Document the config flag dnn.conv.workmem

doc/library/config.txt

@@ -484,6 +484,25 @@ import theano and print the config variable, as in:
 
     A directory with bin/, lib/, include/ folders containing cuda utilities.
 
+.. attribute:: config.dnn.conv.workmem
+
+   String value: 'none', 'small', 'large'
+
+   Default: 'small'
+
+   The default value for the amount of working memory that is
+   tolerated in the convolution implementation in cudnn.
+
+   'none'
+     Don't allow any extra memory.
+
+   'small'
+     Allow extra memory that is much smaller than the input sizes.
+
+   'large'
+     Allow extra memory that is on the order of the input sizes.
+
+
 .. attribute:: config.gcc.cxxflags
 
     Default: ""

theano/sandbox/cuda/dnn.py

@@ -346,7 +346,8 @@ class GpuDnnConv(DnnBase, COp):
 
     def __init__(self, workmem=None):
         """
-        :param workmem: either 'none', 'small' or 'large'.  Default is 'small'.
+        :param workmem: either 'none', 'small' or 'large'.  Default is
+        the value of :attr:`config.dnn.conv.workmem`.
         """
         COp.__init__(self, ["dnn_base.c", "dnn_conv_base.c", "dnn_fwd.c"],
                      "APPLY_SPECIFIC(conv_fwd)")
@@ -587,6 +588,8 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
     :warning: The cuDNN library only works with GPU that have a compute
       capability of 3.0 or higer.  This means that older GPU will not
       work with this Op.
+    :note: The working memory of the op is influenced by
+      :attr:`config.dnn.conv.workmem`.
     """
     fgraph = getattr(img, 'fgraph', None) or getattr(kerns, 'fgraph', None)
     if (border_mode == 'valid' and subsample == (1,1) and