ddpw 5.4.0

A lightweight wrapper that scaffolds PyTorch's (Distributed Data) Parallel.

DDPW

Distributed Data Parallel Wrapper (DDPW) is a lightweight Python wrapper relevant for PyTorch users.

DDPW handles basic logistical tasks such as creating threads on GPUs/SLURM nodes, setting up inter-process communication, etc., and provides simple, default utility methods to move modules to devices and get dataset samplers, allowing the user to focus on the main aspects of the task. It is written in Python 3.13. The documentation contains details on how to use this package.

Overview

Installation

uv add ddpw # with uv

pip install ddpw # with pip

Examples

With the decorator wrapper

from ddpw import Platform, wrapper

platform = Platform(device="gpu", n_cpus=32, ram=64, n_gpus=4, verbose=True)

@wrapper(platform)
def run(*args, **kwargs):
    # global and local ranks, and the process group in:
    # kwargs['global_rank'], # kwargs['local_rank'], kwargs['group']

    ...

if __name__ == '__main__':
    run(...)

As a callable

from ddpw import Platform, Wrapper

# some task
def run(*args, **kwargs):
    # global and local ranks, and the process group in:
    # kwargs['global_rank'], # kwargs['local_rank'], kwargs['group']

# platform (e.g., 4 GPUs)
platform = Platform(device='gpu', n_gpus=4)

# wrapper
wrapper = Wrapper(platform=platform)

# start
wrapper.start(task, ('example',))

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Status