Installation: lookit-api (Django project)

lookit-api is the codebase for Experimenter and Lookit, excluding the actual studies themselves. Any functionality you see as a researcher or a participant (e.g., signing up, adding a child, editing or deploying a study, downloading data) is part of the lookit-api repo. This project is built using Django and PostgreSQL. (The studies themselves use Ember.js; see Ember portion of codebase, ember-lookit-frameplayer.), It was initially developed by the Center for Open Science.

If you install only the lookit-api project locally, you will be able to edit any functionality that does not require actual study participation. For instance, you could contribute an improvement to how studies are displayed to participants or create a new CSV format for downloading data as a researcher.

Note: These instructions are for Mac OS. Installing on another OS? Please consider documenting the exact steps you take and submitting a PR to the lookit-api repo to update the documentation!

Basic installation

Note: the $ represents the command prompt below - e.g. the start of a new line in Terminal. Don’t actually type it in!

  • Clone the lookit-api repo: $ git clone

  • Navigate to the root project directory: $ cd lookit-api

  • Use $ pipenv --version to see if you already have pipenv installed. If it is, you will see the version; if not, you will see “pipenv: command not found”. If needed, install using pip install pipenv.

  • Create a virtual environment using pipenv and Python 3.8: $ pipenv --python 3.8 If you don’t have Python 3.8 available, you can download it from

  • Enter the virtual environment using $ pipenv shell.

  • Install invoke using $ pip install invoke.

  • Use the invoke script to go through setup: $ invoke setup This will install dependencies, create a local .env file, create local SSL certificates, and set up a postgresql database. You will be prompted a few times to enter your password, which is because a command is being run using sudo - this should be the password you use to log in to your account on your computer. When it finishes, you should see something like:

    Serving at
    Watching for file changes with StatReloader
  • Create a superuser by running python createsuperuser

Now you can go to https://localhost:8000 to see your local Lookit server! You should be able to log in using the superuser credentials you created during setup.

Going forward, you can run the server by navigating to the lookit-api directory, entering the virtualenv ($ pipenv shell), and typing $ invoke server.

If you are not working extensively with lookit-api - i.e., if you just want to make some new frames - you do not need to run celery, rabbitmq, or docker.

Running Celery and Rabbitmq

These tools handle deferred tasks like sending emails and generating large file downloads.

  • Run $ rabbitmq-server to start up the rabbitmq server.
  • Run $ invoke celery-service to start celery, which will talk to rabbitmq.

Running Docker

We use docker to build experiment runner images. If you are testing experiment builds, you will need to have Docker running - you can simply run $open /Applications/ or open it from Applications.


You can create participant and researcher accounts through the regular signup flow on your local instance. To access Experimenter you will need to add two-factor authentication to your account following the prompts. In order to access the admin interface (https://localhost:8000/__CTRL__), which provides a convenient way to access and edit records, you will need to log in using the superuser you created earlier using

Handling video

This project includes an incoming webhook handler for an event generated by the Pipe video recording service when video is transferred to our S3 storage. This requires a webhook key for authentication. It can be generated via our Pipe account and, for local testing, stored in project/settings/ as PIPE_WEBHOOK_KEY.

Pipe needs to be told where to send the webhook: You can use Ngrok to generate a public URL that /exp/renamevideo #TODO here

However, Pipe will continue to use the handler on the production/staging site unless you edit the settings to send it somewhere else (e.g., using ngrok to send to localhost for testing).

Common Issues

During installation, you may see the following:

psql: FATAL:  role "postgres" does not exist

To fix, run something like the following from your home directory:

$../../../usr/local/Cellar/postgresql/9.6.3/bin/createuser -s postgres

If your version of postgres is different than 9.6.3, replace with the correct version above. Running this command should be a one-time thing.

You might also have issues with the installation of pygraphviz, with errors like

running install
Trying pkg-config
Package libcgraph was not found in the pkg-config search path.
Perhaps you should add the directory containing `libcgraph.pc'
to the PKG_CONFIG_PATH environment variable
No package 'libcgraph' found


pygraphviz/graphviz_wrap.c:2954:10: fatal error: 'graphviz/cgraph.h' file not found
#include "graphviz/cgraph.h"
1 error generated.
error: command 'clang' failed with exit status 1

To fix, try running something like:

$ brew install graphviz
$ pip install --install-option="--include-path=/usr/local/include" --install-option="--library-path=/usr/local/lib" pygraphviz

Then re-run setup.