Brainiarc7

First, install arch-install-scripts:

sudo pacman -S --needed arch-install-scripts

Secondly, mount your partitions in all the internal hard drives.

Thirdly, generate and validate your config by piping it out to stdout:

Brainiarc7

Build VAAPI with support for VP8/9 decode and encode hardware acceleration on a Skylake validation testbed:

Build platform: Ubuntu 16.04LTS.

First things first:

Install baseline dependencies first

sudo apt-get -y install autoconf automake build-essential libass-dev libtool pkg-config texinfo zlib1g-dev libva-dev cmake mercurial libdrm-dev libvorbis-dev libogg-dev git libx11-dev libperl-dev libpciaccess-dev libpciaccess0 xorg-dev intel-gpu-tools

Brainiarc7

Using VAAPI's hardware accelerated video encoding on Linux with Intel's hardware on FFmpeg and libav

Hello, brethren :-)

As it turns out, the current version of FFmpeg (version 3.1 released earlier today) and libav (master branch) supports full H.264 and HEVC encode in VAAPI on supported hardware that works reliably well to be termed "production-ready".

Brainiarc7

Tuning Intel Skylake and beyond for optimal performance and feature level support on Linux:

Note that on Skylake, Kabylake (and the now cancelled "Broxton") SKUs, functionality such as power saving, GPU scheduling and HDMI audio have been moved onto binary-only firmware, and as such, the GuC and the HuC blobs must be loaded at run-time to access this functionality.

Enabling GuC and HuC on Skylake and above requires a few extra parameters be passed to the kernel before boot.

Instructions provided for both Fedora and Ubuntu (including Debian):

Note that the firmware for these GPUs is often packaged by your distributor, and as such, you can confirm the firmware blob's availability by running:

Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

Brainiarc7

wget: Shortcuts to excellent downloads at your fingertips

Install wget first on your Linux distribution, then proceed to usage.

Download a Single File:

Let’s start with something simple. Copy the URL for a file you’d like to download in your browser.

Now head back to the Terminal and type wget followed by the pasted URL. The file will download, and you’ll see progress in real-time as it does.

Brainiarc7

How to install NVIDIA Docker 2 package on Ubuntu and Debian:

If you came to this result (from Google or elsewhere) after realizing that Nvidia-docker's entry on this subject does not result in a working installation, here are the basic steps needed to install this package correctly:

For starters, ensure that you've installed the latest Docker Community edition by following the steps below:

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88

sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"

Brainiarc7

Useful Examples of ffmpeg and GNU parallel on the command-line:

Transcoding FLAC music to Opus:

ffmpeg is a highly useful application for converting music and videos. However, audio transcoding is limited to a a single core. If you have a large FLAC archive and you wanted to compress it into the efficient Opus codec, it would take forever with the fastest processor to complete, unless you were to take advantage of all cores in your CPU.

parallel 'ffmpeg -v 0 -i "{}" -c:a libopus -b:a 128k "{.}.opus"' ::: $(find -type f -name '*.flac')

Transcoding Videos to VP9:

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Low-Latency Live Streaming for your Desktop using ffmpeg and netcat:

Preamble:

In this post I will explore how to stream a video and audio capture from one computer to another using ffmpeg and netcat, with a latency below 100ms, which is good enough for presentations and general purpose remote display tasks on a local network.

The problem:

Streaming low-latency live content is quite hard, because most software-based video codecs are designed to achieve the best compression and not best latency. This makes sense, because most movies are encoded once and decoded often, so it is a good trade-off to use more time for the encoding than the decoding.

Brainiarc7

Spawning multiple ffmpeg processes with xargs:

On standard NVIDIA GPUs (Not the Quadros and Tesla lines), NVENC encodes are limited to two simultaneous sessions. The sample below illustrates how to pass a list of AVI files to ffmpeg and encode them to HEVC on two encode sessions:

$ find Videos/ -type f -name \*.avi -print | sed 's/.avi$//' |\
  xargs -n 1 -I@ -P 2 ffmpeg -i "@.avi" -c:a aac -c:v hevc_nvenc "@.mp4"

This will find all files with the ending .avi in the directory Videos/ and transcode them into HEVC/H265+AAC files with the ending .mp4. The noteworthy part here is the -P 2 to xargs, which starts up to two processes in parallel.