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VALI
VALI is a video analytics and processing project for python. It’s set of C++ libraries and Python bindings which provide full HW acceleration for video processing tasks such as decoding, encoding, transcoding and GPU-accelerated color space and pixel format conversions.
VALI also supports DLPack and can share memory with all the modules which supports DLPack (e. g. share decoded surfaces with torch).
Documentation
https://romanarzumanyan.github.io/VALI
Prerequisites
VALI works on Linux(tested on Ubuntu 22.04) and Windows
-
NVIDIA display driver: 525.xx.xx or above
-
CUDA Toolkit 11.2 or above
- CUDA toolkit has driver bundled with it e.g. CUDA Toolkit 12.0 has driver
530.xx.xx
. During installation of CUDA toolkit you could choose to install or skip installation of the bundled driver. Please choose the appropriate option.
- CUDA toolkit has driver bundled with it e.g. CUDA Toolkit 12.0 has driver
-
FFMPEG
- VALI will download FFMPEG build from https://github.com/BtbN/FFmpeg-Builds
- Make sure you don't have system FFMPEG packages installed. Otherwise, CMake will use them instead. You may run
apt remove libavfilter-dev libavformat-dev libavcodec-dev libswresample-dev libavutil-dev
on Ubuntu to remove them. Alternatively, build in Docker.
-
Python 3.10 and above
-
Install a C++ toolchain either via Visual Studio or Tools for Visual Studio.
- Recommended version is Visual Studio 2017 and above (Windows only)
Licensing
VALI sources are available under Apache 2 license.
Wheels contain FFMpeg libraries downloaded from https://github.com/BtbN/FFmpeg-Builds/releases that are licensed under LGPLv2.1.
This software uses code of <a href=http://ffmpeg.org>FFmpeg</a> licensed under the <a href=http://www.gnu.org/licenses/old-licenses/lgpl-2.1.html>LGPLv2.1</a> and its source can be downloaded <a href=https://github.com/BtbN/FFmpeg-Builds>here</a>
Install from PyPi (Linux only for now)
python3 -m pip install python_vali
All information below is relevant if you want to build VALI on your local machine.
Linux
Ubuntu 22.04 is recommended.
Install dependencies
apt install -y \
wget \
build-essential \
git
Install CUDA Toolkit
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/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-get install -y cuda
# Ensure nvcc to your $PATH (most commonly already done by the CUDA installation)
export PATH=/usr/local/cuda/bin:$PATH
Install locally with pip
# Update git submodules
git submodule update --init --recursive
pip3 install .
To check whether VALI is correctly installed run the following Python script
import python_vali as vali
If using Docker via Nvidia Container Runtime,
please make sure to enable the video
driver capability: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/user-guide.html#driver-capabilities via
the NVIDIA_DRIVER_CAPABILITIES
environment variable in the container or the --gpus
command line parameter (e.g.
docker run -it --rm --gpus 'all,"capabilities=compute,utility,video"' nvidia/cuda:12.1.0-base-ubuntu22.04
).
Windows
- Install a C++ toolchain either via Visual Studio or Tools for Visual Studio (https://visualstudio.microsoft.com/downloads/)
- Install the CUDA Toolkit: https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64
# Update git submodules
git submodule update --init --recursive
pip install .
To check whether VALI is correctly installed run the following Python script
cuda_path = os.environ["CUDA_PATH"]
os.add_dll_directory(os.path.join(cuda_path, "bin"))
import python_vali as vali
Docker
For convenience, we provide a Docker images located at docker
that you can use to easily install all dependencies
(docker and nvidia-docker
are required)
DOCKER_BUILDKIT=1 sudo docker build \
--tag vali-gpu \
--file docker/Dockerfile \
--build-arg PIP_INSTALL_EXTRAS=torch .
docker run -it --rm --gpus=all vali-gpu
PIP_INSTALL_EXTRAS
can be any subset listed under project.optional-dependencies
in pyproject.toml.
Offline documentation
A documentation for VALI can be generated from this repository:
pip install . # install VALI
pip install sphinx # install documentation tool sphinx
cd docs
make html
You can then open _build/html/index.html
with your browser.
Community Support
Please use project's Discussions page for that.