switch docker links from b.gcr.io to gcr.io (tensorflow#1911)

tensorflow/examples/udacity/Dockerfile

@@ -1,4 +1,4 @@
-FROM b.gcr.io/tensorflow/tensorflow:latest
+FROM gcr.io/tensorflow/tensorflow:latest
 MAINTAINER Vincent Vanhoucke <[email protected]>
 RUN pip install scikit-learn
 RUN rm -rf /notebooks/*

tensorflow/g3doc/get_started/os_setup.md

@@ -184,11 +184,11 @@ packages on your machine.
 
 We provide 4 Docker images:
 
-* `b.gcr.io/tensorflow/tensorflow`: TensorFlow CPU binary image.
-* `b.gcr.io/tensorflow/tensorflow:latest-devel`: CPU Binary image plus source
+* `gcr.io/tensorflow/tensorflow`: TensorFlow CPU binary image.
+* `gcr.io/tensorflow/tensorflow:latest-devel`: CPU Binary image plus source
 code.
-* `b.gcr.io/tensorflow/tensorflow:latest-gpu`: TensorFlow GPU binary image.
-* `b.gcr.io/tensorflow/tensorflow:latest-devel-gpu`: GPU Binary image plus source
+* `gcr.io/tensorflow/tensorflow:latest-gpu`: TensorFlow GPU binary image.
+* `gcr.io/tensorflow/tensorflow:latest-devel-gpu`: GPU Binary image plus source
 code.
 
 We also have tags with `latest` replaced by a released version (e.g., `0.8.0rc0-gpu`).
@@ -209,7 +209,7 @@ After Docker is installed, launch a Docker container with the TensorFlow binary
 image as follows.
 
 ```bash
-$ docker run -it b.gcr.io/tensorflow/tensorflow
+$ docker run -it gcr.io/tensorflow/tensorflow
 ```
 
 If you're using a container with GPU support, some additional flags must be
@@ -219,7 +219,7 @@ include a
 in the repo with these flags, so the command-line would look like
 
 ```bash
-$ path/to/repo/tensorflow/tools/docker/docker_run_gpu.sh b.gcr.io/tensorflow/tensorflow:gpu
+$ path/to/repo/tensorflow/tools/docker/docker_run_gpu.sh gcr.io/tensorflow/tensorflow:gpu
 ```
 
 You can now [test your installation](#test-the-tensorflow-installation) within the Docker container.

tensorflow/tools/docker/README.md

@@ -16,22 +16,22 @@ quick links here:
 
 We currently maintain three Docker container images:
 
-* `b.gcr.io/tensorflow/tensorflow`, which is a minimal VM with TensorFlow and
+* `gcr.io/tensorflow/tensorflow`, which is a minimal VM with TensorFlow and
   all dependencies.
 
-* `b.gcr.io/tensorflow/tensorflow-full`, which contains a full source
+* `gcr.io/tensorflow/tensorflow-full`, which contains a full source
   distribution and all required libraries to build and run TensorFlow from
   source.
 
-* `b.gcr.io/tensorflow/tensorflow-full-gpu`, which is the same as the previous
+* `gcr.io/tensorflow/tensorflow-full-gpu`, which is the same as the previous
   container, but built with GPU support.
 
 ## Running the container
 
 Each of the containers is published to a Docker registry; for the non-GPU
 containers, running is as simple as
 
-    $ docker run -it -p 8888:8888 b.gcr.io/tensorflow/tensorflow
+    $ docker run -it -p 8888:8888 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 -p 8888:8888 $CUDA_SO $DEVICES b.gcr.io/tensorflow/tensorflow-devel-gpu
+    $ docker run -it -p 8888:8888 $CUDA_SO $DEVICES gcr.io/tensorflow/tensorflow-devel-gpu
 
 Alternately, you can use the `docker_run_gpu.sh` script in this directory.