Metadata-Version: 2.1
Name: ddpw
Version: 1.0.0
Summary: A utility package to scaffold PyTorch's DDP
Home-page: http://ddpw.projects-tvs.surge.sh
Author: Sujal T.V.
License: UNKNOWN
Description: # DDPW
        
        [![Publish documentation to Surge](https://github.com/sujaltv/ddpw/actions/workflows/surge_publish.yaml/badge.svg)](https://github.com/sujaltv/ddpw/actions/workflows/surge_publish.yaml)
        [![Publish to Anaconda](https://github.com/sujaltv/ddpw/actions/workflows/conda_publish.yaml/badge.svg)](https://github.com/sujaltv/ddpw/actions/workflows/conda_publish.yaml)
        [![Publish to PyPI](https://github.com/sujaltv/ddpw/actions/workflows/pypi_publish.yaml/badge.svg)](https://github.com/sujaltv/ddpw/actions/workflows/pypi_publish.yaml)
        
        The Distributed Data Parallel wrapper (DDPW) is created as a utility package to
        encapsulate the scaffolding for PyTorch's Distributed Data Parallel.
        
        This code is written in Python 3.8. The [DDPW
        documentation](http://ddpw.projects-tvs.surge.sh) contains details on how to use
        this package.
        
        ## Overview
        
        ### Installation
        
        ```bash
        conda install -c tvsujal ddpw # with conda
        pip install ddpw # with pip from PyPI
        ```
        
        ### Usage
        
        ```python
        from ddpw import DDPWrapper, Platform
        
        job = DDPWrapper(platform=Platform.GPU, nprocs=4, ...) # train on 4 GPUs
        job.start(epoch=30) # start training
        job.resume(ckpt=20, epochs=60) # resume training from 20th epoch
        e = job.evaluate(ckpt=50) # evaluate the model saved at 50th epoch
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.8
Description-Content-Type: text/markdown
