AlexanderNevarko
commented
7 months ago Docker file
# syntax=docker/dockerfile:1
# Does not work because of RTSP connection
# FROM nvcr.io/nvidia/pytorch:23.09-py3 AS base
# Variables used at build time.
## Base CUDA version. See all supported version at https://hub.docker.com/r/nvidia/cuda/tags?page=2&name=-devel-ubuntu
ARG CUDA_VERSION=11.8.0
## Base Ubuntu version.
ARG OS_VERSION=22.04
# Define base image.
FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu${OS_VERSION} AS base
# Dublicate args because of the visibility zone
# https://docs.docker.com/engine/reference/builder/#understand-how-arg-and-from-interact
ARG CUDA_VERSION
ARG OS_VERSION
## Base TensorRT version.
ARG TRT_VERSION=8.6.1.6
## Base PyTorch version.
ARG TORCH_VERSION=2.2.0
## Base TorchVision version.
ARG TORCHVISION_VERSION=0.17.0
## Base OpenCV version.
ARG OPENCV_VERSION=4.8.0.74
## Base CMake version.
ARG CMAKE_VERSION=3.26.0
## Base Timezone
ARG TZ=Europe/Moscow
# Set environment variables.
## Set non-interactive to prevent asking for user inputs blocking image creation.
ENV DEBIAN_FRONTEND=noninteractive \
## Set timezone as it is required by some packages.
TZ=${TZ} \
## CUDA Home, required to find CUDA in some packages.
CUDA_HOME="/usr/local/cuda" \
## Set LD_LIBRARY_PATH for local libs (glog etc.)
LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib" \
## Accelerate compilation flags (use all cores)
MAKEFLAGS=-j$(nproc) \
## Torch GPU arch list
TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6 8.9"
RUN apt-get update && \
apt-get install \
--no-install-recommends \
--yes \
# Requirements from tensor stream
build-essential \
yasm \
nasm \
unzip \
git \
wget \
sysstat \
libtcmalloc-minimal4 \
pkgconf \
autoconf \
libtool \
flex \
bison \
libx264-dev \
python3 \
python3-pip \
python3-dev \
python3-setuptools \
# Requirements from ultralytics
libgl1 \
libglib2.0-0 \
gnupg \
libusb-1.0-0 \
# Linux security updates
# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
openssl \
tar \
# Image I/O libs
libjpeg-dev \
libpng-dev \
libtiff-dev \
# Parallelism library C++ for CPU
libtbb-dev \
# Optimization libraries for OpenCV
libatlas-base-dev \
gfortran \
# Video/Audio Libs - FFMPEG, GSTREAMER, x264 and so on.
## AV Lib [does not work with tensor_stream]
# libavcodec-dev \
# libavformat-dev \
# libswscale-dev \
## Gstreamer
libgstreamer1.0-dev \
libgstreamer-plugins-base1.0-dev \
## Others
libxvidcore-dev \
x264 \
libx264-dev && \
## Clean cached files
ln -s /usr/bin/python3 /usr/bin/python && \
apt-get clean --yes && \
apt-get autoremove --yes && \
rm -rf /var/lib/apt/lists/* && \
rm -rf /var/cache/apt/archives/* && \
## Set timezone
ln -snf /usr/share/zoneinfo/${TZ} /etc/localtime && \
echo ${TZ} > /etc/timezone
SHELL ["/bin/bash", "-c"]
# Install CMake
RUN wget https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-Linux-x86_64.sh \
-q -O /tmp/cmake-install.sh \
&& chmod u+x /tmp/cmake-install.sh \
&& mkdir /usr/bin/cmake \
&& /tmp/cmake-install.sh --skip-license --prefix=/usr/bin/cmake \
&& rm /tmp/cmake-install.sh
ENV PATH="/usr/bin/cmake/bin:${PATH}"
# Install TensorRT
## Now only supported for Ubuntu 22.04
## Cannot install via pip because cuda-based errors
RUN v="${TRT_VERSION}-1+cuda${CUDA_VERSION%.*}" distro="ubuntu${OS_VERSION//./}" arch=$(uname -m) && \
wget https://developer.download.nvidia.com/compute/cuda/repos/${distro}/${arch}/cuda-archive-keyring.gpg && \
mv cuda-archive-keyring.gpg /usr/share/keyrings/cuda-archive-keyring.gpg && \
echo "deb [signed-by=/usr/share/keyrings/cuda-archive-keyring.gpg] https://developer.download.nvidia.com/compute/cuda/repos/${distro}/${arch}/ /" | \
tee /etc/apt/sources.list.d/cuda-${distro}-${arch}.list && \
apt-get update && \
apt-get install \
libnvinfer-headers-dev=${v} \
libnvinfer-dispatch8=${v} \
libnvinfer-lean8=${v} \
libnvinfer-dev=${v} \
libnvinfer-headers-plugin-dev=${v} \
libnvinfer-lean-dev=${v} \
libnvinfer-dispatch-dev=${v} \
libnvinfer-plugin-dev=${v} \
libnvinfer-vc-plugin-dev=${v} \
libnvparsers-dev=${v} \
libnvonnxparsers-dev=${v} \
libnvinfer8=${v} \
libnvinfer-plugin8=${v} \
libnvinfer-vc-plugin8=${v} \
libnvparsers8=${v} \
libnvonnxparsers8=${v} && \
apt-get install \
python3-libnvinfer=${v} \
tensorrt-dev=${v} && \
apt-mark hold tensorrt-dev
# Build nvidia codec headers
RUN git clone -b sdk/11.1 --single-branch https://git.videolan.org/git/ffmpeg/nv-codec-headers.git && \
cd nv-codec-headers && make install && \
cd .. && rm -rf nv-codec-headers
# Build ffmpeg with nvenc support
RUN git clone --depth 1 -b release/6.0 --single-branch https://github.com/FFmpeg/FFmpeg.git && \
cd FFmpeg && \
mkdir ffmpeg_build && cd ffmpeg_build && \
../configure \
--enable-cuda \
--enable-cuvid \
--enable-shared \
--disable-static \
--disable-doc \
--extra-cflags=-I/usr/local/cuda/include \
--extra-ldflags=-L/usr/local/cuda/lib64 \
--extra-libs=-lpthread \
--nvccflags="-arch=sm_60 \
-gencode=arch=compute_60,code=sm_60 \
-gencode=arch=compute_61,code=sm_61 \
-gencode=arch=compute_70,code=sm_70 \
-gencode=arch=compute_75,code=sm_75 \
-gencode=arch=compute_80,code=sm_80 \
-gencode=arch=compute_86,code=sm_86 \
-gencode=arch=compute_89,code=sm_89 \
-gencode=arch=compute_89,code=compute_89" && \
make -j$(nproc) && make install && ldconfig && \
cd ../.. && rm -rf FFmpeg
# Install torch
RUN python3 -m pip install \
--upgrade \
--no-cache \
pip \
wheel \
setuptools \
twine \
awscli \
packaging \
ninja && \
## Install pytorch and submodules
CUDA_VER=${CUDA_VERSION%.*} && CUDA_VER=${CUDA_VER//./} && \
python3 -m pip install \
--no-cache \
torch==${TORCH_VERSION} \
torchvision==${TORCHVISION_VERSION} \
--index-url https://download.pytorch.org/whl/cu${CUDA_VER}
# Create working directory
WORKDIR /usr/src/
# Install TensorStream
RUN git clone -b master --single-branch https://github.com/osai-ai/tensor-stream.git /usr/src/tensor-stream && \
cd /usr/src/tensor-stream && \
python3 setup.py install
# Install nkb-tech detection pipeline
RUN git clone -b main https://github.com/nkb-tech/ultralytics.git /usr/src/ultralytics && \
cd ultralytics && \
python3 -m pip install \
--no-cache \
--editable \
".[export]" \
albumentations
# Install nkb-tech cv pipeline
# Use copy because of it is private project
COPY . /usr/src/app
RUN cd /usr/src/app && \
python3 -m pip install \
--no-cache \
--requirement \
requirements.txt
# cd cranpose && \
# python3 -m pip install .
# Install OpenCV with CUDA
RUN python3 -m pip uninstall \
--yes \
opencv-contrib-python \
opencv-python-headless \
opencv-python && \
ln -s /usr/include/x86_64-linux-gnu/cudnn_version_v8.h /usr/include/x86_64-linux-gnu/cudnn_version.h && \
git clone --depth 1 -b ${OPENCV_VERSION%.*} https://github.com/opencv/opencv.git /usr/src/opencv && \
git clone --depth 1 -b ${OPENCV_VERSION%.*} https://github.com/opencv/opencv_contrib.git /usr/src/opencv_contrib && \
cd /usr/src/opencv && \
mkdir build && \
cd build && \
cmake \
-D CPACK_BINARY_DEB=ON \
-D BUILD_EXAMPLES=OFF \
-D INSTALL_C_EXAMPLES=OFF \
-D BUILD_opencv_cudacodec=ON \
-D BUILD_opencv_python2=OFF \
-D BUILD_opencv_python3=ON \
-D BUILD_opencv_java=OFF \
-D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D CUDA_ARCH_BIN=6.0,6.1,7.0,7.5,8.0,8.6,8.9 \
-D CUDA_ARCH_PTX= \
-D ENABLE_FAST_MATH=ON \
-D CUDA_FAST_MATH=ON \
-D CUDNN_INCLUDE_DIR=/usr/include/x86_64-linux-gnu \
-D EIGEN_INCLUDE_PATH=/usr/include/eigen3 \
-D WITH_EIGEN=ON \
-D ENABLE_NEON=OFF \
-D OPENCV_DNN_CUDA=ON \
-D OPENCV_ENABLE_NONFREE=ON \
-D OPENCV_EXTRA_MODULES_PATH=/usr/src/opencv_contrib/modules \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D WITH_CUBLAS=ON \
-D WITH_CUDA=ON \
-D WITH_CUDNN=ON \
-D WITH_GSTREAMER=ON \
-D WITH_LIBV4L=ON \
-D WITH_OPENGL=ON \
-D WITH_OPENCL=OFF \
-D WITH_IPP=OFF \
-D WITH_TBB=ON \
-D WITH_TIFF=ON \
-D WITH_JPEG=ON \
-D WITH_PNG=ON \
-D BUILD_PERF_TESTS=OFF \
-D BUILD_TESTS=OFF \
-D WITH_QT=OFF \
-D BUILD_DOCS=OFF \
.. && \
make -j$(nproc) && \
make install && \
ldconfig
BykadorovR
TensorStreamConverter initializes successfully, reads the first image correctly (checked by saving and viewing an image). But when I try to read images further it return
RuntimeError