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Research tools for communication-efficient deep learning

Project description

Research tools for communication-efficient deep learning, developed by Muhang Lan at USTC.

Latest version: 0.1.0

Installation

Install using pip

pip install deepcom

Check version number

import deepcom as dc
print(dc.__version__)

Function list

Basic tools

  • Batch processing with enumerating given argument values: python -m deepcom config.json

Deep learning perspective

  • Convert model parameters to a numpy array: model2params()

  • Load a numpy array as model parameters: params2model()

Communication perspective

  • Calculating mutual information: mutual_info()

Compression for training model

Compression for post-training model

  • SuRP algorithm as a sparse compression for Laplacian sequence: surp_algorithm()

Project details


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