search

Apra-Labs / ApraPipes

A pipeline framework for developing video and image processing application. Supports multiple GPUs and Machine Learning tooklits
MIT License
28 stars 33 forks source link

readme

Header Image

ApraPipes

A pipeline framework for developing video and image processing applications. Supports multiple GPUs and Machine Learning toolkits.
Learn more about ApraPipes here https://apra-labs.github.io/ApraPipes.

Build status

Aprapipes is automatically built and tested on Ubuntu (18.04 and 20.04), Jetson Boards (Jetpack 4.4) and Windows (11) x64 Visual Studio 2019 Community. OS Version With Cuda Tests Status
Windows 2019 No Test Results CI-Win-NoCUDA
Windows 2019 Yes Test Results CI-Win-CUDA
Ubuntu x64_86 20.04 No Test Results CI-Linux-NoCUDA
Ubuntu x64_86 18.04 Yes Test Results CI-Linux-CUDA
Ubuntu ARM64 (Jetsons) 18.04 Yes Test Results CI-Linux-ARM64
Ubuntu x64_86-WSL 20.04 Yes Test Results CI-Linux-CUDA-wsl
Ubuntu x64_86-docker 18.04 Yes No CI-Linux-CUDA-Docker

Getting Started with ApraPipes

Please select your Operating System

Windows (Version ≥ 10)

Windows Logo
Requirements ### Prerequisites ### Visual Studio * Install Visual Studio 2019 Community * Install Desktop development C++ * .NET Desktop development * Universal Windows Development Platform ### Cuda * Create an account on developer.nvidia.com if you're not already a member. Note : Otherwise the next step will show HTTP 404/403 error. * Windows 10/11 : [Cuda Toolkit 10.2](https://developer.nvidia.com/cuda-10.2-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal) or [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Windows). ### Cudnn * Download [Cudnn](https://developer.nvidia.com/rdp/cudnn-archive#a-collapse765-102) and extract files where cuda is installed. Note: Please be aware that this process requires some effort. Here are the necessary steps: * Download the correct zip file matching your cuda version. _Do not download the exe/installer/deb package._ * Windows: * Download [this file](https://developer.nvidia.com/compute/cudnn/secure/8.3.2/local_installers/10.2/cudnn-windows-x86_64-8.3.2.44_cuda10.2-archive.zip). * Clone with submodules and LFS. ``` git clone --recursive https://github.com/Apra-Labs/ApraPipes.git ```
Build Open PowerShell as an administrator and execute the following commands ### Build Without Cuda If your windows system does not have an NVIDIA GPU use this script ``` build_windows_no_cuda.bat ``` ### Build With Cuda ``` build_windows_cuda.bat ``` ### To Build With Documentation ``` build_windows_cuda.bat --build-doc ```
Test ### Run Tests * list all tests ``` _build/BUILD_TYPE/aprapipesut.exe --list_content ``` * run all tests ``` _build/BUILD_TYPE/aprapipesut.exe ``` * run all tests disabling memory leak dumps and better progress logging ``` _build/BUILD_TYPE/aprapipesut.exe -p -l all --detect_memory_leaks=0 ``` * run one test ``` _build/BUILD_TYPE/aprapipesut.exe --run_test=filenamestrategy_tests/boostdirectorystrategy ``` * run one test with arguments ``` _build/BUILD_TYPE/aprapipesut.exe --run_test=unit_tests/params_test -- -ip 10.102.10.121 -data ArgusCamera ``` * Look at the unit_tests/params_test to check for sample usage of parameters in test code.

Linux (Ubuntu ≥ 18.04)

Linux Logo
Requirements ### Prerequisites ### Cuda * Create an account on developer.nvidia.com if you're not already a member. Note : Otherwise the next step will show HTTP 404/403 error. * Ubuntu 18.04/20.04: 18.04 - [CUDA Toolkit 10.2](https://developer.nvidia.com/cuda-10.2-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1804&target_type=debnetwork) 20.04 - [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04) ### Cudnn * Download [Cudnn](https://developer.nvidia.com/rdp/cudnn-archive#a-collapse765-102) * Linux: * Download [this file](https://developer.nvidia.com/compute/cudnn/secure/8.3.2/local_installers/10.2/cudnn-linux-x86_64-8.3.2.44_cuda10.2-archive.tar.xz) * Clone with submodules and LFS. ``` git clone --recursive https://github.com/Apra-Labs/ApraPipes.git ```
Build * Run this command to make the script file executable. ``` chmod +x build_linux_*.sh ``` ### Build Without Cuda If your windows system does not have an NVIDIA GPU use this script ``` ./build_linux_no_cuda.sh ``` ### Build With Cuda ``` ./build_linux_cuda.sh ``` ### To Build With Documentation ``` ./build_linux_cuda.sh --build-doc ``` Build can take ~2 hours depending on the machine configuration.
Test ### Run Tests * list all tests ``` ./_build/aprapipesut --list_content ``` * run all tests ``` ./_build/aprapipesut ``` * run all tests disabling memory leak dumps and better progress logging ``` ./_build/aprapipesut -p -l all --detect_memory_leaks=0 ``` * run one test ``` ./_build/aprapipesut --run_test=filenamestrategy_tests/boostdirectorystrategy ``` * run one test with arguments ``` ./_buildaprapipesut --run_test=unit_tests/params_test -- -ip 10.102.10.121 -data ArgusCamera ``` * Look at the unit_tests/params_test to check for sample usage of parameters in test code.

Jetson Boards - Nano, TX2, NX, AGX (Jetpack ≥ 4.4)

Nvidia Logo
Requirements ### Prerequisites * Setup the board with [Jetpack 4.4](https://docs.nvidia.com/sdk-manager/install-with-sdkm-jetson/index.html) or higher as supported. * Clone with submodules and LFS. ``` git clone --recursive https://github.com/Apra-Labs/ApraPipes.git ```
Build ### Build for Jetson (Only Cuda Build) * Run this command to make the script file executable. ``` chmod +x build_jetson.sh ``` * ApraPipes builds CUDA version on Jetson Boards. ``` ./build_jetson.sh ``` * To Build With Documentation ``` ./build_jetson.sh --build-doc ``` Build can take ~12 hours on Jetson Nano. Note: Jetson build can also be done using Ubuntu 18.04 x86_64 Laptop via cross compilation.
Cross Compilation using qemu ### Cross compilation using qemu Conceptual steps adapted from [here](https://github.com/zhj-buffer/Cross-Compile-Jetson): * On any Intel Ubuntu 18.04 computer (physical or virtual including wsl ) mount a Jetson SD Card Image as described above * Copy relevant files from mounted image to created a rootfs * Install qemu on ubuntu host * chroot into emulated aarm64 environment using script provided in the github link above * install extra tools and build aprapipes and aprapipesut * the built aprapipesut can be copied to a Jetson board and run. This approach can use all 12-16 cores of a laptop and hence builds faster.
Test ### Run Tests * list all tests `./_build/aprapipesut --list_content` * run all tests `./_build/aprapipesut` * run one test `./_build/aprapipesut --run_test=filenamestrategy_tests/boostdirectorystrategy` * run one test with arguments `./_build/aprapipesut --run_test=unit_tests/params_test -- -ip 10.102.10.121 -data ArgusCamera` * Look at the unit_tests/params_test to check for sample usage of parameters in test code

Docker

Nvidia Logo
Requirements ### Prerequisites * Ensure virtualization is enabled in both the BIOS settings of your computer and the Windows virtualization feature -Refer [this article](https://support.microsoft.com/en-us/windows/enable-virtualization-on-windows-11-pcs-c5578302-6e43-4b4b-a449-8ced115f58e1#:~:text=Virtualization%20lets%20your%20PC%20emulate,will%20help%20you%20enable%20virtualization) to enable them * Install WSL 2 on your system: ``` wsl --install ``` * Set WSL 2 as the default version using the command line: ``` wsl --set-default-version 2 ``` * Install Ubuntu-18.04 from [Microsoft store](https://apps.microsoft.com/store/detail/ubuntu-1804-on-windows/9N9TNGVNDL3Q?hl=en-in&gl=in&rtc=1) , Refer [this article](https://learn.microsoft.com/en-us/windows/wsl/install-manual) for any issues regarding installation * Install Docker Desktop on Windows -from [here](https://docs.docker.com/desktop/install/windows-install/) * Enable Docker integration with WSL 2 (in Docker Desktop settings -> Resources -> WSL integration -> Enable Ubuntu-18.04 -> Apply&restart) * Install nvida-container-toolkit using (WSL Ubuntu-18.04) for docker to access Host-system GPU -Follow [this document](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) to install nvidia-container-toolkit * Note:"Follow the exact instructions outlined in the document to ensure the correct and successful installation of the NVIDIA Container Toolkit"
Build ### Build for Docker * Use this [docker image](https://github.com/users/kumaakh/packages/container/package/aprapipes-build-x86-ubutu18.04-cuda) with all the software setup. ``` docker pull ghcr.io/kumaakh/aprapipes-build-x86-ubutu18.04-cuda:last-good ``` * Mount an external volume as a build area, and then use the Windows command line to create a Docker container using the above image with the following command: ``` docker run -dit --gpus all -v "":"/mnt/b/" --name a799cc26f4b7 ``` ..your command should look like this [where D:\ws\docker-pipes->local_folder_path , pipes->container_name ] ``` docker run -dit --gpus all -v "D:\ws\docker-pipes":"/mnt/b/" --name pipes a799cc26f4b7 ``` * After creating the container, execute the following command to access its command line interface ``` docker exec -it /bin/bash ``` * Note:"When inside the container, build all contents within the mounted external folder" * clone the repository with submodules and LFS as described above * build using build_linux_\*.sh scripts as described [above](#build-for-linux) This build will be fairly fast (~10 mins) as entire vcpkg cache comes down with the docker image

Update Submodules

git submodule update --init --recursive

Update Documentation

To update documentation, refer to Documentation Guidelines in the Contribution-Guidelines.

To regenerate documentation

Run,

./build_documentation.sh