update supported tf versions on cloud.

research/object_detection/g3doc/running_on_cloud.md

@@ -42,7 +42,7 @@ job using GPUs. A sample YAML file is given below:
 
 ```
 trainingInput:
-  runtimeVersion: "1.0"
+  runtimeVersion: "1.2"
   scaleTier: CUSTOM
   masterType: standard_gpu
   workerCount: 9
@@ -71,6 +71,7 @@ following command:
 ``` bash
 # From tensorflow/models/research/
 gcloud ml-engine jobs submit training object_detection_`date +%s` \
+    --runtime-version 1.2 \
     --job-dir=gs://${TRAIN_DIR} \
     --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \
     --module-name object_detection.train \
@@ -90,6 +91,8 @@ Google Cloud Storage.
 Users can monitor the progress of their training job on the [ML Engine
 Dashboard](https://console.cloud.google.com/mlengine/jobs).
 
+Note: This sample is supported for use with 1.2 runtime version.
+
 ## Running an Evaluation Job on Cloud
 
 Evaluation jobs run on a single machine, so it is not necessary to write a YAML
@@ -98,6 +101,7 @@ job:
 
 ``` bash
 gcloud ml-engine jobs submit training object_detection_eval_`date +%s` \
+    --runtime-version 1.2 \
     --job-dir=gs://${TRAIN_DIR} \
     --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \
     --module-name object_detection.eval \

research/object_detection/g3doc/running_pets.md

@@ -203,12 +203,15 @@ For running the training Cloud ML job, we'll configure the cluster to use 10
 training jobs (1 master + 9 workers) and three parameters servers. The
 configuration file can be found at `object_detection/samples/cloud/cloud.yml`.
 
+Note: This sample is supported for use with 1.2 runtime version.
+
 To start training, execute the following command from the
 `tensorflow/models/research/` directory:
 
 ``` bash
 # From tensorflow/models/research/
 gcloud ml-engine jobs submit training `whoami`_object_detection_`date +%s` \
+    --runtime-version 1.2 \
     --job-dir=gs://${YOUR_GCS_BUCKET}/train \
     --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \
     --module-name object_detection.train \
@@ -224,6 +227,7 @@ Once training has started, we can run an evaluation concurrently:
 ``` bash
 # From tensorflow/models/research/
 gcloud ml-engine jobs submit training `whoami`_object_detection_eval_`date +%s` \
+    --runtime-version 1.2 \
     --job-dir=gs://${YOUR_GCS_BUCKET}/train \
     --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \
     --module-name object_detection.eval \