Apache Mesos backend for Dask scheduling library. Run distributed python dask workflows on your Mesos cluster.

Notable Features

• distributively run tasks in docker container
• specify resource requirements per task
• bin packing for optimized resource utilization

Installation

Prerequisits: satyr, dask, toolz. All of them should be installed w/ the following commands:

pip install dask.mesos or use lensa/dask.mesos Docker image

Configuration:

• MESOS_MASTER=zk://127.0.0.1:2181/mesos
• ZOOKEEPER_HOST=127.0.0.1:2181

Example

from __future__ import absolute_import, division, print_function

from daskos import mesos, MesosExecutor


@mesos(cpus=0.1, mem=64)
def add(x, y):
    """Run add on mesos with specified resources"""
    return x + y


@mesos(cpus=0.3, mem=128, image='lensa/daskos')
def mul(x, y):
    """Run mul on mesos in specified docker image"""
    return x * y


with MesosExecutor(name='dask') as executor:
    """This context handles Mesos scheduler's lifecycle"""
    a, b = 23, 89
    alot = add(a, b)
    gigalot = mul(alot, add(10, 2))

    gigalot.compute(get=executor.get)  # (a + b) * (10 + 2)
    executor.compute([alot, gigalot])  # list of futures

Configuring daskos Tasks

You can configure your mesos tasks in your decorator, currently the following options are available:

• cpus: The amount of cpus to use for the task.
• mem: Memory in MB to reserver for the task.
• disk: The amount of disk to use for the task.
• image: A docker image name. If not set, mesos containerizer will be used.

Both mem and cpus are defaults to some low values set in satyr so you're encouraged to override them here. More options like constraints, other resources are on the way.