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• Outputing HLS m3u8 TS segment PHP-FPM Nginx

  11 avril 2016, par Krasic

  I’m reading a dynamicly generated m3u8 hls playlist from ffmpeg

  basically the file look like

  #EXTM3U
  
  #EXT-X-VERSION:3
  
  #EXT-X-TARGETDURATION:11
  
  #EXT-X-MEDIA-SEQUENCE:3
  
  #EXTINF:11.000000,
  
  2_3.ts
  
  #EXTINF:8.000000,
  
  2_4.ts
  
  #EXTINF:11.000000,
  
  2_5.ts
  
  #EXTINF:11.000000,
  
  2_6.ts
  
  #EXTINF:8.000000,
  
  2_7.ts
  
  #EXTINF:11.000000,
  
  2_8.ts

  I’m trying to find a way to limit the connection to a single connection.
  I have a Table called Connection : it save the Stream ID + User ID + Date_Start & Date_end & status

  Once a user started watching the stream it add new Data : Example

  (StreamID : (2) , userID : (3) , Date_Start : Now() , Date_end : Null , status : ON )

  once he closed the connection i would like to update the date_end with Now() & status to OFF

  Environnement :
  Nginx , PHP-FPM & FFmpeg

  i’m using the register_shutdown_function( 'shutdown' ) to write the last update

  however the problem is once the playlist is played the shutdown is executed many times duo to the m3u8 contains 6 segments , each few seconds the shutdown is executed many times again (duo the chunk list updated) while i didnt closed yet the connection .

  I’m looking a way to handle the playlist as one connection to be keeped , so the function shutdown will be Executed ONLY when client abort the connection and not while the playlist been reloaded

  Regards

• A Better Process Runner

  1er janvier 2011, par Multimedia Mike — Python

  I was recently processing a huge corpus of data. It went like this : For each file in a large set, run 'cmdline-tool <file>', capture the output and log results to a database, including whether the tool crashed. I wrote it in Python. I have done this exact type of the thing enough times in Python that I’m starting to notice a pattern.

  Every time I start writing such a program, I always begin with using Python’s commands module because it’s the easiest thing to do. Then I always have to abandon the module when I remember the hard way that whatever ’cmdline-tool’ is, it might run errant and try to execute forever. That’s when I import (rather, copy over) my process runner from FATE, the one that is able to kill a process after it has been running too long. I have used this module enough times that I wonder if I should spin it off into a new Python module.

  Or maybe I’m going about this the wrong way. Perhaps when the data set reaches a certain size, I’m really supposed to throw it on some kind of distributed cluster rather than task it to a Python script (a multithreaded one, to be sure, but one that runs on a single machine). Running the job on a distributed architecture wouldn’t obviate the need for such early termination. But hopefully, such architectures already have that functionality built in. It’s something to research in the new year.

  I guess there are also process limits, enforced by the shell. I don’t think I have ever gotten those to work correctly, though.

• Parallel transcoding with FFmpeg on M1 Mac

  27 août 2021, par Pushkar

  I'm trying to determine how effective an M1 Mac would be for transcoding video using FFmpeg (specifically resizing and adjusting bitrate). I can do single transcodes using a command like this :

  


  ffmpeg -I in.mp4 -nostdin -c:v h264_videotoolbox -c:a copy -vf scale=1280:720 -b:v 8000k out.mp4


  


  Now, when I run this, I can see the process in Activity Monitor, but it shows significant CPU use but 0% GPU use, although it's certainly using some form of hardware acceleration (using libx264 instead of h264_videotoolbox is much slower).

  


  When I try running multiple processes concurrently, the timings suggest little evidence of parallel execution :

  


  





  


  


  


  


  



  


  


  


  


  


  


  


  


  


  


  


  


  Test	Timing
  convert single test file	6.8s
  convert file 5 times sequentially	33.6s
  convert file 5 times in parallel	31.5s

  


  


  Since the M1 chip is supposed to have 7 or 8 GPUs inside it, I expected to see quite good parallelism, so are there options which I'm missing which would :
  

  


  

  1. ensure that the transcoding is actually running on the GPU ?

  


  2. allow parallel execution across the multiple GPUs ?