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vivarium-cluster-tools

A set of tools for running simulation using vivarium on cluster.

  • 2.1.0
  • PyPI
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Maintainers
6

Vivarium Cluster Tools

.. image:: https://badge.fury.io/py/vivarium-cluster-tools.svg :target: https://badge.fury.io/py/vivarium-cluster-tools

.. image:: https://github.com/ihmeuw/vivarium_cluster_tools/actions/workflows/build.yml/badge.svg?branch=main :target: https://github.com/ihmeuw/vivarium_cluster_tools :alt: Latest Version

.. image:: https://readthedocs.org/projects/vivarium-cluster-tools/badge/?version=latest :target: https://vivarium-cluster-tools.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status

Vivarium cluster tools is a python package that makes running vivarium simulations at scale on a Slurm cluster easy.

Installation

You can install this package with

.. code-block:: console

pip install vivarium-cluster-tools

In addition, this tool needs the redis client. This must be installed using conda.

.. code-block:: console

conda install redis

A simple example

If you have a vivarium model specifcation file defining a particular model, you can use that along side a branches file to launch a run of many simulations at once with variations in the input data, random seed, or with different parameter settings.

.. code-block:: console

psimulate run /path/to/model_specification.yaml /path/to/branches_file.yaml

The simplest branches file defines a count of input data draws and random seeds to launch.

.. code-block:: yaml

input_draw_count: 25
random_seed_count: 10

This branches file defines a set of simulations for all combinations of 25 input draws and 10 random seeds and so would run, in total, 250 simulations.

You can also define a set of parameter variations to run your model over. Say your original model specification looked something like

.. code-block:: yaml

plugins:
  optional: ...

components:
  vivarium_public_health:
    population:
      - BasePopulation()
      - Mortality()
    disease.models:
      - SIS('lower_respiratory_infections')
  my_lri_intervention:
    components:
      - GiveKidsVaccines()

configuration:
  population:
    population_size: 1000
    age_start: 0
    age_end: 5
  lri_vaccine:
    coverage: 0.2
    efficacy: 0.8

Defining a simple model of lower respiratory infections and a vaccine intervention. You could then write a branches file that varied over both input data draws and random seeds, but also over different levels of coverage and efficacy for the vaccine. That file would look like

.. code-block:: yaml

input_draw_count: 25
random_seed_count: 10

branches:
  lri_vaccine:
    coverage: [0.0, 0.2, 0.4, 0.8, 1.0]
    efficacy: [0.4, 0.6, 0.8]

The branches file would overwrite your original lri_vaccine configuration with each combination of coverage and efficacy in the branches file and launch a simulation. More, it would run each coverage-efficacy pair in the branches for each combination of input draw and random seed to produce 25 * 10 * 5 * 3 = 3750 unique simulations.

To read about more of the available features and get a better understanding of how to correctly write your own branches files,

Check out the docs! <https://vivarium-cluster-tools.readthedocs.io/en/latest/>_

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