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Python Library to implement Simulations, built on and Django.

Project description

Simpl Modelservice

Python Library to implement Simulations, built on and Django.

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Django 2.2

$ pip install simpl-modelservice

Django 1.11

$ pip install "simpl-modelservice<0.8.0"

Setup development environment

$ git clone
$ cd simpl-modelservice
$ mkvirtualenv simpl-modelservice
$ pip install -r dev-requirements.txt
$ pip install -e .

Run tests

$ python

Development versioning

Install bumpversion:

$ pip install bumpversion

Then, to release a new version, increment the version number with:

$ bumpversion patch

Then push to the repo:

$ git push && git push --tags

View current WAMP subscriptions and registrations

Point your browser to http://localhost:8080/monitor and open your javascript console

How to run a modelservice as two separate processes

It's sometimes useful to run crossbar and your own model code as separate processes. By default, run_modelservice runs crossbar configured to kick off the sub-process run_guest. You can change this by doing these 3 simple steps:

  1. Get a copy of the currently in use crossbar configuration by running ./ run_modelservice --print-config. This will print the generated configuration file and then run normally. Simply cut-n-paste the configuration which will be a large JSON blob just before the usual Crossbar log messages.

  2. Edit the configuration to remove the entire {"type": "guest", ...} stanza, saved to a file.

  3. Run each piece separately. If we saved our configuration into config.json in the current directly this would look like:

     ./ run_modelservice --config=./config.json --loglevel info --settings=simpl-calc.settings

    for the crossbar service and then:

     HOSTNAME=localhost PORT=8080 ./ run_guest --settings=simpl-calc.settings

    for the modelservice itself.

Environment variables

  • GUEST_LOGLEVEL adjust guest process logging, defaults to info
  • CROSSBAR_LOGLEVEL adjust crossbar process logging, defaults to info


Writing tasks

Profiling tasks are defined in modelservice/profiles.

The profiler will run any method that starts with profile_ one or more times against a different number of workers.

Keep in mind that, unlike unit tests, profile tasks are not isolated.

Profile users

You can have workers that publish and call on WAMP as specific users. By using the .call() or .publish() method, it will call or publish as the user associated to that worker. To learn how to run workers associated to users, see Running profile users.


The modelservice.utils.instruments contains classes for measuring execution times. Check the module's docstrings for details.

Collecting results

You can collect the result of task by calling the .publish_stat() method:

    async def profile_random(self):
        with Timer() as timer:
            some_value = random.random()
        self.publish_stat('<unique stat name>', timer.elapsed, fmt='Average result was {stats.mean:.3f}')

The fmt string will receive an instruments.StatAggregator instance called stats. This object will collect the value from all workers that ran the task and will provide the following properties:

  • .min: The lowest collected value
  • .max: The highest collected value
  • .total: The sum of the collected values
  • .count: The number of collected values

Additionally, functions from the statistics module are aliased as properties (ie: .mean, .stdev, etc.).

Running the profiler anonymously

  1. Run simpl-games-api and its modelservice
  2. From any model, run its modelservice via run_modelservice or run_guest
  3. From the same model directory, call You can use -h for a list of options.

To run any model directory when logged into an AWS instance, call You can use -h for a list of options.

Running profile users

You can have the profiler spawn workers as specific users by passing a file with their emails using the -u option.

Assuming you have a file called myusers.txt with the following content:

You can then call:

$ -u myusers.txt

And it will spawn 3 workers, each of one set up to .call and .publish as that one of those users.

To run from any model directory when logged into an AWS instance, call:

$ -u myusers.txt

Both and invoke the profile management command.

Managing the Modelservice AWS Profiler instance

Django settings
  • PROFILER_AWS_KEY: the AWS IAM access key
  • PROFILER_AWS_SEC: the associated IAM secret key
Management commands

Management of the AWS profiler instance is performed by way of the aws_profiler management command.


:# ./ aws_profiler --status

i-0733d74785931f857: stopped -- type: c5.18xlarge -- ip address:


:# ./ aws_profiler --start

i-0733d74785931f857: pending -- type: c5.18xlarge -- ip address:


:# ./ aws_profiler --stop

i-0733d74785931f857: stopping -- type: c5.18xlarge -- ip address:

Copyright © 2018 The Wharton School,  The University of Pennsylvania 

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

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