jug-schedule 0.2.2

Automatic DRMAA scheduling with resource management for Jug

jug_schedule is a jug subcommand that provides automatic deployment to queueing systems.

Currently supports SGE/GE (Grid Engine), LSF (IBM LSF), and SLURM.

This project is currently experimental so bug reports are welcome.

Requirements

This project depends on drmaa and obviously jug.

Installation

..note ::

Until jug 1.5.0 is released you will need to use jug and jug_schedule from git. Alternatively you can use the 1.4.0+git version of jug from my anaconda channel with conda install -c Unode jug-schedule.

Install jug_schedule with:

pip install jug-schedule

and then simply add the following to your ~/.jug/config/jug_user_commands.py:

try:
    from jug_schedule.schedule import ScheduleCommand
    schedule = ScheduleCommand()
except Exception as e:
    sys.stderr.write("Couldn't import schedule, error was {0}\n".format(e))

If you are running directly from git you can instead use:

import os
import sys

sys.path.insert(0, "/path/to/clone/of/jug_schedule/")

try:
    from jug_schedule.schedule import ScheduleCommand
    schedule = ScheduleCommand()
except Exception as e:
    sys.stderr.write("Couldn't import schedule, error was {0}\n".format(e))

Usage

If installed properly, running jug should now include a schedule subcommand.

Running it will try to detect a queueing system and submit jobs to it. jug schedule will only produce warning and errors. Use --verbose debug for detailed messages.

jug status will behave as usual and is the recommended way to check progress of execution.

Configuration

jug_schedule relies on DRMAA for interaction with the queueing system.

DRMAA support is limited and its quality varies considerably across platforms.

Currently supported platforms include LSF, SGE and SLURM with some limitations on SLURM.

In order to use jug_schedule your environment needs to define DRMAA_LIBRARY_PATH. If running env | grep DRMAA_LIBRARY_PATH returns no match, ask your system administrators for the location of this library.

Then use:

export DRMAA_LIBRARY_PATH=/path/to/libdrmaa.so

You only need to set this option on the environment that runs jug schedule.

Note

You can also use contrib/find_libdrmaa to locate libdrmaa.so on your system. Check the README inside the contrib/ folder for more information.

Resources

An additional feature of jug_schedule is the ability to define job resources.

If you already know jug’s TaskGenerator decorator you can simply replace it with the following where applicable:

from jug_schedule.resources import ResourcesTaskGenerator

@ResourcesTaskGenerator(cpu=10, mem=10, queue="default")
def func(...):
    ...

Supported arguments include: cpu, mem (in MB) and queue.

Command-line options

The following options are available:

--script            - command used to run jug on the cluster. Point this to a shell script if you need to setup jug's environment prior to execution
--max-jobs          - how big is the pool of jug jobs (max number of simultaneous jobs)
--logs              - where to write job logs. Defaults to a directory 'jug_logs' in the current directory.
--cycle-time        - how many seconds to wait between every interaction with the queue system. Defaults to 60
--continue-on-error - jug_schedule will continue until all jobs fail. Default is to stop queueing jobs when a job fails.