Fixes from review.

doc/tutorial/using_multi_gpu.txt

 
-.. _using_multi_gpu:
+.. _tut_using_multi_gpu:
 
 ===================
 Using multiple GPUs
@@ -38,10 +38,20 @@ number of such mappings, but in the example above we have two of them:
 
 The mappings themselves are composed of a context name followed by the
 two characters '->' and the device name.  The context name is a simple
-string which oes not have any special meaning for Theano.  For parsing
-reasons, the context name cannot contain the sequence '->'.  The
-device name is a device in the form that gpuarray expects like 'cuda0'
-or 'opencl0:0'.
+string which does not have any special meaning for Theano.  For
+parsing reasons, the context name cannot contain the sequence '->' or
+';'.  To avoid confusion context names that begin with 'cuda' or
+'opencl' as disallowed.  The device name is a device in the form that
+gpuarray expects like 'cuda0' or 'opencl0:0'.
+
+.. note::
+
+   Since there are a bunch of shell special characters in the syntax,
+   defining this on the command-line will require proper quoting, like this:
+
+   .. code-block:: shell
+
+       $ THEANO_FLAGS="contexts=dev0->cuda0"
 
 If you don't have enough GPUs for a certain model, you can assign the
 same device to more than one name. You can also assign extra names
@@ -72,13 +82,13 @@ which perform two dot products on two different GPUs.
    import numpy
    import theano
 
-   v01 = theano.shared(numpy.random.random(1024, 1024).astype('float32'),
+   v01 = theano.shared(numpy.random.random((1024, 1024)).astype('float32'),
                        target='dev0')
-   v02 = theano.shared(numpy.random.random(1024, 1024).astype('float32'),
+   v02 = theano.shared(numpy.random.random((1024, 1024)).astype('float32'),
                        target='dev0')
-   v11 = theano.shared(numpy.random.random(1024, 1024).astype('float32'),
+   v11 = theano.shared(numpy.random.random((1024, 1024)).astype('float32'),
                        target='dev1')
-   v12 = theano.shared(numpy.random.random(1024, 1024).astype('float32'),
+   v12 = theano.shared(numpy.random.random((1024, 1024)).astype('float32'),
                        target='dev1')
 
    f = theano.function([], [theano.tensor.dot(v01, v02),