TensorFlow: Add a port mapping option for docker command.

Changes:

* Add port mapping options to `docker run` commands in
  docker_run_gpu.sh and README.md. Docker on Mac needs an extra option
  `-p` to expose 8888 for Jupyter Notebook. The added option makes the
  behavior of containers consistent on both Ubuntu and Mac.

Change-Id: I29002329f08d7dc05415925e9b2aedbd3f112813

tensorflow/tools/docker/README.md

@@ -31,7 +31,7 @@ We currently maintain three Docker container images:
 Each of the containers is published to a Docker registry; for the non-GPU
 containers, running is as simple as
 
-    $ docker run -it b.gcr.io/tensorflow/tensorflow
+    $ docker run -it -p 8888:8888 b.gcr.io/tensorflow/tensorflow
 
 For the container with GPU support, we require the user to make the appropriate
 NVidia libraries available on their system, as well as providing mappings so
@@ -40,7 +40,7 @@ accomplished via
 
     $ export CUDA_SO=$(\ls /usr/lib/x86_64-linux-gnu/libcuda* | xargs -I{} echo '-v {}:{}')
     $ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
-    $ docker run -it $CUDA_SO $DEVICES b.gcr.io/tensorflow/tensorflow-devel-gpu
+    $ docker run -it -p 8888:8888 $CUDA_SO $DEVICES b.gcr.io/tensorflow/tensorflow-devel-gpu
 
 Alternately, you can use the `docker_run_gpu.sh` script in this directory.
 

tensorflow/tools/docker/docker_run_gpu.sh

@@ -34,4 +34,4 @@ if [[ "${DEVICES}" = "" ]]; then
   exit 1
 fi
 
-docker run -it $CUDA_SO $DEVICES b.gcr.io/tensorflow/tensorflow-full-gpu "$@"
+docker run -it -p 8888:8888 $CUDA_SO $DEVICES b.gcr.io/tensorflow/tensorflow-full-gpu "$@"