Configuration¶

The Dallinger configuration module provides tools for reading and writing configuration parameters that control the behavior of an experiment. To use the configuration, first import the module and get the configuration object:

import dallinger

config = dallinger.config.get_config()

You can then get and set parameters:

config.get("duration")
config.set("duration", 0.50)

When retrieving a configuration parameter, Dallinger will look for the parameter first among environment variables, then in a config.txt in the experiment directory, and then in the .dallingerconfig file, using whichever value is found first. If the parameter is not found, Dallinger will use the default.

Built-in configuration¶

Built-in configuration parameters, grouped into categories:

General¶

mode unicode: Run the experiment in this mode. Options include debug (local testing), sandbox (MTurk sandbox), and live (MTurk).
logfile unicode: Where to write logs.
loglevel unicode: A number between 0 and 4 that controls the verbosity of logs, from debug to critical.
whimsical boolean: What’s life without whimsy? Controls whether email notifications sent regarding various experiment errors are whimsical in tone, or more matter-of-fact.

Recruitment (General)¶

auto_recruit boolean

A boolean on whether recruitment should be automatic.

browser_exclude_rule unicode - comma separated

A set of rules you can apply to prevent participants with unsupported web browsers from participating in your experiment.

recruiter unicode

The recruiter class to use during the experiment run. While this can be a full class name, it is more common to use the class’s nickname property for this value; for example mturk, cli, bots, or multi. NOTE: when running in debug mode, the HotAir (hotair) recruiter will always be used. The exception is if the --bots option is passed to dallinger debug, in which case the BotRecruiter will be used instead.

recruiters unicode - custom format

When using multiple recruiters in a single experiment run via the multi setting for the recruiter config key, recruiters allows you to specify which recruiters you’d like to use, and how many participants to recruit from each. The special syntax for this value is:

recruiters = [nickname 1]: [recruits], [nickname 2]: [recruits], etc.

For example, to recruit 5 human participants via MTurk, and 5 bot participants, the configuration would be:

recruiters = mturk: 5, bots: 5

Amazon Mechanical Turk Recruitment¶

aws_access_key_id unicode: AWS access key ID.
aws_secret_access_key unicode: AWS access key secret.
aws_region unicode: AWS region to use. Defaults to us-east-1.
ad_group unicode: Obsolete. See group_name.
assign_qualifications boolean: A boolean which controls whether an experiment-specific qualification (based on the experiment ID), and a group qualification (based on the value of group_name) will be assigned to participants by the recruiter. This feature assumes a recruiter which supports qualifications, like the MTurkRecruiter.
group_name unicode: Assign a named qualification to workers who complete a HIT.
qualification_blacklist unicode - comma seperated: Comma-separated list of qualification names. Workers with qualifications in this list will be prevented from viewing and accepting the HIT.
title unicode: The title of the HIT on Amazon Mechanical Turk.
description unicode: The description of the HIT on Amazon Mechanical Turk.
keywords unicode: A comma-separated list of keywords to use on Amazon Mechanical Turk.
lifetime integer: How long in hours that your HIT remains visible to workers.
duration float: How long in hours participants have until the HIT will time out.
us_only boolean: Controls whether this HIT is available only to MTurk workers in the U.S.
base_payment float: Base payment in U.S. dollars. All workers who accept the HIT are guaranteed this much compensation.
approve_requirement integer: The percentage of past MTurk HITs that must have been approved for a worker to qualify to participate in your experiment. 1-100.
organization_name unicode: Obsolete.

Preventing Repeat Participants¶

If you set a group_name and assign_qualifications is also set to true, workers who complete your HIT will be given an MTurk qualification for your group_name. In the future, you can prevent these workers from participating in a HIT with the same group_name by including that name in the qualification_blacklist configuration. These four configuration keys work together to create a system to prevent recuiting workers who have already completed a prior run of the same experiment.

Email Notifications¶

See Email Notification Setup for a much more detailed explanation of these values and their use.

contact_email_on_error unicode: The email address used as the recipient for error report emails, and the email displayed to workers when there is an error.
dallinger_email_address unicode: An email address for use by Dallinger to send status emails.
smtp_host unicode: Hostname and port of a mail server for outgoing mail. Defaults to smtp.gmail.com:587
smtp_username unicode: Username for outgoing mail host.
smtp_password unicode: Password for the outgoing mail host.

Deployment Configuration¶

database_url unicode: URI of the Postgres database.
database_size unicode: Size of the database on Heroku. See Heroku Postgres plans.
dyno_type unicode: Heroku dyno type to use. See Heroku dynos types.
redis_size unicode: Size of the redis server on Heroku. See Heroku Redis.
num_dynos_web integer: Number of Heroku dynos to use for processing incoming HTTP requests. It is recommended that you use at least two.
num_dynos_worker integer: Number of Heroku dynos to use for performing other computations.
host unicode: IP address of the host.
port unicode: Port of the host.
notification_url unicode: URL where notifications are sent. This should not be set manually.
clock_on boolean: If the clock process is on, it will perform a series of checks that ensure the integrity of the database.
heroku_team unicode: The name of the Heroku team to which all applications will be assigned. This is useful for centralized billing. Note, however, that it will prevent you from using free-tier dynos.
worker_multiplier float: Multiplier used to determine the number of gunicorn web worker processes started per Heroku CPU count. Reduce this if you see Heroku warnings about memory limits for your experiment. Default is 1.5

Choosing configuration values¶

When running real experiments it is important to pick configuration variables that result in a deployment that performs appropriately.

The number of Heroku dynos that are required and their specifications can make a very large difference to how the application behaves.

num_dynos_web: This configuration variable determines how many dynos are run to deal with web traffic. They will be transparently load-balanced, so the more web dynos are started the more simultaneous HTTP requests the stack can handle. If an experiment defines the channel variable to subscribe to websocket events then all of these callbacks happen on the dyno that handles the initial /launch POST, so experiments that use this functionality heavily receive significantly less benefit from increasing num_dynos_web. The optimum value differs between experiments, but a good rule of thumb is 1 web dyno for every 10-20 simultaneous human users.
num_dynos_worker: Workers are dynos that pull tasks from a queue and execute them in the background. They are optimized for many short tasks, but they are also used to run bots which are very long-lived. Each worker can run up to 20 concurrent tasks, however they are co-operatively multitasked so a poorly behaving task can cause all others sharing its host to block. When running with bots, you should always pick a value of num_dynos_worker` that is at least ``0.05*number_of_bots, otherwise it is guaranteed to fail. In practice, there may well be experiment-specific tasks that also need to execute, and bots are more performant on underloaded dynos, so a better heuristic is 0.25*number_of_bots.
dyno_type: This determines how powerful the heroku dyno that’s started is. It applies to both web and worker dyno types. The minimum recommended is standard-1x, which should be sufficient for experiments that do not rely on real-time coordination, such as Bartlett (1932), stories. Experiments that require significant power to process websocket events should consider the higher levels, standard-2x, performance-m and performance-l. In all but the most intensive experiments, either dyno_type or num_dynos_web should be increased, not both.
redis_size: A larger value for this increases the number of connections available on the redis dyno. This should be increased for experiments that make substantial use of websockets. Values are premium-0 to premium-14. It is very unlikely that values higher than premium-5 are useful.
duration: The duration parameter determines the number of hours that an MTurk worker has to complete the experiment. Choosing numbers that are too short can cause people to refuse to work on a HIT. A deadline that is too long may give people pause for thought as it may make the task seem underpaid. Set this to be significantly above the total time from start to finish that you’d expect a user to take in the worst case.
base_payment: The amount of US dollars to pay for completion of the experiment. The higher this is, the easier it will be to attract workers.