Dockerfile to reproduce MKL bug in TF 1.6

Dockerfile.devel-cpu-mkl

FROM tensorflow/tensorflow:latest-devel

# Based on https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl
# Original by Clayne Robison<[email protected]>

# These arguments are parameterized. Use --build-args to override.
ARG TF_BRANCH=r1.6

ARG WHL_DIR=/whl

# Set this to 0 to build without MKL
ARG MKL_FLAG=1 


RUN apt-get update && apt-get install -y --no-install-recommends \
        wget \
        && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

RUN pip --no-cache-dir install --upgrade \
        pip setuptools wheel h5py

# 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 \
    TF_NEED_JEMALLOC=0 \
    TF_NEED_GCP=0 \
    TF_NEED_CUDA=0 \
    TF_NEED_HDFS=0 \
    TF_NEED_S3=0 \
    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. 
RUN LD_LIBRARY_PATH=${LD_LIBRARY_PATH} \
    bazel build --config="opt" \
                `if [ "$MKL_FLAG" -eq "1" ]; then echo -n "--config=mkl"; else echo -n ""; fi` \
                --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

WORKDIR /root

RUN wget \
    https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels.h5 \
    -O xception_weights.h5

ENTRYPOINT python -c \
    "import tensorflow as tf; \
    import numpy as np; \
    x = tf.keras.applications.xception.Xception(weights=\"xception_weights.h5\"); \
    x.predict(np.zeros((1, 299, 299, 3)), batch_size=1);"

Tensorflow 1.6

Build with MKL library: docker build -t tf_mkl -f Dockerfile.devel-cpu-mkl .
Build without MKL library: docker build -t tf_nomkl -f Dockerfile.devel-cpu-mkl --build-arg MKL_FLAG=0 .

Run image built with MKL library: docker run tf_mkl
Result:

2018-02-15 15:04:26.610461: F tensorflow/core/kernels/mkl_input_conversion_op.cc:448] Check failed: tf_input.CheckReorderToOpMem( memory::primitive_desc(output_mkl_md, cpu_engine), tensor_out, &net) == true (0 vs. 1)
Aborted (core dumped)

Run image built without MKL library: docker run tf_nomkl
Result: no error