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Are We Compressed Yet?

This repository contains the arewecompressedyet.com website source code.

Running your own local copy of the website

To run a local copy, you will need a copy of node and npm.

You will need node, npm, etc. For Ubuntu:

sudo apt install nodejs npm nodejs-legacy libicu-dev

You'll also need tools to build AV1 or other codecs being tested:

sudo apt install yasm libtool

First, run the ./setup.sh script. It will create directories needed for AWCY to run.

Next, create a configuration file called config.json.

Here is an example config.json:

{ "channel": "#daalatest",
  "have_aws": false,
  "port": 3000,
  "rd_server_url": "http://localhost:4000"
}

You will also need a file called secret_key which contains the key needed to use the website.

echo 'fake_password_to_compile' > secret_key

These commands will create the configuration files and install the node.js modules that get used by awcy. Open a node command line and run the following:

cd www
npm install
cd ..
npm install
npm start

To run the server, execute the run_awcy.bat file or run the following in your command line:

  node awcy_server.js

Now you can open localhost:3000 with your browser to see your local version of the website.

Setting up repositories

For the website to build codecs, you need local checkouts of every codec. While the ./setup.sh script automatically adds AV1, you can add your own. For example:

git clone https://aomedia.googlesource.com/aom av1
ln -s av1 av1-rt

Setting up rd_server

The AWCY web server manages the repositories and runs/ directory, and compiles and builds the codecs. Once this is done, it hands off the job of actually talking to all the AWS machines to rd_server.

To install rd_server, checkout the rd_tool repository in the same directory as awcy:

git clone https://github.com/tdaede/rd_tool.git

Then start the rd_server.py daemon:

./rd_server.py

The rd_server.py daemon listens on port 4000 by default.

More documentation on rd_server.py can be found in its README.

Run database format

The runs/ directory will contain all of the output files generated from a job. There is a info.json file that specifies what options were used by that particular run. Here is an example of an info.json file:

{"codec":"daala","commit":"","run_id":"2014-09-19T22-00-08.196Z","task":"video-1-short","nick":"AWCY","task_type":"video"}

There is also an output.txt file that contains the output of the rd_tool.py script.

After each run, a cache file called list.json file is generated by the generate_list.js script. This contains all of the info.json files, as an ordered list. This should probably be replaced by a "real" database at some point.