Skip to main content

futures for remote execution on clusters

Project description

This module provides a Python concurrent.futures executor that lets you run functions on remote systems in your HTCondor or Slurm cluster. Stop worrying about writing job files, scattering/gathering, and serialization—this module does it all for you.

It uses the cloudpickle library to allow (most) closures to be used transparently, so you’re not limited to “pure” functions.

Installation:

pip install clusterfutures

Usage:

import cfut
def square(n):
    return n * n

with cfut.SlurmExecutor() as executor:
    for result in executor.map(square, [5, 7, 11]):
        print(result)

See slurm_example.py and condor_example.py for further examples. The easiest way to get started is to ignore the fact that futures are being used at all and just use the provided map function, which behaves like itertools.imap but transparently distributes your work across the cluster.

Goals & design

clusterfutures is a simple wrapper to run Python functions in batch jobs on an HPC cluster. Each future corresponds to one batch job. The functions that you run through clusterfutures should normally run for at least a few seconds each: running smaller functions will be inefficient because of the overhead of launching jobs and moving data.

Functions, parameters and return values are sent by creating files; this assumes that the control process and the worker nodes have a shared filesystem. This mechanism is convenient for relatively small amounts of data; it’s probably not the best way to transfer large amounts of data to & from workers.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

clusterfutures-0.5.tar.gz (9.1 kB view details)

Uploaded Source

Built Distribution

clusterfutures-0.5-py3-none-any.whl (9.6 kB view details)

Uploaded Python 3

File details

Details for the file clusterfutures-0.5.tar.gz.

File metadata

  • Download URL: clusterfutures-0.5.tar.gz
  • Upload date:
  • Size: 9.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.0

File hashes

Hashes for clusterfutures-0.5.tar.gz
Algorithm Hash digest
SHA256 261e82c44b500e39b71e3a2c41db66d9777e9674d58449cb90aa83a1955e9453
MD5 a90c41b7a8ad8d5be30638330008dba0
BLAKE2b-256 9105667b80f05dfb2b175f8d36f3c737e5ae9cb6e65f0c0a2cea0d655d73c2c8

See more details on using hashes here.

File details

Details for the file clusterfutures-0.5-py3-none-any.whl.

File metadata

File hashes

Hashes for clusterfutures-0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 391c9258da445366b7e859ac8ed2883aecfd26de364959fc1910e1a7c63bb933
MD5 11ca7a908f1a5f6cd5277c8830ba80aa
BLAKE2b-256 8a0cb3357f3f7fe40009f24c2aac4ad6ae902071c5eff340c162f4c895751a20

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page