Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models

TL;DR

This paper explores methods to measure and reduce energy consumption in training and inference of language models, emphasizing hardware tuning and power management to achieve sustainability.

Contribution

It introduces practical techniques for energy reduction in NLP, including power-capping and hardware adjustments, validated through experiments on HPC and cloud platforms.

Findings

Power-capping reduces energy use by 15% with minimal performance impact.

Hardware tuning can significantly lower energy consumption during training.

Energy-efficient settings maintain model performance while reducing environmental impact.

Abstract

The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP and machine learning more broadly. In this article, we investigate techniques that can be used to reduce the energy consumption of common NLP applications. In particular, we focus on techniques to measure energy usage and different hardware and datacenter-oriented settings that can be tuned to reduce energy consumption for training and inference for language models. We characterize the impact of these settings on metrics such as computational performance and energy consumption through experiments conducted on a high performance computing system as well as popular cloud computing platforms. These techniques can lead to significant reduction in energy consumption when training language models or their use for inference. For example, power-capping, which limits the maximum power a GPU can consume, can enable a 15\% decrease in energy usage with marginal increase in overall computation time when training a transformer-based language model.