Installation

This topic provides instructions for installing MIVisionX and related packages.

Prerequisites

The following are the hardware and OS requirements of the MIVisionX library. Refer to System Requirements for additional information.

Hardware

• CPU: AMD64

• GPU: AMD Radeon Graphics

• APU: AMD Radeon Mobile/Embedded

Note

Some modules in MIVisionX can be built for CPU only. To take advantage of advanced features and modules we recommend using AMD GPUs or APUs.

Operating Systems

• Linux

  • Ubuntu - 20.04 or 22.04

  • CentOS - 7

  • RedHat - 8 or 9

  • SLES - 15-SP4

• Windows 10 or 11

• macOS Ventura 13 or Sonoma 14

Libraries

• AMD Clang++

• CMake - Version 3.5 and above

• Half-precision floating-point(half) library - Version 1.12.0

• MIOpen

• MIGraphX

• RPP

• OpenCV - Version 3.X or 4.X

• OpenMP

• pkg-config

• FFmpeg - Version 4.X

Linux installation

The installation process uses the following steps:

• ROCm-supported hardware install verification

• Install ROCm 6.0.0 or later with amdgpu-install with --usecase=rocm

• Install either from existing Packages or by building Source files as described below

Installation from packages

Install MIVisionX runtime, development, and test packages.

• Runtime package - mivisionx only provides the dynamic libraries and executables

• Development package - mivisionx-dev / mivisionx-devel provides the libraries, executables, header files, and samples

• Test package - mivisionx-test provides ctest to verify installation

Ubuntu

sudo apt-get install mivisionx mivisionx-dev mivisionx-test

CentOS / RedHat

sudo yum install mivisionx mivisionx-devel mivisionx-test

SLES

sudo zypper install mivisionx mivisionx-devel mivisionx-test

• Package install supports HIP backend

• Package install requires OpenCV V4.6 manual install

• CentOS/RedHat/SLES requires FFMPEG Dev package manual install

Installation from source files

For your convenience a setup script is provided, MIVisionX-setup.py, which installs all required dependencies:

python MIVisionX-setup.py --directory [setup directory - optional (default:~/)]
                          --opencv    [OpenCV Version - optional (default:4.6.0)]
                          --ffmpeg    [FFMPEG V4.4.2 Installation - optional (default:ON) [options:ON/OFF]]
                          --amd_rpp   [MIVisionX VX RPP Dependency Install - optional (default:ON) [options:ON/OFF]]
                          --neural_net[MIVisionX Neural Net Dependency Install - optional (default:ON) [options:ON/OFF]]
                          --inference [MIVisionX Neural Net Inference Dependency Install - optional (default:ON) [options:ON/OFF]]
                          --developer [Setup Developer Options - optional (default:OFF) [options:ON/OFF]]
                          --reinstall [Remove previous setup and reinstall (default:OFF)[options:ON/OFF]]
                          --backend   [MIVisionX Dependency Backend - optional (default:HIP) [options:HIP/OCL/CPU]]
                          --rocm_path [ROCm Installation Path - optional (default:/opt/rocm ROCm Installation Required)]

• Install ROCm before running the setup script

• This script only needs to be executed once

• ROCm upgrade requires the setup script to be rerun

Using MIVisionX-setup.py

• Clone MIVisionX git repository

git clone https://github.com/ROCm/MIVisionX.git

Note

MIVisionX supports two GPU backends: HIP and OPENCL. Refer to the following instructions for installing with HIP backend. Refer to OPENCL GPU backend for instructions on installing with OpenCL backend.

Instructions for building MIVisionX with the HIP GPU backend (default backend)

1. Run the setup script to install all the dependencies required by the HIP GPU backend:

   cd MIVisionX
   python MIVisionX-setup.py
   

2. Run the following commands to build MIVisionX with the HIP GPU backend:

   mkdir build-hip
   cd build-hip
   cmake ../
   make -j8
   sudo make install
   

3. Run tests - test option instructions

   make test
   

Windows

• Windows SDK

• Visual Studio 2019 or later

• Install the latest AMD drivers

• Install OpenCL SDK

• Install OpenCV 4.6.0

  • Set OpenCV_DIR environment variable to OpenCV/build folder

  • Add %OpenCV_DIR%\x64\vc14\bin or %OpenCV_DIR%\x64\vc15\bin to your $PATH

Using Visual Studio

Use MIVisionX.sln to build for x64 platform

Important

Some modules in MIVisionX are only supported on Linux

macOS

Refer to macOS build instructions

Important

macOS only supports MIVisionX CPU backend

Verify installation

Linux / macOS

The installer will copy:

• Executables into /opt/rocm/bin

• Libraries into /opt/rocm/lib

• Header files into /opt/rocm/include/mivisionx

• Apps, & Samples folder into /opt/rocm/share/mivisionx

• Documents folder into /opt/rocm/share/doc/mivisionx

• Model Compiler, and Toolkit folder into /opt/rocm/libexec/mivisionx

Verify with sample application

Canny Edge Detection

export PATH=$PATH:/opt/rocm/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib
runvx /opt/rocm/share/mivisionx/samples/gdf/canny.gdf

Note

• More samples are available at ../samples/README.md#samples

• For macOS use export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/opt/rocm/lib

Verify with mivisionx-test package

Test package will install ctest module to test MIVisionX. Use the following steps to test package install:

mkdir mivisionx-test && cd mivisionx-test
cmake /opt/rocm/share/mivisionx/test/
ctest -VV

Windows

• MIVisionX.sln builds the libraries & executables in the folder MIVisionX/x64

• Use RunVX to test the build

./runvx.exe ADD_PATH_TO/MIVisionX/samples/gdf/skintonedetect.gdf

Docker

MIVisionX provides developers with docker images for Ubuntu 20.04 / 22.04. Using docker images developers can quickly prototype and build applications without having to be locked into a single system setup or lose valuable time figuring out the dependencies of the underlying software.

For more information about the Docker files used to build MIVisionX containers and a suggested workflow, see MIVisionX Docker documentation.

MIVisionX docker

• Ubuntu 22.04

• Ubuntu 20.04

Tested configurations

• Windows 10 or 11

• Linux distribution

  • Ubuntu - 20.04 or 22.04

  • CentOS - 7

  • RHEL - 8 or 9

  • SLES - 15-SP4

• ROCm: rocm-core - 6.1.0.60100

• RPP - 1.5.0.60100

• miopen-hip - 3.1.0.60100

• migraphx - 2.9.0.60100

• OpenCV - 4.6.0

• FFMPEG - n4.4.2

• Dependencies for all the above packages

• MIVisionX Setup Script - V3.1.0

Known issues

• OpenCV 4.X support for some apps missing

• MIVisionX Package install requires manual prerequisites installation

MIVisionX dependency map

Docker Image:
sudo docker build -f docker/ubuntu20/{DOCKER_LEVEL_FILE_NAME}.dockerfile -t {mivisionx-level-NUMBER} .

• New component added to the level

• Existing component from the previous level

Build Level	MIVisionX Dependencies	Modules	Libraries and Executables	Docker Tag
Level_1	cmake gcc g++	amd_openvx utilities	libopenvx.so - OpenVX Lib - CPU libvxu.so - OpenVX immediate node Lib - CPU runvx - OpenVX Graph Executor - CPU with Display OFF
Level_2	ROCm HIP +Level 1	amd_openvx amd_openvx_extensions utilities	libopenvx.so - OpenVX Lib - CPU/GPU libvxu.so - OpenVX immediate node Lib - CPU/GPU runvx - OpenVX Graph Executor - Display OFF
Level_3	OpenCV FFMPEG +Level 2	amd_openvx amd_openvx_extensions utilities	libopenvx.so - OpenVX Lib libvxu.so - OpenVX immediate node Lib libvx_amd_media.so - OpenVX Media Extension libvx_opencv.so - OpenVX OpenCV InterOp Extension mv_compile - Neural Net Model Compile runvx - OpenVX Graph Executor - Display ON
Level_4	MIOpen MIGraphX ProtoBuf +Level 3	amd_openvx amd_openvx_extensions apps utilities	libopenvx.so - OpenVX Lib libvxu.so - OpenVX immediate node Lib libvx_amd_media.so - OpenVX Media Extension libvx_opencv.so - OpenVX OpenCV InterOp Extension mv_compile - Neural Net Model Compile runvx - OpenVX Graph Executor - Display ON libvx_nn.so - OpenVX Neural Net Extension
Level_5	AMD_RPP RPP deps +Level 4	amd_openvx amd_openvx_extensions apps AMD VX RPP utilities	libopenvx.so - OpenVX Lib libvxu.so - OpenVX immediate node Lib libvx_amd_media.so - OpenVX Media Extension libvx_opencv.so - OpenVX OpenCV InterOp Extension mv_compile - Neural Net Model Compile runvx - OpenVX Graph Executor - Display ON libvx_nn.so - OpenVX Neural Net Extension libvx_rpp.so - OpenVX RPP Extension

Note

OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc.