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  • Websites made ​​with MediaSPIP

    2 mai 2011, par

    This page lists some websites based on MediaSPIP.

  • Qu’est ce qu’un éditorial

    21 juin 2013, par

    Ecrivez votre de point de vue dans un article. Celui-ci sera rangé dans une rubrique prévue à cet effet.
    Un éditorial est un article de type texte uniquement. Il a pour objectif de ranger les points de vue dans une rubrique dédiée. Un seul éditorial est placé à la une en page d’accueil. Pour consulter les précédents, consultez la rubrique dédiée.
    Vous pouvez personnaliser le formulaire de création d’un éditorial.
    Formulaire de création d’un éditorial Dans le cas d’un document de type éditorial, les (...)

  • Des sites réalisés avec MediaSPIP

    2 mai 2011, par

    Cette page présente quelques-uns des sites fonctionnant sous MediaSPIP.
    Vous pouvez bien entendu ajouter le votre grâce au formulaire en bas de page.

Sur d’autres sites (14638)

  • Amazon Elastic Transcoder vs FFMPEG [closed]

    7 juillet 2017, par KiranD

    I’m developing a website (php based) and there is a provision to upload videos in different formats. I’m using HTML5 player for the front end presentation. So, as the ideal format that is supported by most of the browsers is mp4, I tried using ffmpeg and it works fine.

    I would like to know which transcoder (Amazon Elastic Transcoder or FFMPEG) would be best for handling conversions parallely when there is a huge traffic.

    There could me approximately thousands of users watching the videos and may be hundreds uploading the videos at the same time. I’m using Amazon EC2 for deployment and the traffic is mostly spiky (not flat).

    I’m not sure about the acceptable speed. But, I need the one which can transcode the videos much faster.

  • Best practices for developing scalable video transcoding server on Amazon Web Services ? [closed]

    5 février, par undefined

    What do people think are the most important issues when developing an application that is going to allow users to upload video and images to a server and have them transcoded by FFMPEG and stored in amazon S3 ? I have a couple of options ;

    


      

    1. install FFMPEG on the same server that handles file uploads, when a video is uploaded and stored on EC2 instance, call FFMPEG to convert it then when done, write the file to S3 bucket and dispose of the original.
    2. 


    


    How scalable is this ? What happens when many users upload at the same time ? How do I manage multiple processes at once ? How do I know when to start another instance and load balance this configuration ?

    


      

    1. Have one server for processing uploads (updating database, renaming files etc) and one server for doing transcoding. Again what is the best way to manage multiple processes ? should I be looking at Amazon SQS for this ? Can I tell the transcoding server to get the file from the upload server or should I copy the file to the transcoding server ? Should I just store all files on S3 and SQS can read from there. I am trying to have as little traffic as possible.
    2. 


    


    I am running a linux box as the upload server and have FFMPEG running on this.

    


  • How to transcode .mp4 files using ffmpeg celery rabbitMq in Amazon Linux ?

    11 mars 2017, par Srinivas 25

    I want to transcode .mp4, .flv files by using ffmpeg, celery and rabbitMQ. With the help of these tools i can able to transcode in localhost, where in
    my OS is ubuntu, but i am unable to do the same on AWS Linux for production
    Here is the code i am using to integrate ffmpeg, rabbitMQ and celery to transcode on Amazon Linux

    FFMPEG_PATH = '/usr/bin/ffmpeg'


    CELERY_BROKER_URL = 'amqp://guest:guest@awsuser:5672//'
    CELERY_ACCEPT_CONTENT = 'file'
    CELERY_RESULT_BACKEND = 'rpc://'
    CELERY_TASK_SERIALIZER = 'file'

    celery.py

    from __future__ import absolute_import
    import os
    from celery import Celery
    from afnity.settings import CELERY_BROKER_URL

    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'afnity.settings')


    app = Celery('taskapp',
            broker=CELERY_BROKER_URL,
            include=['taskapp.tasks'])

    app.config_from_object('django.conf:settings', namespace='CELERY')


    if __name__ == '__main__':
    app.start()

    tasks.py

    from .celery import app

    @app.task
    def add()
    return(3+4d)