GMAT is a collection of tools for accelerating video/image processing and transcoding using GPUs in datacenter.
For those who have known this repo as ffmpeg-gpu-demo, you can still find the demo pipelines in the ffmpeg-gpu-demo branch. GMAT is developed based on ffmpeg-gpu-demo, and features like tensorrt filter are kept in GMAT. We have been listening and gathering feedback from the industry during the past year, and developed tools that users told us useful but missing in GPU video processing. We organized these tools together and hence the new GMAT, hope you can find what you need here : )
- ffmpeg-gpu: GPU-enhanced ffmpeg
- GPU filters: crop, rotate, flip, smooth, tensorrt
- libgpuscale: GPU accelerated libswscale, providing rgb<->yuv conversion and scaling on GPU
- CUDA Runtime API support
- MeTrans SDK: GPU transcoding toolkit
- GPU codec tools: Programs to access and benchmark nvdec/nvenc/nvjpeg
- Smart decoding: Decode video frames at uniform intervals, or decode frames with scene cut detection.
- HEIF codec: HEIF image encoding/decoding accelerated by nvenc/nvdec
It should be noted that GMAT does not aim to provide a complete set of APIs for GPU video processing, there are a lot of great libraries/SDKs doing that already. Instead, our target is to solve the missing puzzle pieces. It's intented to use GMAT along with other libraries/SDKs you have been using in your current pipeline or solution. You can carve out whatever you need and integrate it into your project.
If you want to do something using GPU but can't find it in GMAT or elsewhere, you are welcome to submit an issue or even better, a PR, we would be happy to look into it.
Currently, ffmpeg-gpu and MeTrans need to be compiled separately.
ffmpeg-gpu has the following dependencies:
- CUDA Toolkit >= 11.0
- TensorRT >= 8.2 (Optional)
- CV-CUDA >= 0.3.0 (Optional)
We strongly recommend you to start with the NGC containers. If you would like to use the tensorrt filter, choose the tensorrt image; If you do not care for the tensorrt filter, the CUDA image will do. Note that for CUDA images, tags including devel are required since we need NVCC and CUDA header files, which are not included in base or runtime images.
# use CUDA image
docker run --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all -it --rm -v $(pwd):/gmat nvcr.io/nvidia/cuda:12.2.0-devel-ubuntu20.04 bash
# use TensorRT image
docker run --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all -it --rm -v $(pwd):/gmat nvcr.io/nvidia/tensorrt:23.07-py3 bashOnce inside the container, you can install CV-CUDA. Please refer to CV-CUDA's GitHub repo for more details on installation.
wget https://github.com/CVCUDA/CV-CUDA/releases/download/v0.3.1-beta/nvcv-lib-0.3.1_beta-cuda12-x86_64-linux.deb
wget https://github.com/CVCUDA/CV-CUDA/releases/download/v0.3.1-beta/nvcv-dev-0.3.1_beta-cuda12-x86_64-linux.deb
sudo apt install ./nvcv-lib-0.3.1_beta-cuda12-x86_64-linux.deb ./nvcv-dev-0.3.1_beta-cuda12-x86_64-linux.debLike compiling the original ffmpeg, we need to configure ffmpeg before running make.
cd gmat/ffmpeg-gpu
./configure --disable-ptx-compression --enable-cvcuda --enable-libtensorrt --extra-cflags=-I/opt/nvidia/cvcuda0/include/ --disable-static --enable-shared --enable-nonfree --enable-cuda-nvcc --extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64 --nvccflags='-gencode arch=compute_86,code=sm_86 -I./' --extra-libs=-lstdc++
make -j10The TensorRT filter is enabled by the --enable-libtensorrt option. Remember to change the --nvccflags option to match your GPU arch. E.g. arch=compute_86,code=sm_86 is for Ampere GPUs except A100. If you are using L4 (Ada), you should change it to arch=compute_89,code=sm_89. FFmpeg does not support PTX JIT at this moment, CUDA will report no kernel image available if you don't get the arch correct.
MeTranshas the following dependencies:
- CUDA Toolkit
- ffmpeg-gpu
- HEIF (Optional)
(Optional) If you need HEIF codec, you need to install HEIF reader/writer before compiling MeTrans:
git clone https://github.com/nokiatech/heif.git
cd heif/build
cmake ../srcs
make -j10
sudo mkdir /usr/local/include/heif
sudo cp -r ../srcs/api/* /usr/local/include/heif
sudo cp ./lib/*.so /usr/local/libMeTrans does not require ffmpeg-gpu to be built with cvcuda or tensorrt, so we can use the CUDA image:
docker run --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all -it --rm -v $(pwd):/gmat nvcr.io/nvidia/cuda:12.2.0-devel-ubuntu20.04 bash
# build and install ffmpeg-gpu
cd gmat/ffmpeg-gpu
./configure --disable-ptx-compression --disable-static --enable-shared --enable-nonfree --enable-cuda-nvcc --extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64 --nvccflags='-gencode arch=compute_86,code=sm_86 -I./' --extra-libs=-lstdc++
make -j10
sudo make install
cd ../metrans
make -j10
# make all_but_gl -j10The compiled binaries are located in metrans/build.
If you need HEIF codec, run make all_but_gl -j10 instead of make -j10. If you also need the AppNvDecGL sample which demonstrates how to display decoded video frames in OpenGL using CUDA OpenGL interoperation, run make all -j10. Note that AppNvDecGL requires OpenGL to be setup properly, it is recommended to run the sample on a local machine, as setup display on a remote environment can be tricky.
FFmpeg GPU Demo is under MIT license, check out the LICENSE for details.
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