Allow is_initialized and initializer to be called on MirroredVariables and TowerLocalVariables.

PiperOrigin-RevId: 203520287

2 files changed, 198 insertions, 0 deletions

diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl
new file mode 100644
index 0000000000..6796ad70e5
--- /dev/null
+++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl
@@ -0,0 +1,83 @@
+FROM tensorflow/tensorflow:latest-devel
+
+LABEL maintainer="Clayne Robison<clayne.b.robison@intel.com>"
+
+# These arguments are parameterized. Use --build-args to override.
+ARG TF_BRANCH=r1.9
+ARG WHL_DIR=/whl
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        golang \
+        vim \
+        emacs \
+        && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip --no-cache-dir install --upgrade \
+        pip setuptools
+
+RUN pip --no-cache-dir install wheel 
+
+# Download and build TensorFlow.
+WORKDIR /
+RUN rm -rf tensorflow && \
+    git clone https://github.com/tensorflow/tensorflow.git && \
+    cd tensorflow && \
+    git checkout ${TF_BRANCH}
+WORKDIR /tensorflow
+
+# Configure the build for CPU with MKL by accepting default build options and
+# setting library locations
+ENV CI_BUILD_PYTHON=python \
+   LD_LIBRARY_PATH=${LD_LIBRARY_PATH} \
+    PYTHON_BIN_PATH=/usr/bin/python \
+    PYTHON_LIB_PATH=/usr/local/lib/python2.7/dist-packages \
+    CC_OPT_FLAGS='-march=native' \
+    TF_NEED_JEMALLOC=0 \
+    TF_NEED_GCP=1 \
+    TF_NEED_CUDA=0 \
+    TF_NEED_HDFS=0 \
+    TF_NEED_S3=1 \
+    TF_NEED_OPENCL=0 \
+    TF_NEED_GDR=0 \
+    TF_ENABLE_XLA=0 \
+    TF_NEED_VERBS=0 \
+    TF_NEED_MPI=0
+RUN ./configure
+
+# Build and Install TensorFlow.
+# The 'mkl' option builds with Intel(R) Math Kernel Library (MKL), which detects
+# the platform it is currently running on and takes appropriately optimized 
+# paths. The -march=native option is for code that is not in MKL, and assumes
+# this container will be run on the same architecture on which it is built.
+RUN LD_LIBRARY_PATH=${LD_LIBRARY_PATH} \
+    bazel build --config=mkl \
+                --config="opt" \
+                --copt="-march=broadwell" \
+                --copt="-O3" \
+                //tensorflow/tools/pip_package:build_pip_package && \
+    mkdir ${WHL_DIR} && \
+    bazel-bin/tensorflow/tools/pip_package/build_pip_package ${WHL_DIR}
+
+# Clean up Bazel cache when done, but leave the whl.
+# This will upgrade the default Tensorflow version with the Intel MKL version
+RUN pip --no-cache-dir install --upgrade ${WHL_DIR}/tensorflow-*.whl && \
+    rm -rf /root/.cache
+
+WORKDIR /root
+
+#add welcome message with instructions
+
+RUN echo '[ ! -z "$TERM" -a -r /etc/motd ] && cat /etc/issue && cat /etc/motd' \
+	>> /etc/bash.bashrc \
+	; echo "\
+||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\
+|								\n\
+| Docker container running Ubuntu				\n\
+| with TensorFlow ${TF_BRANCH} optimized for CPU		\n\
+| with Intel(R) MKL						\n\
+|								\n\
+||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\
+\n "\
+	> /etc/motd
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7 b/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7
new file mode 100644
index 0000000000..3bedc8cf34
--- /dev/null
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7
@@ -0,0 +1,115 @@
+FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
+
+LABEL maintainer="Gunhan Gulsoy <gunan@google.com>"
+
+# It is possible to override these for releases.
+ARG TF_BRANCH=master
+ARG BAZEL_VERSION=0.5.4
+ARG TF_AVAILABLE_CPUS=32
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        curl \
+        git \
+        golang \
+        libcurl3-dev \
+        libfreetype6-dev \
+        libpng12-dev \
+        libzmq3-dev \
+        pkg-config \
+        python-dev \
+        python-pip \
+        rsync \
+        software-properties-common \
+        unzip \
+        zip \
+        zlib1g-dev \
+        openjdk-8-jdk \
+        openjdk-8-jre-headless \
+        wget \
+        && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip --no-cache-dir install --upgrade \
+        pip setuptools
+
+RUN pip --no-cache-dir install \
+        ipykernel \
+        jupyter \
+        matplotlib \
+        numpy \
+        scipy \
+        sklearn \
+        pandas \
+        wheel \
+        && \
+    python -m ipykernel.kernelspec
+
+# Set up our notebook config.
+COPY jupyter_notebook_config.py /root/.jupyter/
+
+# Jupyter has issues with being run directly:
+#   https://github.com/ipython/ipython/issues/7062
+# We just add a little wrapper script.
+COPY run_jupyter.sh /
+
+# Set up Bazel.
+
+# Running bazel inside a `docker build` command causes trouble, cf:
+#   https://github.com/bazelbuild/bazel/issues/134
+# The easiest solution is to set up a bazelrc file forcing --batch.
+RUN echo "startup --batch" >>/etc/bazel.bazelrc
+# Similarly, we need to workaround sandboxing issues:
+#   https://github.com/bazelbuild/bazel/issues/418
+RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
+    >>/etc/bazel.bazelrc
+WORKDIR /
+RUN mkdir /bazel && \
+    cd /bazel && \
+    wget --quiet https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
+    wget --quiet https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \
+    chmod +x bazel-*.sh && \
+    ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
+    rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh
+
+# Download and build TensorFlow.
+WORKDIR /
+RUN git clone https://github.com/tensorflow/tensorflow.git && \
+    cd tensorflow && \
+    git checkout ${TF_BRANCH}
+WORKDIR /tensorflow
+
+# Configure the build for our CUDA configuration.
+ENV CI_BUILD_PYTHON=python \
+    LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:${LD_LIBRARY_PATH} \
+    CUDNN_INSTALL_PATH=/usr/lib/x86_64-linux-gnu \
+    PYTHON_BIN_PATH=/usr/bin/python \
+    PYTHON_LIB_PATH=/usr/local/lib/python2.7/dist-packages \
+    TF_NEED_CUDA=1 \
+    TF_CUDA_VERSION=9.0 \
+    TF_CUDA_COMPUTE_CAPABILITIES=3.0,3.5,5.2,6.0,6.1,7.0 \
+    TF_CUDNN_VERSION=7
+RUN ./configure
+
+# Build and Install TensorFlow.
+RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 && \
+    LD_LIBRARY_PATH=/usr/local/cuda/lib64/stubs:${LD_LIBRARY_PATH} \
+    bazel build -c opt \
+                --config=cuda \
+                --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
+                --jobs=${TF_AVAILABLE_CPUS} \
+                tensorflow/tools/pip_package:build_pip_package && \
+    mkdir /pip_pkg && \
+    bazel-bin/tensorflow/tools/pip_package/build_pip_package /pip_pkg && \
+    pip --no-cache-dir install --upgrade /pip_pkg/tensorflow-*.whl && \
+    rm -rf /pip_pkg && \
+    rm -rf /root/.cache
+# Clean up pip wheel and Bazel cache when done.
+
+WORKDIR /root
+
+# TensorBoard
+EXPOSE 6006
+# IPython
+EXPOSE 8888