Video is not just a story; it is the storyteller. It weaves narratives, captures moments, and connects us in ways words alone cannot. The ever-increasing demand for video content in marketing, streaming and OTT platforms, social media, and mobile video consumption has increased the demand for bandwidth and server capacity. processing has revolutionized the media industry, enhancing accessibility to video content beyond previous boundaries. The continuously increasing appetite for multimedia content and the growing prominence of deep learning have driven industry leaders like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform to offer GPU-enabled instances tailored for parallel processing excellence. Cloud media Installing FFmpeg on CPU for Optimal Performance FFmpeg is an indispensable multimedia innovation champion, consistently delivering eminence in audio-video processing and transcoding. If you are new to FFmpeg or someone who wants to make the most out of FFmpeg and set it up for optimal performance on CPU, you can refer to, , where we discussed FFmpeg and provided a step-by-step guide to make the most out of FFmpeg on CPU. How to install FFmpeg on Linux from Source Now, we will take a step further and look into how FFmpeg can leverage the power of hardware acceleration, which can significantly reduce processing time and help deliver content to users faster than ever before. This How-to guide will show you how to set up FFmpeg for multimedia transcoding and other multimedia task with hardware acceleration. Problem Traditional CPU-based video processing on cloud servers can be time-consuming and inefficient. As videos increase in resolution and size, the processing demand grows exponentially. Even with multi-core CPU configurations, the computational overhead can cause extended processing times, leading to delays in workflows and increased costs. Given cloud infrastructure providers charge per instance-hour, the longer your server runs, the more expensive your bill becomes. Failing to embrace GPU-powered machines means underutilizing their potential, especially since GPUs are specifically designed for parallel computational tasks. Thus, there is a pressing need to harness the GPU's power to optimize FFmpeg's performance and make the most out of it. The Remedy Know your Hardware The first step is to figure out the GPU for your required architecture. For the scope of this guide, we will focus on and available with AWS Graviton2 and Intel-based instances on AWS, but you can follow this guide for any Nvidia GPU by installing the desired driver yourself or by following and getting the appropriate driver from the . Nvidia Tesla T4 Nvidia Tesla T4G G4g G4dn this guide Nvidia driver download center Update and Install System Utilities The following snippet updates your existing libraries and other utilities. It uses some variables that are defined in the final script. echo "Installing utilities..."
dnf -y update
dnf -y groupinstall "Development Tools"
dnf install -y openssl-devel cmake3 amazon-efs-utils htop iotop yasm nasm jq Setup Nvidia GPU, CUDA, and CUDNN As the scope of this article is to demonstrate a working GPU-powered FFmpeg on AWS, the following will check whether this is the Graviton2-powered instance which is based on architecture or an Intel-based instance. Once selected, script will install the appropriate drivers for the architecture. G4g ARM/AARCH64 G4dn if [ "$(uname -m)" = "aarch64" ]; then
    echo "System is running on ARM / AArch64"
    DRIVE_URL="https://us.download.nvidia.com/tesla/535.104.05/NVIDIA-Linux-aarch64-535.104.05.run"
    CUDA_SDK_URL="https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux_sbsa.run"
    CUDNN_ARCHIVE_URL="https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-8.9.5.29_cuda12-archive.tar.xz"
else
    DRIVE_URL="https://us.download.nvidia.com/tesla/535.104.05/NVIDIA-Linux-x86_64-535.104.05.run"
    CUDA_SDK_URL="https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux.run"
    CUDNN_ARCHIVE_URL="https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-8.9.5.29_cuda12-archive.tar.xz"
fi
echo "Setting up GPU..."
DRIVER_NAME="NVIDIA-Linux-driver.run"
wget -O "$DRIVER_NAME" "$DRIVE_URL"
TMPDIR=$LOCAL_TMP sh "$DRIVER_NAME" --disable-nouveau --silent

CUDA_SDK="cuda-linux.run"
wget -O "$CUDA_SDK" "$CUDA_SDK_URL"
TMPDIR=$LOCAL_TMP sh "$CUDA_SDK" --silent --override --toolkit --samples --toolkitpath=$USR_LOCAL_PREFIX/cuda-12.2 --samplespath=$CUDA_HOME --no-opengl-libs

CUDNN_ARCHIVE="cudnn-linux.tar.xz"
EXTRACT_PATH="$SRC_DIR/cudnn-extracted"
mkdir -p "$EXTRACT_PATH"

wget -O "$CUDNN_ARCHIVE" "$CUDNN_ARCHIVE_URL"
tar -xJf "$CUDNN_ARCHIVE" -C "$EXTRACT_PATH"
CUDNN_INCLUDE=$(find "$EXTRACT_PATH" -type d -name "include" -print -quit)
CUDNN_LIB=$(find "$EXTRACT_PATH" -type d -name "lib" -print -quit)
cp -P "$CUDNN_INCLUDE"/* $CUDA_HOME/include/
cp -P "$CUDNN_LIB"/* $CUDA_HOME/lib64/
chmod a+r $CUDA_HOME/lib64/*
ldconfig By now, you should have a working system with an Nvidia device driver, which can be checked by running in the terminal. nvidia-smi Install FFmpeg Prerequisites Before diving into FFmpeg's installation, it's crucial to understand and set up its dependencies so FFmpeg can process most general process tasks for different formats and get an understanding of how to expand or limit the scope of FFmpeg for your multimedia task. System Dependencies FFmpeg is a great tool for text processing and text rendering in videos as well. To enable this feature, we will need to install libraries that are used for text manipulation, embedding subtitles, and other fancy operations like embedding the watermark of your brand in the video. These are essential system libraries that FFmpeg requires. dnf install -y freetype-devel fribidi-devel harfbuzz-devel fontconfig-devel bzip2-devel Installing Audio & Video Codecs At its core, FFmpeg functions like a versatile framework, ready to be extended with various plugins and modules. One of the primary ways this extensibility shines is through its support for many audio and video codecs. These codecs are the building blocks that allow FFmpeg to decode and encode media in various formats. Now, we will install different codecs. FFmpeg is like a template that is built in a way anyone can write a plugin like a video codec and that can be used with FFmpeg : Since the target instance is powered with GPU, codec enables FFmpeg to leverage NVIDIA GPU acceleration, significantly speeding up video processing tasks for and / ffnvcodec ffnvcodec av1, H.264, HEVC H.265 : FFmpeg can use for subtitle renderer to handle subtitles in various video formats. LIBASS LIBASS : FFmpeg uses LIBAOM to decode and encode videos, if you want to use software transcoding for . LIBAOM (AV1 Codec Library) AV1 AV1 : These include libraries for various audio and video formats (e.g., for MP3 audio, for the Opus audio codec, and for the Ogg container). In this build we will also enable software encoding of and using and libraries . Other Codecs libmp3lame opus libogg H.264 HEVC/H.265 x264 x265 By ensuring all these prerequisites are installed and correctly set up, FFmpeg can provide its robust set of features, and users can harness the desired power out of this great utility. Installing FFmpeg Finally, we will compile GPU-accelerated FFmpeg from the source, with enabled support of all the codecs and libraries we have compiled or installed in previous steps. wget https://ffmpeg.org/releases/ffmpeg-snapshot.tar.bz2 &&
tar -jxf ffmpeg-snapshot.tar.bz2 &&
pushd ffmpeg &&
PKG_CONFIG_PATH="$USR_LOCAL_PREFIX/lib/pkgconfig:/usr/lib64/pkgconfig:/usr/share/pkgconfig:/usr/lib/pkgconfig:$USR_LOCAL_PREFIX/lib/pkgconfig" \
    ./configure \
    --prefix="$USR_LOCAL_PREFIX" --disable-static --enable-shared \
    --extra-cflags="-I$USR_LOCAL_PREFIX/include $NVIDIA_CFLAGS" \
    --extra-ldflags="-L$USR_LOCAL_PREFIX/lib $NVIDIA_LDFLAGS" \
    --extra-libs='-lpthread -lm' --bindir="$USR_LOCAL_PREFIX/bin" \
    --enable-gpl --enable-libaom --enable-libass --enable-libfdk-aac \
    --enable-libfreetype --enable-libmp3lame --enable-libopus \
    --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 \
    --enable-nonfree --enable-openssl $NVIDIA_FFMPEG_OPTS &&
make -j $CPUS &&
make install Verifying GPU Acceleration Support in FFmpeg Once you've set up FFmpeg with GPU acceleration capabilities, it's a good idea to ensure that it recognizes and can utilize the GPU for tasks like video encoding and decoding. The command below checks for the presence of NVIDIA's NVENC (NVIDIA Video Encoder) in the list of supported codecs within FFmpeg: ffmpeg -hide_banner -codecs | grep nvenc If you see the output codecs with the label (e.g., , , ), it indicates that FFmpeg has been correctly configured to support GPU-accelerated encoding for those specific codecs using NVIDIA's hardware. nvenc h264_nvenc hevc_nvenc av1_nvenc The Complete Script Building upon the various segments we've discussed, here's the consolidated FFmpeg installation script along with the Nvidia GPU installation. This script has been thoroughly tested on and running on AWS instances. It's not just limited to those; its flexibility is its strength. Whether you're on Debian, Red Hat, CentOS, Fedora, openSUSE, or even the sleek Alpine, this script is crafted to serve both and architectures seamlessly. Dive in and experience the universality of our FFmpeg installation script, bringing the power of multimedia to every corner of the Linux world. Amazon Linux 2023 Ubuntu 22.04 G5g ARM/aarch64 x86_64 https://gist.github.com/bilalmughal/cb89936bc947fa727a8ec66e3ddf768a?embedable=true Hands-On: Converting H.264 to H.265 using Hardware Acceleration In this section, we'll take a hands-on approach to show you how to convert an H.264 encoded video to H.265 using the power of NVIDIA's GPU acceleration. This way we can test the installation and experience the power of GPU acceleration in action. To perform this conversion, use the following command: ffmpeg -y -hide_banner -hwaccel cuvid -c:v h264_cuvid -i input.mp4 -vf "scale_cuda=720:480" -c:v hevc_nvenc output.mp4 In this command: and uses GPU-based decoding of the H.264 video. -hwaccel cuvid -c:v h264_cuvid scales the video using GPU acceleration. -vf "scale_cuda=1920:1080" instructs FFmpeg to encode the output video using the H.265 codec with NVIDIA's GPU acceleration. -c:v hevc_nvenc After executing the command, you'll get an encoded video. output.mp4 H.265 1080p Save up to 400% on Video-Transcoding. We conducted a comprehensive cost-performance-centric benchmark on AWS with CPU and GPU-powered instances, and the findings changed our perspective about the narrative “CPU is slow, but GPU is expensive.” The results were astonishing and enabled us to make decisions that enabled us to save up to 400% cost on video-transcoding jobs. We recommend you to check that out as well at CPU vs GPU for Video Transcoding: Challenging the Cost-Speed Myth Conclusion By integrating FFmpeg with GPU hardware acceleration on AWS or any other infra-provider or architecture, you can optimize costs and reduce processing times, but it also future-proofs your workflow for higher-resolution media processing tasks down the line. By compiling from the source, we are also addressing the outdated libraries and generalized package issues, which are not fully optimized for your architecture, as discussed in " " and some aspects are also discussed in the article as well. The evolution of multimedia is ceaseless, but with tools like FFmpeg combined with AWS's GPU prowess and cost-effectiveness, you are well-equipped to handle whatever challenges come next. Whether you're a media company looking to scale or a hobbyist aiming for maximum efficiency, GPU-accelerated FFmpeg on AWS is a game-changer. How to install FFmpeg on Linux from source "Why your AWS Deep learning AMI is Holding you back and how to fix Resources : Follow the detailed instructions on . Building FFmpeg from Source Master the Compilation Process! https://mirzabilal.com/how-to-install-ffmpeg-on-linux-from-source : See for yourself at CPU vs GPU Benchmark for video Transcoding on AWS Debunking the CPU-GPU myth! https://mirzabilal.com/cpu-vs-gpu-for-video-transcoding-challenging-the-cost-speed-myth : Learn How to Break Free at . Dark Truth about AWS Deep Learning AMIs The Silent Roadblock to Your Success! https://mirzabilal.com/why-your-aws-deep-learning-ami-is-holding-you-back-and-how-to-fix : Read this article and more at . Deep Learning with AWS Graviton2 and Nvidia Tensor T4g Discover Cutting-Edge Insights! https://mirzabilal.com/deep-learning-with-aws-graviton2-nvidia-tensor-t4g-for-as-low-as-free-with-cuda-12-2-56d8457a6f6d This guide was originally published at . https://mirzabilal.com/how-to-install-ffmpeg-with-harware-accelaration-on-aws Also published . here

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Enabling GPU-Accelerated FFmpeg on AWS for Optimal Video Processing

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