FROM debian:9 LABEL maintainer="Anton Lokhmotov " # Use the Bash shell. SHELL ["/bin/bash", "-c"] # Allow stepping into the Bash shell interactively. ENTRYPOINT ["/bin/bash", "-c"] # Install known system dependencies and immediately clean up to make the image smaller. # CK needs: git, wget, zip. # TF needs: curl. # Install to share with other images: cmake. RUN apt update -y\ && apt install -y apt-utils\ && apt upgrade -y\ && apt install -y\ git wget zip libz-dev\ curl\ cmake\ python3 python3-pip\ vim\ && apt clean # Create non-root user. RUN useradd --create-home --user-group --shell /bin/bash dvdt USER dvdt:dvdt WORKDIR /home/dvdt # Install Collective Knowledge (CK). ENV CK_ROOT=/home/dvdt/CK \ CK_REPOS=/home/dvdt/CK_REPOS \ CK_TOOLS=/home/dvdt/CK_TOOLS \ PATH=${CK_ROOT}/bin:/home/dvdt/.local/bin:${PATH} \ CK_PYTHON=python3 \ CK_CC=gcc \ GIT_USER="dividiti" \ GIT_EMAIL="info@dividiti.com" \ LANG=C.UTF-8 RUN mkdir -p ${CK_ROOT} ${CK_REPOS} ${CK_TOOLS} RUN git config --global user.name ${GIT_USER} && git config --global user.email ${GIT_EMAIL} RUN git clone https://github.com/ctuning/ck.git ${CK_ROOT} RUN cd ${CK_ROOT} \ && ${CK_PYTHON} setup.py install --user \ && ${CK_PYTHON} -c "import ck.kernel as ck; print ('Collective Knowledge v%s' % ck.__version__)" # Pull CK repositories (including ck-env, ck-autotuning and ck-tensorflow). RUN ck pull repo:ck-mlperf # Use generic Linux settings with dummy frequency setting scripts. RUN ck detect platform.os --platform_init_uoa=generic-linux-dummy # Detect C/C++ compiler (gcc). RUN ck detect soft:compiler.gcc --full_path=`which ${CK_CC}` # Install TFLite. RUN ck install package --tags=lib,tensorflow-lite,tensorflow-static,v1.13.1 # Detect Python. RUN ck detect soft:compiler.python --full_path=`which ${CK_PYTHON}` # Install the latest Python package installer (pip). RUN ${CK_PYTHON} -m pip install --ignore-installed pip setuptools --user # Install Python dependencies. RUN ck install package --tags=lib,python-package,numpy # Download the MobileNet TF/TFLite models (non-quantized and quantized). # https://github.com/mlperf/inference/blob/master/v0.5/classification_and_detection/optional_harness_ck/classification/tflite/README.md#mobilenet-non-quantized RUN ck install package --tags=image-classification,model,tf,tflite,mlperf,mobilenet,non-quantized,from-zenodo # https://github.com/mlperf/inference/blob/master/v0.5/classification_and_detection/optional_harness_ck/classification/tflite/README.md#mobilenet-quantized RUN ck install package --tags=image-classification,model,tf,tflite,mlperf,mobilenet,quantized,from-zenodo # Download the ResNet TFLite models (with and without the ArgMax operator). # https://github.com/mlperf/inference/blob/master/v0.5/classification_and_detection/optional_harness_ck/classification/tflite/README.md#resnet RUN ck install package --tags=image-classification,model,tflite,mlperf,resnet,downloaded,with-argmax RUN ck install package --tags=image-classification,model,tflite,mlperf,resnet,downloaded,no-argmax # Download and preprocess the first 500 images of the ImageNet 2012 validation dataset. RUN ck install package --tags=dataset,imagenet,aux RUN ck install package --tags=dataset,imagenet,val,original,min --no_tags=resized # Preprocess using "headless" OpenCV (which doesn't need libsm6, libxext6, libxrender-dev). RUN ck install package --tags=lib,python-package,cv2,opencv-python-headless RUN ck install package --tags=dataset,imagenet,val,preprocessed,using-opencv # Compile the Image Classification TFLite program. RUN ck compile program:image-classification-tflite # Run the Image Classification TFLite program # with the non-quantized MobileNet model twice. CMD ["ck run program:image-classification-tflite \ --dep_add_tags.images=preprocessed,using-opencv \ --dep_add_tags.weights=mobilenet,non-quantized \ --env.CK_BATCH_COUNT=2"]