Documentation: Copy images locally

Changelog

@@ -54,7 +54,7 @@ Important Notes
         >>> celery.platform
         <module 'celery.platform' from '/opt/devel/celery/celery/platform.pyc'>
 
-    Here the compiled module is in :file:`/opt/devel/celery/celery/",
+    Here the compiled module is in :file:`/opt/devel/celery/celery/`,
     to remove the offending files do::
 
         $ rm -f /opt/devel/celery/celery/platform.py*
@@ -1103,7 +1103,7 @@ News
 
     Screenshot:
 
-    .. image:: http://celeryproject.org/img/celeryevshotsm.jpg
+    .. figure:: images/celeryevshotsm.jpg
 
     If you run ``celeryev`` with the ``-d`` switch it will act as an event
     dumper, simply dumping the events it receives to standard out::

docs/index.rst

@@ -1,4 +1,4 @@
-.. image:: http://cloud.github.com/downloads/ask/celery/celery_favicon_128.png
+.. image:: images/celery_favicon_128.png
    :class: celerylogo
 
 =================================

docs/internals/worker.rst

@@ -14,7 +14,7 @@ The worker consists of 4 main components: the broker listener, the scheduler,
 the mediator and the task pool. All these components runs in parallel working
 with two data structures: the ready queue and the ETA schedule.
 
-.. image:: http://cloud.github.com/downloads/ask/celery/Celery1.0-inside-worker.jpg
+.. image:: ../images/Celery1.0-inside-worker.jpg
 
 Data structures
 ===============

docs/userguide/executing.rst

@@ -187,7 +187,7 @@ to use when sending a task:
     3. The default :setting:`CELERY_TASK_SERIALIZER` setting.
 
 
-*Using the ``serializer`` argument to ``apply_async``:
+*Using the ``serializer`` argument to ``apply_async``*:
 
 .. code-block:: python
 
@@ -204,7 +204,7 @@ it is called.  This is something you need to be aware of when sending
 more than one task at a time.
 
 You handle the connection manually by creating a
-publisher::
+publisher:
 
 .. code-block:: python
 

docs/userguide/monitoring.rst

@@ -137,7 +137,7 @@ This can also be used if you're not using Celery with a Django project.
 
 *Screenshot*
 
-.. image:: http://celeryproject.org/beta/djangoceleryadmin2.jpg
+.. figure:: ../images/djangoceleryadmin2.jpg
 
 .. _`django-celery`: http://pypi.python.org/pypi/django-celery
 
@@ -274,7 +274,7 @@ task and worker history.  You can inspect the result and traceback of tasks,
 and it also supports some management commands like rate limiting and shutting
 down workers.
 
-.. image:: http://celeryproject.org/img/celeryevshotsm.jpg
+.. figure:: ../images/celeryevshotsm.jpg
 
 
 :mod:`~celery.bin.celeryev` is also used to start snapshot cameras (see
@@ -412,7 +412,7 @@ write it to a database, send it by e-mail or something else entirely.
 
 :program:`celeryev` is then used to take snapshots with the camera,
 for example if you want to capture state every 2 seconds using the
-camera ``myapp.Camera`` you run :pogram:`celeryev` with the following
+camera ``myapp.Camera`` you run :program:`celeryev` with the following
 arguments::
 
     $ celeryev -c myapp.Camera --frequency=2.0

docs/userguide/remote-tasks.rst

@@ -112,4 +112,4 @@ Since applying tasks can be done via HTTP using the
 ``djcelery.views.apply`` view, executing tasks from other languages is easy.
 For an example service exposing tasks via HTTP you should have a look at
 ``examples/celery_http_gateway`` in the Celery distribution:
-    http://github.com/ask/celery/tree/master/examples/celery_http_gateway/
+http://github.com/ask/celery/tree/master/examples/celery_http_gateway/

docs/userguide/routing.rst

@@ -546,7 +546,7 @@ Router classes can also be added by name::
 
 
 For simple task name -> route mappings like the router example above, you can simply
-drop a dict into :setting:`CELERY_ROUTES` to get the same behavior::
+drop a dict into :setting:`CELERY_ROUTES` to get the same behavior:
 
 .. code-block:: python