| #include <gst/gst.h> | |
| static gboolean bus_call (G_GNUC_UNUSED GstBus *bus, GstMessage *msg, gpointer data) | |
| { | |
| GMainLoop *loop = (GMainLoop *) data; | |
| switch (GST_MESSAGE_TYPE (msg)) { | |
| case GST_MESSAGE_EOS: | |
| g_print ("End of stream\n"); |
Here's my setup:
Complete up to the "Generate the cert" section in this gist
Using ffmpeg-3.1.6-1.fc25.x86_64 on Fedora 25, available from rpmfusion-free repo. It has been built with va-api support.
The following ffmpeg invocation works for me.
ffmpeg -hwaccel vaapi -hwaccel_output_format vaapi -vaapi_device /dev/dri/renderD128 \
-i input.mkv -vf 'format=nv12,hwupload' -map 0:0 -map 0:1 -threads 4 \
-acodec copy -vcodec h264_vaapi -qp:v 23 \
output.mp4
The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.
I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).
So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht
| silly gist hack, why do we need you? :( |
After a weekend of research, stress and pain I finally figure out how to install manjaro 17 and configure the nvidia/bumblebee drivers on my avell laptop
Here's my notebook specs:
$ inxi -MGCNA
Machine: Device: laptop System: Avell High Performance product: 1513
Mobo: N/A model: N/A v: 0.1 UEFI: American Megatrends v: N.1.02 date: 09/28/2016
Battery BAT0: charge: 44.0 Wh 100.0% condition: 44.0/44.0 Wh (100%)
| During GSoC I was able to accomplish the following: optimize part of the ipred functions and implement tile threading support. | |
| Links for optimized avx2 ipred functions: | |
| http://git.videolan.org/?p=ffmpeg.git;a=commit;h=35a5d9715dd82fd00f1d1401ec6be2d3e2eea81c | |
| http://git.videolan.org/?p=ffmpeg.git;a=commit;h=81fc617c125734aa6f3b3d938af75fef6db750e7 | |
| http://git.videolan.org/?p=ffmpeg.git;a=commit;h=73d9a9a6af5d00cfa9b98c7d9fc9abd0c734ba8e | |
| Links for the tile threading code: | |
| http://ffmpeg.org/pipermail/ffmpeg-devel/2017-August/215363.html | |
| http://ffmpeg.org/pipermail/ffmpeg-devel/2017-August/215361.html |
| @echo on | |
| REM checked for Windows 10 | |
| REM fork from https://gist.github.com/theultramage/cbdfdbb733d4a5b7d2669a6255b4b94b | |
| REM you may want full list https://gist.github.com/raspi/203aef3694e34fefebf772c78c37ec2c | |
| REM SET attrib=+ATTRIB_HIDE | |
| SET attrib=-ATTRIB_HIDE | |
| REM Hard disk burst ignore time | |
| powercfg -attributes 0012ee47-9041-4b5d-9b77-535fba8b1442 80e3c60e-bb94-4ad8-bbe0-0d3195efc663 %attrib% |