diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md
index c9975b7ce8216e..e94b11e4f2fd8f 100644
--- a/tensorflow/tools/docker/README.md
+++ b/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.
 
diff --git a/tensorflow/tools/docker/docker_run_gpu.sh b/tensorflow/tools/docker/docker_run_gpu.sh
index badc98f660473a..d5fe213952196c 100755
--- a/tensorflow/tools/docker/docker_run_gpu.sh
+++ b/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 $DEVICES b.gcr.io/tensorflow/tensorflow-full-gpu "$@"