Volume 1 Issue 2 | 2024 | View PDF
Paper Id: IJMSM-V1I2P105
doi: 10.64137/30485037/V1I2P105

Green ML: Designing Energy-Efficient Machine Learning Pipelines at Scale

Guru Pramod Rusum

Citation:
Guru Pramod Rusum, "Green ML: Designing Energy-Efficient Machine Learning Pipelines at Scale" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 2, pp. 49-61, 2024.

Abstract:
Indeed, the energy consumption behind Machine Learning (ML) models and applications is surpassing at an astonishing rate as the demand for said Machine Learning (ML) models and applications increases exponentially. Such a growth is troubling, given the ecological costs of training large-scale models like GPT, BERT and ResNet. The goal of this paper is to describe a systematic methodology that we have developed to design energy-efficient ML pipelines, maximizing performance and minimizing energy consumption. In this paper, we introduce Green ML, which provides such a methodology. We discuss major principles of design, optimization, and those interventions that could be performed at the system level to enforce sustainability throughout the ML lifecycle, including data preprocessing and training, deployment, and inference. The paper involves energy-aware sampling of data and Quantization-Aware Training (QAT) data, Neural Architecture Search (NAS), and hardware information, as well as model pruning. We also examine the effect of different training frameworks and hardware accelerators on energy efficiency. We benchmark conventional and optimized pipelines, using a complete benchmarking suite regarding energy consumption, carbon footprint, and precision. We develop a modular framework that enables the creation of energy-efficient ML architectures and facilitates empirical studies, demonstrating that up to a 60% energy reduction can be achieved with a less than 5% decrease in model accuracy. A detailed overview of recent sustainable ML techniques, their comparative effectiveness, and recommendations for developing a green ML pipeline for the future are also included in the paper. What we do is a push towards introducing energy-efficient ideas at the industry level and assisting policymakers in developing green AI policies.

Keywords: Green ML, Machine Learning Pipelines, Quantization, Model Pruning, Hardware-aware NAS.

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