scaled 0.9

Scale Distribution Framework

Scaled

This project is aiming the target that provides simple and efficient and reliable way for distributing computing framework, centralized scheduler and stable protocol when client and worker talking to scheduler

Introduction

The goal for this project should be as simple as possible

• It built on top of zmq
• it has ready python version of Client, Scheduler, Worker
• I will provide golang or Rust version of Scheduler, the goal for the Scheduler should be completely computer language agnostic, which means they follow the same protocol
• Scheduler might support function based computing tree in the future

Installation

pip install scaled

if you want to use uvloop, please do: pip install uvloop, default we are using python builtin uvloop

How to use it

Start local scheduler and cluster at the same time in the code

import random

from scaled.client import Client
from scaled.cluster.combo import SchedulerClusterCombo


def calculate(sec: int):
    return sec * 1


def main():
    address = "tcp://127.0.0.1:2345"

    cluster = SchedulerClusterCombo(address=address, n_workers=10, event_loop="uvloop")
    client = Client(address=address)

    tasks = [random.randint(0, 100) for _ in range(100000)]
    futures = [client.submit(calculate, i) for i in tasks]

    results = [future.result() for future in futures]

    assert results == tasks

    client.disconnect()
    cluster.shutdown()


if __name__ == "__main__":
    main()

Start scheduler and cluster independently

use scaled_scheduler to start scheduler, for example:

scaled_scheduler --allocator-type queued tcp://0.0.0.0:8516

use scaled_cluster to start workers:

scaled_worker -n 10 tcp://127.0.0.1:8516

Then you can write simply write client code as:

from scaled.client import Client


def foobar(foo: int):
    return foo


client = Client(address="tcp://127.0.0.1:2345")
future = client.submit(foobar, 1)

print(future.result())