• Create and activate a Python virtual environment.
• Load application dependencies into the Python virtual environment by running pip install -r requirements.txt.
• Set up a MongoDB database. One option is to create a free Heroku instance with a MongoLab sandbox add-on.
• Create a .env file in the root directory with the following lines (substitute db_user, db_password, host, and port with details of your development MongoDB connection):

MONGOLAB_URI=mongodb://db_user:db_password@host:port
APP_SETTINGS='config.DevelopmentConfig'

• Create a production version .production-env that uses the production configuration.

APP_SETTINGS='config.Production'

• Set up a Redis instance. For Heroku, follow the instructions on https://devcenter.heroku.com/articles/heroku-redis In your .env file, add:

REDIS_URL=redis://user:password@host:port

To set up Heroku environment, add the following buildpacks, using the toolbelt command heroku buildpacks:add or equivalent:

1. https://github.com/heroku/heroku-buildpack-python

• Run the application using either heroku local (if using Heroku) or ./run.sh .env -b host:port. Use the second option if you would like to see exceptions.

• Use heroku local -f Procfile.test (if using Heroku) or
• Be sure to run both ./run_tests.sh .env AND ./run_tests.sh .production-env to test both dev and production environments.

• Go to /index.html to define an event.

• Inside /data/test_data are test sets. test_strict_new_feature is from Liu et al. (NAACL 2016). testEvents is from TAC-KBP dry run 2016. self_generated contains data sets created using this system.