Upgrade Tensorflow version to 1.13.1

containers/base/Dockerfile

@@ -15,7 +15,7 @@
 # We use different base images for GPU vs CPU Dockerfiles, so we expect
 # that the appropriate image is pulled and tagged locally.
 # CPU should use ubuntu:16.04
-# and GPU uses nvidia/cuda:9.1-cudnn7-devel-ubuntu16.04
+# and GPU uses nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04
 FROM datalab-external-base-image
 MAINTAINER Google Cloud DataLab
 
@@ -120,7 +120,7 @@ RUN echo "deb-src http://ftp.us.debian.org/debian testing main" >> /etc/apt/sour
         google-cloud-monitoring==0.28.0 \
         lime==0.1.1.23 \
         protobuf==3.6.1 \
-        tensorflow==1.8.0 && \
+        tensorflow==1.13.1 && \
     source deactivate && \
 # Clean up before setting up the Python3 env.
     conda clean -tipsy && \
@@ -232,7 +232,7 @@ RUN source activate $PYTHON_3_ENV && \
         google-cloud-monitoring==0.28.0 \
         lime==0.1.1.23 \
         protobuf==3.6.1 \
-        tensorflow==1.8.0
+        tensorflow==1.13.1
 
 ENV LANG en_US.UTF-8
 

containers/base/Dockerfile.gpu

@@ -20,8 +20,8 @@ SHELL ["/bin/bash", "-c"]
 
 # Download and Install GPU specific packages
 RUN source activate py2env && \
-    pip install -U --upgrade-strategy only-if-needed --no-cache-dir tensorflow-gpu==1.8.0 tflearn h5py && \
+    pip install -U --upgrade-strategy only-if-needed --no-cache-dir tensorflow-gpu==1.13.1 tflearn h5py && \
     source deactivate && \
     source activate py3env && \
-    pip install -U --upgrade-strategy only-if-needed --no-cache-dir tensorflow-gpu==1.8.0 tflearn h5py && \
+    pip install -U --upgrade-strategy only-if-needed --no-cache-dir tensorflow-gpu==1.13.1 tflearn h5py && \
     source deactivate

containers/base/build.gpu.sh

@@ -32,11 +32,11 @@ fi
 
 trap 'rm -rf pydatalab' exit
 
-docker pull nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
+docker pull nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04
 # Docker tag flags changed in an incompatible way between versions.
 # The Datalab Jenkins build still uses the old one, so try it both ways.
-if ! $(docker tag -f nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 datalab-external-base-image 2> /dev/null); then
-  docker tag nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 datalab-external-base-image
+if ! $(docker tag -f nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04 datalab-external-base-image 2> /dev/null); then
+  docker tag nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04 datalab-external-base-image
 fi
 docker build ${DOCKER_BUILD_ARGS} -t datalab-core-gpu .
 docker build ${DOCKER_BUILD_ARGS} -f Dockerfile.gpu -t datalab-base-gpu .

containers/base/third_party_licenses.csv

@@ -71,6 +71,7 @@ tensorflow,https://raw.githubusercontent.com/tensorflow/tensorflow/master/LICENS
 tensorboard,https://raw.githubusercontent.com/tensorflow/tensorboard/master/LICENSE,Apache 2.0
 keras,https://raw.githubusercontent.com/keras-team/keras/master/LICENSE,MIT
 Keras-Applications,https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE,MIT
+Keras-Preprocessing,https://raw.githubusercontent.com/keras-team/keras-preprocessing/master/LICENSE,MIT
 jupyter-tensorboard,https://raw.githubusercontent.com/lspvic/jupyter_tensorboard/master/LICENSE,MIT
 torch,https://raw.githubusercontent.com/pytorch/pytorch/master/LICENSE,BSD-3-clause
 torchvision,https://raw.githubusercontent.com/pytorch/vision/master/LICENSE,BSD-3-clause
@@ -168,6 +169,7 @@ scipy,https://raw.githubusercontent.com/scipy/scipy/master/LICENSE.txt,BSD
 tensorflow-tensorboard,https://raw.githubusercontent.com/tensorflow/tensorboard/master/LICENSE,Apache 2.0
 tensorflow-data-validation,https://raw.githubusercontent.com/tensorflow/data-validation/master/LICENSE,Apache 2.0
 tensorflow-metadata,https://raw.githubusercontent.com/tensorflow/metadata/master/LICENSE,Apache 2.0
+tensorflow-estimator,https://raw.githubusercontent.com/tensorflow/estimator/master/LICENSE,Apache 2.0
 defusedxml,https://raw.githubusercontent.com/tiran/defusedxml/master/LICENSE,PSF
 conda,https://raw.githubusercontent.com/conda/conda/master/LICENSE.txt,3-Clause BSD
 packaging,https://raw.githubusercontent.com/pypa/packaging/master/LICENSE,BSD or Apache 2.0

tools/release/cloudbuild.yaml

@@ -81,10 +81,10 @@ steps:
 
 ## Second, we build the GPU base image
 - name: 'gcr.io/cloud-builders/docker'
-  args: ['pull', 'nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04']
+  args: ['pull', 'nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04']
   id:   'pullNvidiaUbuntu'
 - name: 'gcr.io/cloud-builders/docker'
-  args: ['tag', 'nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04', 'datalab-external-base-image']
+  args: ['tag', 'nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04', 'datalab-external-base-image']
   id:   'tagNvidiaUbuntu'
   waitFor: ['buildBase', 'pullNvidiaUbuntu']
 - name: 'gcr.io/cloud-builders/docker'