The divvy configuration file

At the heart of divvy is a the divvy configuration file, or DIVCFG for short. This is a yaml file that specifies a user's available compute packages. Each compute package represents a computing resource; for example, by default we have a package called local that populates templates to simple run jobs in the local console, and another package called slurm with a generic template to submit jobs to a SLURM cluster resource manager. Users can customize compute packages as much as needed.

Configuration file priority lookup

When divvy starts, it checks a few places for the DIVCFG file. First, the user may may specify a DIVCFG file when invoking divvy either from the command line or from within python. If the file is not provided, divvy will next look file in the $DIVCFG environment variable. If it cannot find one there, then it will load a default configuration file with a few basic compute packages. We recommend setting the DIVCFG environment variable as the most convenient use case.

Customizing your configuration file

The easiest way to customize your computing configuration is to edit the default configuration file. To get a fresh copy of the default configuration, use divvy init custom_divvy_config.yaml. This will create for you a config file along with a folder containing all the default templates.

Here is an example divvy configuration file:

    submission_template: templates/local_template.sub
    submission_command: sh
    submission_template: templates/local_template.sub
    submission_command: sh
    submission_template: templates/slurm_template.sub
    submission_command: sbatch
    partition: develop
    submission_template: templates/slurm_template.sub
    submission_command: sbatch
    partition: bigmem

The sub-sections below compute_packages each define a compute package that can be activated. Divvy uses these compute packages to determine how to submit your jobs. If you don't specify a package to activate, divvy uses the package named default. You can make your default whatever you like. You can activate any other compute package on the fly by calling the activate_package function from python, or using the --package command-line option.

You can make as many compute packages as you wish, and name them whatever you wish. You can also add whatever attributes you like to the compute package. There are only two required attributes: each compute package must specify the submission_command and submission_template attributes.

The submission_command attribute

The submission_command attribute is the string your cluster resource manager uses to submit a job. For example, in our compute package named develop_package, we've set submission_command to sbatch. We are telling divvy that submitting this job should be done with: sbatch submission_script.txt.

The submission_template attribute

Each compute package specifies a path to a template file (submission_template). The template file provides a skeleton that divvy will populate with job-specific attributes. These paths can be relative or absolute; relative paths are considered relative to the DIVCFG file. Let's explore what template files look like next.

Template files

Each compute package must point to a template file with the submission_template attribute. These template files are typically stored relative to the divvy configuration file. Template files are taken by divvy, populated with job-specific information, and then run as scripts. Here's an example of a generic SLURM template file:

#SBATCH --job-name='{JOBNAME}'
#SBATCH --output='{LOGFILE}'
#SBATCH --mem='{MEM}'
#SBATCH --cpus-per-task='{CORES}'
#SBATCH --time='{TIME}'
#SBATCH --partition='{PARTITION}'
#SBATCH -m block
#SBATCH --ntasks=1

echo 'Compute node:' `hostname`
echo 'Start time:' `date +'%Y-%m-%d %T'`

srun {CODE}

Template files use variables (e.g. {VARIABLE}), which will be populated independently for each job. If you want to make your own templates, you should check out the default templates (in the submit_templates folder). Many users will not need to tweak the template files, but if you need to, you can also create your own templates, giving divvy ultimate flexibility to work with any compute infrastructure in any environment. To create a custom template, just follow the examples. Then, point to your custom template in the submission_template attribute of a compute package in your DIVCFG config file.


You may notice that the compute config file does not specify resources to request (like memory, CPUs, or time). Yet, these are required in order to submit a job to a cluster. Resources are not handled by the divcfg file because they not relative to a particular computing environment; instead they vary by pipeline and sample. As such, these items should be provided elsewhere.