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ale
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# syntax = docker/dockerfile:1.0-experimental
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# https://docs.docker.com/develop/develop-images/build_enhancements/#overriding-default-frontends
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# xxx move from cudagl to cuda, for runtime?
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ARG CUDA_DEVEL="11.0.3-devel-ubuntu20.04"
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ARG CUDA_RUNTIME="11.0.3-runtime-ubuntu20.04"
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# runtime starts as 2.3GB image
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# devel is runtime+ and is 4.2GB image
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FROM nvidia/cuda:$CUDA_DEVEL AS builder
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# v7.5 == Turing
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# v6.1 == Pascal
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ARG CUDA_ARCH_75=75
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ARG CUDA_ARCH_61=61
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ARG FFMPEG_TGZ=https://ffmpeg.org/releases/ffmpeg-5.0.tar.gz
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# create an ffmpeg (w/ shared libs) that can utilize nvidia GPU
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WORKDIR /tmp
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ARG DEBIAN_FRONTEND=noninteractive
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RUN apt-get -yqq update && apt-get -yqq install \
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# get ffmpeg wanted typicals:
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autoconf automake build-essential cmake git-core wget \
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pkg-config texinfo wget yasm sudo \
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libass-dev libfreetype6-dev libgnutls28-dev libsdl2-dev libtool libva-dev libvdpau-dev \
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libvorbis-dev libxcb1-dev libxcb-shm0-dev libxcb-xfixes0-dev meson ninja-build \
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zlib1g-dev \
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\
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# needed to make archive.org mp4 derivatives:
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libx264-dev libfdk-aac-dev \
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# needed for https:// source urls:
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openssl libssl-dev \
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# these allow us to make any rarely encoded source file decoding avail:
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libx265-dev libvpx-dev libopus-dev
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# install nvidia headers (got moved out of ffmpeg)
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RUN git clone https://git.videolan.org/git/ffmpeg/nv-codec-headers && \
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cd nv-codec-headers && git checkout origin/sdk/11.0 && \
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sudo make install && \
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cd ..
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# build ffmpeg from source, so we can add in all the nvidia/cuda options
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RUN wget -qO- ${FFMPEG_TGZ} | tar xzf - && mv $(basename ${FFMPEG_TGZ} .tar.gz) ffmpeg
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WORKDIR /tmp/ffmpeg
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# Compile ffmpeg twice - same stanzas just `$CUDA_ARCH_..` and final `cp` differ
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# patch `configure` since Tesla T4 is on Turing architecture GPU (o/w --enable-libnpp fails)
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# https://en.wikipedia.org/wiki/CUDA#GPUs_supported
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# https://github.com/NVIDIA/cuda-samples/issues/46
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RUN sed -i -e "s/gencode arch=compute_..,code=sm_../gencode arch=compute_${CUDA_ARCH_75},code=sm_${CUDA_ARCH_75}/" ./configure\
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&& make distclean || echo && \
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./configure --enable-nonfree --enable-gpl \
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--enable-libfdk-aac \
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--enable-libfreetype \
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--enable-libopus \
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--enable-libvpx \
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--enable-libx264 \
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--enable-libx265 \
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--enable-openssl \
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# --enable-libvorbis # xxxx
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--enable-cuda --enable-cuda-sdk --enable-cuda-nvcc --enable-nvenc --enable-cuvid --enable-libnpp \
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--extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64 && \
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make -j4 && \
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cp ffmpeg /tmp/ffmpeg-turing
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RUN sed -i -e "s/gencode arch=compute_..,code=sm_../gencode arch=compute_${CUDA_ARCH_61},code=sm_${CUDA_ARCH_61}/" ./configure\
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&& make distclean || echo && \
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./configure --enable-nonfree --enable-gpl \
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--enable-libfdk-aac \
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--enable-libfreetype \
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--enable-libopus \
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--enable-libvpx \
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--enable-libx264 \
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--enable-libx265 \
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--enable-openssl \
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# --enable-libvorbis # xxxx
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--enable-cuda --enable-cuda-sdk --enable-cuda-nvcc --enable-nvenc --enable-cuvid --enable-libnpp \
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--extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64 && \
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make -j4 && \
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cp ffmpeg /tmp/ffmpeg-pascal
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# now collect up all the .so files we'll need for the runtime, into new lib/ subdir
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RUN BIN=ffmpeg && mkdir lib && ( \
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ldd ${BIN?} |awk '{if(substr($3,0,1)=="/") print $3}'; \
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) |xargs -d '\n' -I{} cp --copy-contents {} ./lib
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# switch to the smaller "runtime" baseline.
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# now we just keep the executable(s) and .so files they need and chuck everything else above.
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FROM nvidia/cudagl:$CUDA_RUNTIME
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COPY --from=builder /tmp/ffmpeg-pascal /ffmpeg-pascal
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COPY --from=builder /tmp/ffmpeg-turing /ffmpeg
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COPY --from=builder /tmp/ffmpeg/lib/ /fflib
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# @see cuda-runtime.sh for where this small image of three cuda runtime .so files came from
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# COPY --from=registry.archive.org/www/ffmpeg-gpu/cuda /cuda/*.so.1 /fflib
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# /cuda we volume mount from the container's host /usr/lib/x86_64-linux-gnu/ so we runtime load:
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# libcuda.so.1
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# libnvcuvid.so.1
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# libnvidia-encode.so.1
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ENV LD_LIBRARY_PATH=/fflib:/cuda
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ENTRYPOINT ["/ffmpeg"]
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CMD ["--help"]
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48
README.md
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48
README.md
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# docker-gpu-ffmpeg
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Use `ffmpeg` with `docker` and `nvidia` powers to transcode in Debian 11 Bullseye, based on [this nice docker project](https://git.archive.org/www/ffmpeg-gpu)
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(__)
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(oo)
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/------\/
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/ | ||
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* /\---/\
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~~ ~~
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..."Have you mooed today?"...
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## Requisites
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### This project needs docker and docker-compose working with [nvidia runtime](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
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## Build
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$ git clone https://git.manalejandro.com/ale/docker-ffmpeg-gpu
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$ cd docker-ffmpeg-gpu && docker-compose build --force-rm
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## Usage
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### You can use "/ffmpeg" or "/ffmpeg-pascal" for architecture version
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$ docker-compose up -d
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$ docker-compose run --rm --entrypoint /ffmpeg nvidia-ffmpeg -hwaccels -v 0
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Hardware acceleration methods:
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vdpau
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cuda
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vaapi
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## Sample decode using CUDA:
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$ docker-compose run --rm --entrypoint /ffmpeg nvidia-ffmpeg -hwaccel cuda -i /folder/input /folder/output
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## Full hardware transcode with NVDEC and NVENC:
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$ docker-compose run --rm --entrypoint /ffmpeg nvidia-ffmpeg -hwaccel cuda -hwaccel_output_format nvdec -i /folder/input -c:v h264_nvenc /folder/output
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## Shutdown
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$ docker-compose down
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## License
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MIT
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24
docker-compose.yml
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24
docker-compose.yml
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version: '2'
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services:
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nvidia-ffmpeg:
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build: ./
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image: nvidia-ffmpeg
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container_name: nvidia-ffmpeg
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restart: "no"
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entrypoint:
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- /bin/sleep
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- infinity
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volumes:
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- /usr/lib/x86_64-linux-gnu/nvidia/current:/cuda:ro
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- $PWD/folder:/folder
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environment:
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- NVIDIA_VISIBLE_DEVICES=all
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devices:
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- /dev/nvidia0
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- /dev/nvidiactl
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- /dev/nvidia-uvm
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- /dev/nvidia-uvm-tools
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cap_add:
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- IPC_LOCK
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network_mode: host
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