.rst

@@ -9,16 +9,16 @@ In particular, we show:
 - How to load the model from PyTorch's pre-trained modelzoo.
 - How to parse the JSON request, transform the payload and evaluated in the model.
 
-Please see the :ref:`overview <rayserve-overview>` to learn more general information about RayServe.
+Please see the :ref:`overview <rayserve-overview>` to learn more general information about Ray Serve.
 
-This tutorial requires Pytorch and Torchvision installed in your system. RayServe
+This tutorial requires Pytorch and Torchvision installed in your system. Ray Serve
 is :ref:`framework agnostic <serve_frameworks>` and work with any version of PyTorch.
 
 .. code-block:: bash
 
     pip install torch torchvision
 
-Let's import RayServe and some other helpers.
+Let's import Ray Serve and some other helpers.
 
 .. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_pytorch.py
     :start-after: __doc_import_begin__
@@ -32,7 +32,7 @@ The ``__call__`` method will be invoked per request.
     :start-after: __doc_define_servable_begin__
     :end-before: __doc_define_servable_end__
 
-Now that we've defined our services, let's deploy the model to RayServe. We will
+Now that we've defined our services, let's deploy the model to Ray Serve. We will
 define an :ref:`endpoint <serve-endpoint>` for the route representing the digit classifier task, a
 :ref:`backend <serve-backend>` correspond the physical implementation, and connect them together.
 

doc/source/serve/tutorials/sklearn-tutorial.rst

@@ -6,18 +6,18 @@ Scikit-Learn Tutorial
 In this guide, we will train and deploy a simple Scikit-Learn classifier.
 In particular, we show:
 
-- How to load the model from file system in your RayServe definition
+- How to load the model from file system in your Ray Serve definition
 - How to parse the JSON request and evaluated in sklearn model
 
-Please see the :ref:`overview <rayserve-overview>` to learn more general information about RayServe.
+Please see the :ref:`overview <rayserve-overview>` to learn more general information about Ray Serve.
 
-RayServe supports :ref:`arbitrary frameworks <serve_frameworks>`. You can use any version of sklearn.
+Ray Serve supports :ref:`arbitrary frameworks <serve_frameworks>`. You can use any version of sklearn.
 
 .. code-block:: bash
 
     pip install scikit-learn
 
-Let's import RayServe and some other helpers.
+Let's import Ray Serve and some other helpers.
 
 .. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
     :start-after: __doc_import_begin__
@@ -36,7 +36,7 @@ The ``__call__`` method will be invoked per request.
     :start-after: __doc_define_servable_begin__
     :end-before: __doc_define_servable_end__
 
-Now that we've defined our services, let's deploy the model to RayServe. We will
+Now that we've defined our services, let's deploy the model to Ray Serve. We will
 define an :ref:`endpoint <serve-endpoint>` for the route representing the classifier task, a
 :ref:`backend <serve-backend>` correspond the physical implementation, and connect them together.
 

doc/source/serve/tutorials/tensorflow-tutorial.rst

@@ -6,12 +6,12 @@ Keras and Tensorflow Tutorial
 In this guide, we will train and deploy a simple Tensorflow neural net.
 In particular, we show:
 
-- How to load the model from file system in your RayServe definition
+- How to load the model from file system in your Ray Serve definition
 - How to parse the JSON request and evaluated in Tensorflow
 
-Please see the :ref:`overview <rayserve-overview>` to learn more general information about RayServe.
+Please see the :ref:`overview <rayserve-overview>` to learn more general information about Ray Serve.
 
-RayServe makes it easy to deploy models from :ref:`all popular frameworks <serve_frameworks>`.
+Ray Serve makes it easy to deploy models from :ref:`all popular frameworks <serve_frameworks>`.
 However, for this tutorial, we use Tensorflow 2 and Keras. Please make sure you have
 Tensorflow 2 installed.
 
@@ -20,7 +20,7 @@ Tensorflow 2 installed.
 
     pip install "tensorflow>=2.0"
 
-Let's import RayServe and some other helpers.
+Let's import Ray Serve and some other helpers.
 
 .. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_tensorflow.py
     :start-after: __doc_import_begin__
@@ -39,7 +39,7 @@ The ``__call__`` method will be invoked per request.
     :start-after: __doc_define_servable_begin__
     :end-before: __doc_define_servable_end__
 
-Now that we've defined our services, let's deploy the model to RayServe. We will
+Now that we've defined our services, let's deploy the model to Ray Serve. We will
 define an :ref:`endpoint <serve-endpoint>` for the route representing the digit classifier task, a
 :ref:`backend <serve-backend>` correspond the physical implementation, and connect them together.