Fix image paths in blog post, add section on ray.wait. (#580)

* Fix image paths in blog post.

* Use future instead of object ID.

* Add description of ray.wait.

* Revert to keep some of the object ID terminology.

site/README.md

@@ -15,6 +15,11 @@ To view the site, run:
 bundle exec jekyll serve
 ```
 
+Note that images included under `site/assets/` should be referred to with
+`<img src="{{ site.base-url }}/ray/assets/...">`. They will not render properly
+when serving the site locally, but this is required for getting the paths to
+work out on GitHub.
+
 ## Deployment
 
 To deploy the site, run (inside the main ray directory):

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

@@ -46,8 +46,9 @@ for _ in range(8):
 {% 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.
+a future (we will refer to these as object IDs). A task is then created,
+scheduled, and executed somewhere in the cluster. This example would take 1
+second to execute.
 
 {% highlight python %}
 @ray.remote
@@ -65,11 +66,11 @@ results = ray.get(results)
 
 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.
+``ray.get`` on the list of object IDs (remember that object IDs are futures) in
+order to block until the corresponding tasks have finished executing.
 
 <div align="center">
-<img src="/assets/announcing_ray/graph1.png">
+<img src="{{ site.base-url }}/ray/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
@@ -112,7 +113,7 @@ 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">
+<img src="{{ site.base-url }}/ray/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
@@ -152,11 +153,11 @@ 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`.
+Like remote functions, actor methods return object IDs (that is, futures) 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">
+<img src="{{ site.base-url }}/ray/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
@@ -164,6 +165,43 @@ 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 />
 
+# Waiting for a Subset of Tasks to Finish
+
+Sometimes when running tasks with variable durations, we don't want to wait for
+all of the tasks to finish. Instead, we may wish to wait for half of the tasks
+to finish or perhaps to use whichever tasks have completed after one second.
+
+{% highlight python %}
+@ray.remote
+def f():
+    time.sleep(np.random.uniform(0, 5))
+
+# Launch 10 tasks with variable durations.
+results = [f.remote() for _ in range(10)]
+
+# Wait until either five tasks have completed or two seconds have passed and
+# return a list of the object IDs whose tasks have finished.
+ready_ids, remaining_ids = ray.wait(results, num_returns=5, timeout=2000)
+{% endhighlight %}
+
+In this example `ready_ids` is a list of object IDs whose corresponding tasks
+have finished executing, and `remaining_ids` is a list of the remaining object
+IDs.
+
+This primitive makes it easy to implement other behaviors, for example we may
+wish to process some tasks in the order that they complete.
+
+{% highlight python %}
+# Launch 10 tasks with variable durations.
+remaining_ids = [f.remote() for _ in range(10)]
+
+# Process the tasks in the order that they complete.
+results = []
+while len(remaining_ids) > 0:
+    ready_ids, remaining_ids = ray.wait(remaining_ids, num_returns=1)
+    results.append(ray.get(ready_ids[0]))
+{% endhighlight %}
+
 # Efficient Shared Memory and Serialization with Apache Arrow
 
 Serializing and deserializing data is often a bottleneck in distributed