Website for the v0.1 release (#576)

* commit jekyll template

* Port blog post to markdown.

* Small changes.

* Improvements to layout and post.

* More improvements.

* Add computation graph figures to the blog post.

* Small changes.

* Update gitignore.

.gitignore

@@ -113,3 +113,8 @@ cmake-build-debug/
 # Gradle:
 .idea/**/gradle.xml
 .idea/**/libraries
+
+# Website
+/site/Gemfile.lock
+/site/.sass-cache
+/site/_site

site/Gemfile

@@ -0,0 +1,28 @@
+source "https://rubygems.org"
+ruby RUBY_VERSION
+
+# Hello! This is where you manage which Jekyll version is used to run.
+# When you want to use a different version, change it below, save the
+# file and run `bundle install`. Run Jekyll with `bundle exec`, like so:
+#
+#     bundle exec jekyll serve
+#
+# This will help ensure the proper Jekyll version is running.
+# Happy Jekylling!
+gem "jekyll", "3.4.3"
+
+# This is the default theme for new Jekyll sites. You may change this to anything you like.
+gem "minima", "~> 2.0"
+
+# If you want to use GitHub Pages, remove the "gem "jekyll"" above and
+# uncomment the line below. To upgrade, run `bundle update github-pages`.
+# gem "github-pages", group: :jekyll_plugins
+
+# If you have any plugins, put them here!
+group :jekyll_plugins do
+   gem "jekyll-feed", "~> 0.6"
+end
+
+# Windows does not include zoneinfo files, so bundle the tzinfo-data gem
+gem 'tzinfo-data', platforms: [:mingw, :mswin, :x64_mingw, :jruby]
+

site/README.md

@@ -0,0 +1,24 @@
+# Ray Website
+
+## Development instructions
+
+With Ruby >= 2.1 installed, run:
+
+```
+gem install jekyll bundler
+bundle install
+```
+
+To view the site, run:
+
+```
+bundle exec jekyll serve
+```
+
+## Deployment
+
+To deploy the site, run (inside the main ray directory):
+
+```
+git subtree push --prefix site origin gh-pages
+```

site/_config.yml

@@ -0,0 +1,31 @@
+# Welcome to Jekyll!
+#
+# This config file is meant for settings that affect your whole blog, values
+# which you are expected to set up once and rarely edit after that. If you find
+# yourself editing this file very often, consider using Jekyll's data files
+# feature for the data you need to update frequently.
+#
+# For technical reasons, this file is *NOT* reloaded automatically when you use
+# 'bundle exec jekyll serve'. If you change this file, please restart the server process.
+
+# Site settings
+# These are used to personalize your new site. If you look in the HTML files,
+# you will see them accessed via {{ site.title }}, {{ site.email }}, and so on.
+# You can create any custom variable you would like, and they will be accessible
+# in the templates via {{ site.myvariable }}.
+title: "Ray: A Distributed Execution Framework for AI Applications"
+email: ""
+description: > # this means to ignore newlines until "baseurl:"
+  Ray is a flexible, high-performance distributed execution framework for AI applications.
+baseurl: "" # the subpath of your site, e.g. /blog
+url: "" # the base hostname & protocol for your site, e.g. http://example.com
+github_username:  ray-project
+
+# Build settings
+markdown: kramdown
+theme: minima
+gems:
+  - jekyll-feed
+exclude:
+  - Gemfile
+  - Gemfile.lock

site/_posts/2017-05-17-announcing-ray.markdown

@@ -0,0 +1,231 @@
+---
+layout: post
+title: "Ray: A Distributed Execution Framework for AI Applications"
+excerpt: "This post announces Ray, a framework for efficiently running Python code on clusters and large mult-core machines."
+date: 2017-05-20 14:00:00
+---
+
+This post announces Ray, a framework for efficiently running Python code on
+clusters and large multi-core machines. The project is open source.
+You can check out [the code](https://github.com/ray-project/ray) and
+[the documentation](http://ray.readthedocs.io/en/latest/?badge=latest).
+
+Many AI algorithms are computationally intensive and exhibit complex
+communication patterns. As a result, many researchers spend most of their
+time building custom systems to efficiently distribute their code across
+clusters of machines.
+
+However, the resulting systems are often specific to a single algorithm or class
+of algorithms. We built Ray to help eliminate a bunch of the redundant
+engineering effort that is currently repeated over and over for each new
+algorithm. Our hope is that a few basic primitives can be reused to implement
+and to efficiently execute a broad range of algorithms and applications.
+
+<div align="center">
+<iframe width="560" height="315" src="https://www.youtube.com/embed/07eIebEk1MM?autoplay=1&loop=1&playlist=07eIebEk1MM" frameborder="0" allowfullscreen></iframe>
+<div>A simulated robot learning to run using Ray.</div>
+</div>
+<br />
+
+# Simple Parallelization of Existing Code
+
+Ray enables Python functions to be executed remotely with minimal modifications.
+
+With **regular Python**, when you call a function, the call blocks until the
+function has been executed. This example would take 8 seconds to execute.
+
+{% highlight python %}
+def f():
+    time.sleep(1)
+
+# Calls to f executed serially.
+results = []
+for _ in range(8):
+    result = f()
+    results.append(result)
+{% endhighlight %}
+
+**With Ray**, when you call a **remote function**, the call immediately returns
+an object ID. A task is then created, scheduled, and executed somewhere in the
+cluster. This example would take 1 second to execute.
+
+{% highlight python %}
+@ray.remote
+def f():
+    time.sleep(1)
+
+# Tasks executed in parallel.
+results = []
+for _ in range(8):
+    result = f.remote()
+    results.append(result)
+
+results = ray.get(results)
+{% endhighlight %}
+
+Note that the only changes are that we add the ``@ray.remote`` decorator to the
+function definition, we call the function with ``f.remote()``, and we call
+``ray.get`` on the list of object IDs in order to block until the corresponding
+tasks have finished executing.
+
+<div align="center">
+<img src="/assets/announcing_ray/graph1.png">
+</div>
+<div><i>A graph depicting the tasks and objects in this example. The circles
+represent tasks, and the boxes represent objects. There are no arrows between
+the 8 separate tasks indicating that all of the tasks can be executed in
+parallel.</i></div>
+<br />
+
+# Flexible Encoding of Task Dependencies
+
+In contrast with bulk-synchronous parallel frameworks like MapReduce or Apache
+Spark, Ray is designed to support AI applications which require fine-grained
+task dependencies. In contrast with the computation of aggregate statistics of
+an entire dataset, a training procedure may operate on a small subset of data or
+on the outputs of a handful of tasks.
+
+Dependencies can be encoded by passing object IDs (which are the outputs of
+tasks) into other tasks.
+
+{% highlight python %}
+import numpy as np
+
+@ray.remote
+def aggregate_data(x, y):
+    return x + y
+
+data = [np.random.normal(size=1000) for i in range(4)]
+
+while len(data) > 1:
+  intermediate_result = aggregate_data.remote(data[0], data[1])
+  data = data[2:] + [intermediate_result]
+
+result = ray.get(data[0])
+{% endhighlight %}
+
+By passing the outputs of some calls to `aggregate_data` into subsequent calls
+to `aggregate_data`, we encode dependencies between these tasks which can be
+used by the system to make scheduling decisions and to coordinate the transfer
+of objects. Note that when object IDs are passed into remote function calls, the
+actual values will be unpacked before the function is executed, so when the
+`aggregate_data` function is executed, `x` and `y` will be numpy arrays.
+
+<div align="center">
+<img src="/assets/announcing_ray/graph2.png">
+</div>
+<div><i>A graph depicting the tasks and objects in this example. The circles
+represent tasks, and the boxes represent objects. Arrows point from tasks to the
+objects they produce and from objects to the tasks that depend on
+them.</i></div>
+<br />
+
+# Shared Mutable State with Actors
+
+Ray uses actors to share mutable state between tasks. Here is an example in
+which multiple tasks share the state of an Atari simulator. Each task runs the
+simulator for several steps picking up where the previous task left off.
+
+{% highlight python %}
+import gym
+
+@ray.remote
+class Simulator(object):
+    def __init__(self):
+        self.env = gym.make("Pong-v0")
+        self.env.reset()
+
+    def step(self, action):
+        return self.env.step(action)
+
+# Create a simulator, this will start a new worker that will run all
+# methods for this actor.
+simulator = Simulator.remote()
+
+observations = []
+for _ in range(4):
+    # Take action 0 in the simulator.
+    observations.append(simulator.step.remote(0))
+{% endhighlight %}
+
+Each call to `simulator.step.remote` generates a task that is scheduled on the
+actor. These tasks mutate the state of the simulator object, and they are
+executed one at a time.
+
+Like remote functions, actor methods return object IDs that can be passed into
+other tasks and whose values can be retrieved with `ray.get`.
+
+<div align="center">
+<img src="/assets/announcing_ray/graph3.png">
+</div>
+<div><i>A graph depicting the tasks and objects in this example. The circles
+represent tasks, and the boxes represent objects. The first task is the actor's
+constructor. The thick arrows are used to show that the methods invoked on this
+actor share the underlying state of the actor.</i></div>
+<br />
+
+# Efficient Shared Memory and Serialization with Apache Arrow
+
+Serializing and deserializing data is often a bottleneck in distributed
+computing. Ray lets worker processes on the same machine access the same objects
+through shared memory. To facilitate this, Ray uses an in-memory object store on
+each machine to serve objects.
+
+To illustrate the problem, suppose we create some neural network weights and
+wish to ship them from one Python process to another.
+
+{% highlight python %}
+import numpy as np
+
+weights = {"Variable{}".format(i): np.random.normal(size=5000000)
+           for i in range(10)}  # 2.68s
+{% endhighlight %}
+
+To ship the neural network weights around, we need to first serialize them into
+a contiguous blob of bytes. This can be done with standard serialization
+libraries like pickle.
+
+{% highlight python %}
+import pickle
+
+# Serialize the weights with pickle. Then deserialize them.
+pickled_weights = pickle.dumps(weights)      # 0.986s
+new_weights = pickle.loads(pickled_weights)  # 0.241s
+{% endhighlight %}
+
+The time required for deserialization is particularly important because one of
+the most common patterns in machine learning is to aggregate a large number of
+values (for example, neural net weights, rollouts, or other values) in a single
+process, so the deserialization step could happen hundreds of times in a row.
+
+To minimize the time required to deserialize objects in shared memory, we use
+the [Apache Arrow](https://arrow.apache.org/) data layout. This allows us to
+compute offsets into the serialized blob without scanning through the entire
+blob. **In practice, this can translate into deserialization that is several
+orders of magnitude faster**.
+
+{% highlight python %}
+# Serialize the weights and copy them into the object store. Then deserialize
+# them.
+weights_id = ray.put(weights)      # 0.525s
+new_weights = ray.get(weights_id)  # 0.000622s
+{% endhighlight %}
+
+The call to `ray.put` serializes the weights using Arrow and copies the result
+into the object store's memory. The call to `ray.get` then deserializes the
+serialized object and constructs a new dictionary of numpy arrays. However, the
+underlying arrays backing the numpy arrays live in shared memory and are not
+copied into the Python process's heap.
+
+Note that if the call to `ray.get` happens from a different machine, the
+relevant serialized object will be copied from a machine where it lives to the
+machine where it is needed.
+
+In this example, we call `ray.put` explicitly. However, normally this call would
+happen under the hood when a Python object is passed into a remote function or
+returned from a remote function.
+
+# Feedback is Appreciated
+
+This project is in its early stages. If you try it out, we'd love to hear your
+thoughts and suggestions.

site/index.html

@@ -0,0 +1,13 @@
+---
+layout: default
+---
+
+<div class="home">
+
+{% for post in site.posts %}
+  <span>{{ post.date | date: "%b %-d, %Y" }}</span>
+  <a class="post-link" href="{{ post.url | prepend: site.baseurl }}">{{ post.title }}</a>
+  {{ post.excerpt }}
+{% endfor %}
+
+</div>