vivarium-cluster-tools 3.1.6

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

NOTE: This repository has been archived.

The vivarium_cluster_tools package has been renamed and migrated into the vivarium-suite monorepo.

What changed

• Import path: vivarium_cluster_tools -> vivarium.cluster_tools

• Source: ihmeuw/vivarium_cluster_tools (archived) -> ihmeuw/vivarium-suite (under libs/cluster-tools/)

To migrate fully to the new package

Install:

pip install vivarium-cluster-tools  # no change here

Import:

import vivarium.cluster_tools  # was: import vivarium_cluster_tools

Original package overview

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

pip install vivarium-cluster-tools

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.

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.

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

plugins:
  optional: ...

components:
  vivarium_public_health:
    population:
      - BasePopulation()
    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

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!