AI-Engineer-Note

A collection for AI Engineer & Deploy Services

• Deeplearning

  • 1. ComputerVision
    • 1.1 Common Architectures
      • 1.1.1 ResBlock
      • 1.1.2 Gated Convolution
      • 1.1.3 Multi-head Attention
  • 2. NLP

• Frameworks

  • 1. TensorRT
    • 1.1 Convert ONNX model to TensorRT
    • 1.2 Wrapped TensorRT-CPP Models
      • 1.2.1 Arcface
      • 1.2.2 SCRFD
      • 1.2.3 YOLOv7
  • 2. Pytorch
    • 2.1 Build Pytorch from source (Optimize speed for AMD CPU & NVIDIA GPU)

• Deploy

  • 1. NVIDIA
    • 1.1 Multi-instance GPU (MIG)
    • 1.2 FFMPEG with Nvidia hardware-acceleration
  • 2. Deepstream
    • 2.1 Yolov4
    • 2.2 Traffic Analyst
    • 2.3 SCRFD Face Detection (custom parser & NMS plugin with landmark)
  • 3. Triton Inference Server
    • 3.1 Cài đặt triton-server và triton-client
      • 3.1.1 Các chế độ quản lý model (load/unload/reload)
    • 3.2 Sơ lược về các backend trong Triton
    • 3.3 Cấu hình cơ bản khi deploy mô hình
    • 3.4 Deploy mô hình
      • 3.4.1 ONNX-runtime
      • 3.4.2 TensorRT
      • 3.4.3 Pytorch & TorchScript
      • 3.4.4 Kaldi (Advanced)
    • 3.5 Model Batching
    • 3.6 Ensemble Model và pre/post processing
    • 3.7 Sử dụng Performance Analyzer Tool
    • 3.8 Optimizations
      • 3.8.1 Tối ưu Pytorch backend
  • 4. TAO Toolkit (Transfer-Learning-Toolkit)

• Linux & CUDA & APT-Packages

  • Build OpenCV from source

    • Build OpenCV from source
  • Install Math Kernel Library (MKL/BLAS/LAPACK/OPENBLAS)

    You are recommended to install all Math Kernel Library and then compile framework (e.g pytorch, mxnet) from source using custom config for optimization. Install all LAPACK+BLAS:

    sudo apt install libjpeg-dev libpng-dev libblas-dev libopenblas-dev libatlas-base-dev liblapack-dev liblapacke-dev gfortran 
    

    Install MKL:

    # Get the key
    wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
    # now install that key
    apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
    # now remove the public key file exit the root shell
    rm GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
    # Add to apt
    sudo wget https://apt.repos.intel.com/setup/intelproducts.list -O /etc/apt/sources.list.d/intelproducts.list
    sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
    # Install
    sudo apt-get update
    sudo apt-get install intel-mkl-2020.4-912
    

  • Fresh install NVIDIA driver (PC/Laptop/Workstation)

    # Remove old packages
    sudo apt-get remove --purge '^nvidia-.*'
    sudo apt-get install ubuntu-desktop
    sudo apt-get --purge remove "*cublas*" "cuda*"
    sudo apt-get --purge remove "*nvidia*"
    sudo add-apt-repository --remove ppa:graphics-drivers/ppa
    sudo rm /etc/X11/xorg.conf
    sudo apt autoremove
    sudo reboot
    
    # After restart
    sudo ubuntu-drivers devices
    sudo ubuntu-drivers autoinstall
    sudo reboot
    

  • Install CuDNN

    Install keyring: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/#network-repo-installation-for-ubuntu Install CuDNN9 with CUDA 11

    sudo apt-get update
    sudo apt-get -y install cudnn9-cuda-11
    

  • NVIDIA-SMI has failed because it couldn’t communicate with the NVIDIA driver

    First, make sure that you have "Fresh install NVIDIA driver". If not work, try this bellow

    • Make sure the package nvidia-prime is installed:

    sudo apt install nvidia-prime
    

    Afterwards, run

    sudo prime-select nvidia
    

    • Make sure that NVIDIA is not in blacklist

    grep nvidia /etc/modprobe.d/* /lib/modprobe.d/*
    

    to find a file containing blacklist nvidia and remove it, then run

    sudo update-initramfs -u
    

    Get boot log

    journalctl -b | grep NVIDIA
    

    • If get error This PCI I/O region assigned to your NVIDIA device is invalid:

    sudo nano /etc/default/grub
    

    edit GRUB_CMDLINE_LINUX_DEFAULT="quiet splash pci=realloc=off"

    sudo update-grub
    sudo reboot
    

  • Check current CUDA version

    nvcc --version
    

  • Check current supported CUDA versions

    ls /usr/local/
    

  • Select GPU devices

    CUDA_VISIBLE_DEVICES=<index-of-devices> <command>
    CUDA_VISIBLE_DEVICES=0 python abc.py
    CUDA_VISIBLE_DEVICES=0 ./sample.sh
    CUDA_VISIBLE_DEVICES=0,1,2,3 python abc.py
    CUDA_VISIBLE_DEVICES=0,1,2,3 ./sample.sh
    

  • Switch CUDA version

    CUDA_VER=11.3
    export PATH="/usr/local/cuda-$CUDA_VER/bin:$PATH"
    export LD_LIBRARY_PATH=/usr/local/cuda-$CUDA_VER/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
    

  • Check NVENV/NVDEC status

    nvidia-smi dmon
    

    see the tab %enc and %dec

  • Error with distributed training NCCL (got freezed)

    export NCCL_P2P_DISABLE="1"
    

  • Broken pipe (Distributed training with NCCL)

    Run training with args

    NCCL_DEBUG=INFO TORCH_CPP_LOG_LEVEL=INFO TORCH_DISTRIBUTED_DEBUG=INFO torchrun ...
    

    to gather socket name (e.g eno1)

    NCCL INFO NET/IB : No device found.
    rnd3:77634:79720 [0] NCCL INFO NET/Socket : Using [0]eno1:10.9.3.241<0>
    rnd3:77634:79720 [0] NCCL INFO Using network Socket
    

    In other nodes, run with arg

    NCCL_SOCKET_IFNAME=eno1    
    

  • Install CMake from source

    version=3.23
    build=2 ## don't modify from here
    mkdir ~/temp
    cd ~/temp
    wget https://cmake.org/files/v$version/cmake-$version.$build.tar.gz
    tar -xzvf cmake-$version.$build.tar.gz
    cd cmake-$version.$build/
    ./bootstrap
    make -j8
    sudo make install
    

  • Install NCCL Backend (Distributed training)

    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb
    sudo dpkg -i cuda-keyring_1.0-1_all.deb
    sudo apt-get update
    sudo apt install libnccl2 libnccl-dev
    

  • Install MXNet from source

    git clone --recursive --branch 1.9.1 https://github.com/apache/incubator-mxnet.git mxnet
    cd mxnet
    cp config/linux_gpu.cmake config.cmake
    rm -rf build
    mkdir -p build && cd build
    cmake -DUSE_CUDA=ON -DUSE_CUDNN=OFF -DUSE_MKL_IF_AVAILABLE=OFF -DUSE_MKLDNN=OFF -DUSE_OPENMP=OFF -DUSE_OPENCV=ON -DUSE_BLAS=open ..
    make -j32
    cd ../python
    pip install --user -e .
    

  • Tensorflow could not load dynamic library 'cudart64_101.dll'

    For above example tensorflow would require CUDA 10.1, please switch to CUDA 10.1 or change tensorflow version which compatible with CUDA version, check here: https://www.tensorflow.org/install/source#gpu
  • Fix Deepstream (6.2+) FFMPEG OpenCV installation

    Fix some errors about undefined reference & not found of libavcodec, libavutil, libvpx, ...

    apt-get install --reinstall --no-install-recommends -y libavcodec58 libavcodec-dev libavformat58 libavformat-dev libavutil56 libavutil-dev gstreamer1.0-libav
    apt install --reinstall gstreamer1.0-plugins-good
    apt install --reinstall libvpx6 libx264-155 libx265-179 libmpg123-0 libmpeg2-4 libmpeg2encpp-2.1-0
    gst-inspect-1.0 | grep 264
    rm ~/.cache/gstreamer-1.0/registry.x86_64.bin
    apt install --reinstall libx264-155
    apt-get install gstreamer1.0-libav
    apt-get install --reinstall gstreamer1.0-plugins-ugly
    

  • Gstreamer pipeline to convert MP4-MP4 with re-encoding

    gst-launch-1.0 filesrc location="<path-to-input>" ! qtdemux ! video/x-h264 ! h264parse ! avdec_h264 ! videoconvert ! x264enc ! h264parse ! qtmux ! filesink location=<path-to-output>
    

  • Gstreamer pipeline to convert RTSP-RTMP

    gst-launch-1.0 rtspsrc location='rtsp://<path-to-rtsp-input>' ! rtph264depay ! h264parse ! flvmux ! rtmpsink location='rtmp://rtmp://<path-to-rtmp-output>'
    

  • Gstreamer pipeline to convert RTSP-RTMP with reducing resolution

    gst-launch-1.0 rtspsrc location='rtsp://<path-to-rtsp-input>' ! rtpbin ! rtph264depay ! h264parse ! avdec_h264 ! videoconvert ! videoscale ! video/x-raw,width=640,height=640 ! x264enc ! h264parse ! flvmux streamable=true ! rtmpsink location='rtmp://<path-to-rtmp-output>'