README.md

yatamana - yet another task manager

Yatamana simplifies submitting tasks to (HPC) clusters in a cluster-agnostic way. As of now, it supports Sun Grid Engine, Slurm, and local execution. Local execution is not parallelized and can be used for debugging. The plan is to add execution using Celery in the future.

Usage

1. Instantiate a subclass of TaskManager, e.g., SlurmTaskManager
2. Subclass Task setting its command field.
3. Ask the task manager to enqueue the task.

Runner script and task options are defined through a simple json setup file.

As of now, there is no automated serialization of the Task or its data. The Task is expected to prepare a command line to be run in its command field, which is then embedded inside a runner script submitted to the cluster.

Json setup file

Setup files have to contain at least a runner script template under runner.template key.

• runner.template - a list of lines of the runner script to use
• opts.tasks.SomeTask - options for a given class of tasks

Task options include:

• cores - number of requested cores
• current_working_directory - run in the current working directory [default]
• log_filename
• memory - requested memory in GB
• walltime - maximum runtime in minutes (integer) or: "minutes" "minutes:seconds" "hours:minutes:seconds" "days-hours" "days-hours:minutes" "days-hours:minutes:seconds"
• raw - raw options passed to the submission process as they are

Minimal example

setup.json:

{
  "runner": {
    "template": [
      "#!/bin/bash",
      "%(command)s"
    ]
  },
  "tasks": {
    "SimpleTask": {
      "cores": 3
    }
  }
}

submit.py:

from yatamana import Task, SgeTaskManager

class SimpleTask(Task):
    def __init__(self):
        super(SimpleTask, self).__init__()
        self.command = ['echo Running on $(hostname) at $(date)']

if __name__ == '__main__':
    manager = SgeTaskManager('setup.json')
    manager.enqueue(SimpleTask())