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Analysing the carbon footprint of Differential privacy guarantees on Vanilla BERT ( modelled for news classification ), MNIST and the Cartpole Problem using Opacus. [ Accepted to appear at ICML 2021 - Workshop on Socially Responsible Machine Learning ]

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differential-privacy carbon-emissions carbon-footprint

dp-carbon-footprint's Introduction

Experiment and Environment Meta Info

DP-NLP - Analyzing Carbon Footprint of DP-BERT at different degrees of differential privacy; Modelled for Topic Classification

System Config:

PyTorch version: 1.7.0+cu92 CUDA used to build PyTorch: 9.2 ROCM used to build PyTorch: N/A OS: Ubuntu 18.04.5 LTS (x86_64)

GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 Clang version: 6.0.0-1ubuntu2 (tags/RELEASE_600/final) CMake version: version 3.12.0 Libc version: glibc-2.26 Python version: 3.7 (64-bit runtime) Python platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic Is CUDA available: True CUDA runtime version: 11.0.221 GPU models and configuration: GPU 0: Tesla T4 Nvidia driver version: 460.32.03

Scripts

1. DPLSTM_v2.ipynb: : DPLSTM for News Classification
2. BERTNewsClassificationDP.ipynb: : Privacy Variant Experiments with BERT
3. BERTNewsClassification.ipynb: : Non-Privacy Variant Experiments with BERT ( ~ epsilon = infinity ) 
4. Visualization.ipynb: Visualizer 

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