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Code release for Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. If you find this code useful, please cite us!

@article{kang2021jointly,
  title={Jointly optimizing preprocessing and inference for DNN-based visual analytics},
  author={Kang, Daniel and Mathur, Ankit and Veeramacheneni, Teja and Bailis, Peter and Zaharia, Matei},
  journal={PVLDB},
  year={2021}
}

Setup

Start with a g4dn.xlarge AWS instance, using the following AMI: Ubuntu Server 18.04 LTS (HVM), SSD Volume Type

Set the SSD memory to be around 100GB (you can adjust this based on your specific needs).

Step 1: Install CUDA and associated libraries

Follow the instructions here: https://ddkang.github.io/2020/01/02/installing-tensorrt.html

Step 2: Install extra libraries

You will need to install:

conda (for the latest CMake)
FFMpeg
OpenCV (+ contrib)
folly
onnx
yaml-cpp

You may need to add these lines to your bashrc:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib/
export LD_RUN_PATH=$LD_RUN_PATH:/usr/local/lib/
export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/usr/local/lib/

For all the cmake commands, you can install locally by adding cmake -DCMAKE_INSTALL_PREFIX=..

conda

wget https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh
bash Anaconda3-2019.10-Linux-x86_64.sh

conda create -n dev anaconda
conda install -c anaconda cmake

You will need to supervise this install. You can avoid this step by manually installing the latest CMake.

FFmpeg

sudo apt install yasm

git clone https://github.com/FFmpeg/FFmpeg.git
cd FFmpeg
git checkout 4c07985
mkdir build; cd build
../configure --enable-gpl --enable-postproc --enable-pic --enable-shared
make -j$(nproc)
sudo make install -j$(nproc)

Add --prefix=... to configure to install locally.

OpenCV

Note: do not run this in the conda environment. You can alternatively install in conda and set the appropriate paths to the conda install.

sudo apt install build-essential
sudo apt install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libdc1394-22-dev
sudo apt install libtiff5-dev
sudo apt install libx264-dev

git clone https://github.com/opencv/opencv.git
git clone https://github.com/opencv/opencv_contrib.git

cd opencv
mkdir build ; cd build
cmake -D WITH_FFMPEG=OFF -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules ..
make -j$(nproc)
sudo make install -j$(nproc)

folly

You need to install fmt from source:

git clone https://github.com/fmtlib/fmt.git && cd fmt
mkdir _build && cd _build
cmake ..

make -j$(nproc)
sudo make install -j$(nproc)

sudo apt install libgoogle-glog-dev
sudo apt-get install \
    g++ \
    cmake \
    libboost-all-dev \
    libevent-dev \
    libdouble-conversion-dev \
    libgoogle-glog-dev \
    libgflags-dev \
    libiberty-dev \
    liblz4-dev \
    liblzma-dev \
    libsnappy-dev \
    make \
    zlib1g-dev \
    binutils-dev \
    libjemalloc-dev \
    libssl-dev \
    pkg-config \
    libunwind-dev
sudo apt-get install \
    libunwind8-dev \
    libelf-dev \
    libdwarf-dev


git clone https://github.com/facebook/folly.git
cd folly
git checkout efaea2394de97a4cbcc6e504ae8eb315a4e4aed4
mkdir _build && cd _build
cmake ..
make -j$(nproc)
sudo make install -j$(nproc)

Later versions of folly do not work.

onnx

Ensure this is executed in the conda environment.

sudo apt-get update
sudo apt install libprotobuf-dev protobuf-compiler cmake -y

git clone --recursive https://github.com/onnx/onnx.git
cd onnx
mkdir build ; cd build
cmake ..
make -j$(nproc)
sudo make install -j$(nproc)

yaml-cpp

Ensure this is in the conda environment.

git clone https://github.com/jbeder/yaml-cpp.git
cd yaml-cpp
mkdir build ; cd build
cmake -DBUILD_SHARED_LIBS=ON ..
make -j$(nproc)
sudo make install -j$(nproc)

libjpeg-turbo

Download the following file: https://sourceforge.net/projects/libjpeg-turbo/files/2.0.3/libjpeg-turbo-official_2.0.3_amd64.deb/download

SCP the deb file to the remote machine.

sudo dpkg -i libjpeg-turbo-official_2.0.3_amd64.deb
sudo apt-get install -f

Add the following to your .bashrc

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/libjpeg-turbo/lib64/
export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/opt/libjpeg-turbo/include/

Cloning the code

Clone and build the repository:

git clone https://github.com/stanford-futuredata/image-serving.git
cd e2e/trt
mkdir build ; cd build
cmake ..
make -j$(nproc)

You may need to specify the yaml-cpp library and the OpenCV version.

Get the Imagenet val dataset onto your remote machine. Use the following script to organize it into a format that the InferenceServer will be able to understand:

wget https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh

Setting up a Preprocessing Configuration

Fields

See im-single-full-base.yaml in cfgs as an example:

model-config: Contains information of the models.
experiment-type: Whether or not to do full or infer-only.
experiment-config: Other experimental configuration, including loading, inference, writing out predictions, and the multiplier.
criterion: For filtering.
infer-config: Whether or not to do memcpy (do-memcpy).

The video config is similar.

Execution

Create a YAML configuration file by filling out the above fields (a sample configuration is included). Run:

./runner <path to config file>

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