Please consult the wiki for detailed information.

Note: In addition to this initial setup, you will need to [[configure AWS|AWS-Setup]] so that distributed processing works.

The setup process for Argos is fairly complex, but some scripts vastly simplify it.

Argos is built in Python 3.3, so make sure you have pip3 and virtualenv-3.3:

# OSX
$ brew install python3 # (also installs pip3)
$ pip3 install virtualenv

# Ubuntu
$ sudo apt-get install python3.3 python3-pip -y
$ sudo pip3 install virtualenv

Then, the easiest way to set things up is to just run the setup script:

$ ./setup

This will install any necessary system dependencies, setup the virtualenv, setup NLTK with the necessary data, install Postgres and setup its databases, download and setup Stanford NER, and generate the documentation.

You will also need to setup the databases, which you can do with:

$ ./run db:create

This creates a Postgres user, argos_user, and sets up development and testing databases (argos_dev, and argos_test) respectively. (If you ran ./setup already, this step should not be necessary.)

And then when you're ready to start developing/testing, run:

$ ./go &

This command will startup the Argos environment as a background process. It will tell you its pid, keep note of that so you can kill it later. The environment runs:

• Redis (6379)
• Stanford NER (8080)
• RabbitMQ (5672)
• A Celery worker

Note: If you're running this on Ubuntu, some of these processes may fail, but it is because they are already running as services. Don't worry about it.

Then when you're done, kill it with:

$ kill <pid>

When you get everything setup it's worth running the tests to ensure that things have installed correctly:

$ source dev-env/bin/activate
$ ./run test

You can also profile some of the more intensive parts to identify bottlenecks:

$ source dev-env/bin/activate
$ ./run profile

You can also evaluate the quality of some of the algorithms, such as clustering:

$ source dev-env/bin/activate
$ ./run evaluate

Note: If you are having import errors or the packages seem to be missing, fear not ~ it may be because some package failed to install and pip rolled back the installs of everything else. Check your pip logs at ~/.pip/pip.log. I'd wager it is scipy which ran into a missing dependency.

You can optionally setup the default ~436 sources for collecting articles by doing (make sure Postgres is running):

$ ./manage/load_sources

You will also need to set up Amazon Web Services to adminster and use cloud instances. See AWS Setup