Added comments about using multiple GPUs

examples/dnn_mnist_advanced_ex.cpp

@@ -4,6 +4,7 @@
     dlib C++ Library.  I'm assuming you have already read the dnn_mnist_ex.cpp
     example.  So in this example program I'm going to go over a number of more
     advanced parts of the API, including:
+        - Using multiple GPUs
         - Training on large datasets that don't fit in memory 
         - Defining large networks
         - Accessing and configuring layers in a network
@@ -201,7 +202,13 @@ int main(int argc, char** argv) try
 
     // The dnn_trainer will use SGD by default, but you can tell it to use
     // different solvers like adam.  
-    dnn_trainer<net_type,adam> trainer(net,adam(0.001));
+    //dnn_trainer<net_type,adam> trainer(net,adam(0.001));
+    // Also, if you have multiple graphics cards you can tell the trainer to use
+    // them together to make the training faster.  For example, replacing the
+    // above constructor call with this one would cause it to use GPU cards 0
+    // and 1.
+    dnn_trainer<net_type,adam> trainer(net,adam(0.001), {0,1});
+
     trainer.be_verbose();
     trainer.set_synchronization_file("mnist_resnet_sync", std::chrono::seconds(100));
     // While the trainer is running it keeps an eye on the training error.  If