cleup profileMode deprecation in docs

doc/cifarSC2011/theano.txt

@@ -176,7 +176,7 @@ Theano flags
 
 Theano can be configured with flags. They can be defined in two ways
 
-* With an environment variable: ``THEANO_FLAGS="mode=ProfileMode,ProfileMode.profile_memory=True"``
+* With an environment variable: ``THEANO_FLAGS="profile=True,profile_memory=True"``
 
 * With a configuration file that defaults to ``~/.theanorc``
 

doc/crei2013/advanced_theano.txt

@@ -104,7 +104,7 @@ Exercise 5
 -----------
 
 - In the last exercises, do you see a speed up with the GPU?
-- Where does it come from? (Use ProfileMode)
+- Where does it come from? (Use profile=True)
 - Is there something we can do to speed up the GPU version?
 
 

doc/crei2013/theano.txt

@@ -133,7 +133,7 @@ Theano flags
 
 Theano can be configured with flags. They can be defined in two ways
 
-* With an environment variable: ``THEANO_FLAGS="mode=ProfileMode,ProfileMode.profile_memory=True"``
+* With an environment variable: ``THEANO_FLAGS="profile=True,profile_memory=True"``
 
 * With a configuration file that defaults to ``~/.theanorc``
 

doc/library/compile/mode.txt

@@ -23,7 +23,7 @@ Theano defines the following modes by name:
 - ``'DebugMode'``: A mode for debugging. See :ref:`DebugMode <debugmode>` for details.
 - ``'ProfileMode'``: Deprecated, use the Theano flag :attr:`config.profile`.
 - ``'DEBUG_MODE'``: Deprecated. Use the string DebugMode.
-- ``'PROFILE_MODE'``: Deprecated. Use the string ProfileMode.
+- ``'PROFILE_MODE'``: Deprecated, use the Theano flag :attr:`config.profile`.
 
 The default mode is typically ``FAST_RUN``, but it can be controlled via the
 configuration variable :attr:`config.mode`, which can be
@@ -70,4 +70,3 @@ Reference
 
         Return a new Mode instance like this one, but with an
         optimizer modified by requiring the given tags.
-

doc/tutorial/profiling.txt

@@ -68,6 +68,15 @@ compare equal, if their parameters differ (the scalar being
 executed). So the class section will merge more Apply nodes then the
 Ops section.
 
+Note that the profile also shows which Ops were running a c implementation.
+
+Developers wishing to optimize the performance of their graph should
+focus on the worst offending Ops and Apply nodes – either by optimizing
+an implementation, providing a missing C implementation, or by writing
+a graph optimization that eliminates the offending Op altogether.
+You should strongly consider emailing one of our lists about your
+issue before spending too much time on this.
+
 Here is an example output when we disable some Theano optimizations to
 give you a better idea of the difference between sections. With all
 optimizations enabled, there would be only one op left in the graph.

doc/tutorial/using_gpu.txt

@@ -213,8 +213,8 @@ Tips for Improving Performance on GPU
   frequently-accessed data (see :func:`shared()<shared.shared>`).  When using
   the GPU, *float32* tensor ``shared`` variables are stored on the GPU by default to
   eliminate transfer time for GPU ops using those variables.
-* If you aren't happy with the performance you see, try building your functions with
-  ``mode='ProfileMode'``. This should print some timing information at program
+* If you aren't happy with the performance you see, try running your script with
+  ``profil=True`` flag. This should print some timing information at program
   termination. Is time being used sensibly?   If an op or Apply is
   taking more time than its share, then if you know something about GPU
   programming, have a look at how it's implemented in theano.sandbox.cuda.
@@ -339,7 +339,7 @@ to the exercise in section :ref:`Configuration Settings and Compiling Mode<using
 
 Is there an increase in speed from CPU to GPU?
 
-Where does it come from? (Use ``ProfileMode``)
+Where does it come from? (Use ``profile=True`` flag.)
 
 What can be done to further increase the speed of the GPU version? Put your ideas to test.