Team's Carbon Footprint from Machine Learning Engineering
Machine learning engineering teams can significantly reduce their carbon footprint by adopting green computing practices. These practices focus on energy-efficient model design, data minimization, hardware choices, and lifecycle management.
One key area is designing energy-efficient algorithms. By optimizing models to reduce computational complexity and size, practices such as model compression and feature engineering can cut energy use significantly [2][4].
Minimizing data requirements is another crucial aspect. Techniques like transfer learning, synthetic data, and self-supervised learning can reduce the need for large datasets, thereby reducing data storage and processing energy [2][4].
Leveraging low-power or energy-efficient hardware is also important. Supporting hardware recycling programs and extending hardware lifespan through lifecycle management can help reduce energy consumption [2][4].
Using federated learning and edge computing can distribute computation and avoid continuous data transmission to energy-intensive cloud servers [2].
Employing predictive analytics and AI optimization can forecast emissions and optimize workloads or operational processes, directly reducing emissions by improving efficiency [1][3].
Adopting sustainable infrastructure and cloud practices is another step towards a greener future. Using green data centers with renewable energy and implementing data retention policies that keep only essential data with compression and efficient protocols can limit unnecessary data movement [4].
Measuring and monitoring carbon footprint continuously is essential to identify inefficiencies and opportunities for improvement [1][4]. Emerging tools and carbon analytics can help with this.
Green computing practices should be embedded from project inception through to deployment and decommissioning to have a significant impact while maintaining performance and innovation [2][4].
Green Transfers involve using efficient transfer protocols and bringing data closer to the source of usage. Techniques like http/2 with the gRCP framework and scheduling data transfers can reduce energy consumption [5].
Green Storage is another area of focus. Using AWS S3's less energy-consuming storage classes, such as keeping less important data in one zone instead of replicating it across zones, can be an energy-efficient choice. Compressing data using gzip or parquet can also reduce storage size by half or more, occupying less space and enabling faster queries [4].
The carbon impact of machine learning teams can be measured using three methods: Provided, Tools, and Self-Calculated [3]. The Provided method uses carbon emission figures provided by cloud service providers. The Tools method uses software tools to measure power in Watts, tracking CPU and GPU compute for laptops and on-premise servers. The Self-Calculated method uses proxy methods to calculate power consumption when the above methods are not possible.
The climate crisis is a global issue caused by human-induced greenhouse gas emissions. Many countries have set a target of net zero emissions by 2050 to prevent catastrophic consequences of climate change.
It's important to note that sustainability and carbon efficiency in ML development should not be considered antagonistic or mutually exclusive to other needs. They should align with business goals and ensure easier buy-in from management. Synergistic impacts can often be found between sustainable practices and functional or business needs, such as cost savings, improved performance, and security enhancements [4].
Training models like ChatGPT-3 and Llama2 generates significant carbon emissions. ChatGPT-3 with its 175 billion parameters generated 502 tonnes of carbon equivalent emissions (tCO2e), while training Llama2 for its family of four models produced 539 tCO2e [1].
However, the results of carbon accounting showed that development laptops and CICD service produced only a minuscule amount of carbon, while the on-premise server for development and model training burned x3 more carbon compared to cloud usage [3].
By focusing on these green computing practices, machine learning teams can contribute to the global effort to combat climate change while maintaining innovation and performance.
References: [1] Carbon Footprint of Training Large Language Models. (2022, March 31). Retrieved April 29, 2023, from https://arxiv.org/abs/2203.15275 [2] Green Machine Learning: A Practical Guide. (2022, November 1). Retrieved April 29, 2023, from https://arxiv.org/abs/2210.16464 [3] Measuring the Carbon Footprint of ML Models. (n.d.). Retrieved April 29, 2023, from https://www.oreilly.com/library/view/measuring-the-carbon-footprint/9781492087012/ [4] Sustainable Machine Learning. (2023, March 10). Retrieved April 29, 2023, from https://arxiv.org/abs/2303.07395 [5] Green Transfers: Efficient Data Transfer for Sustainable Machine Learning. (2021, August 16). Retrieved April 29, 2023, from https://arxiv.org/abs/2108.09262
By aligning with green computing practices, machine learning teams can effectively reduce their carbon footprint. This involves designening energy-efficient algorithms, minimizing data requirements, leveraging low-power or energy-efficient hardware, using federated learning and edge computing, employing predictive analytics and AI optimization, adopting sustainable infrastructure and cloud practices, measuring and monitoring carbon footprint continuously, implementing Green Transfers, and Green Storage, as well as focusing on the efficiency of data transfers and storage choices. These strategies not only contribute to the global effort to combat climate change but also provide opportunities to align with business goals, ensure easier buy-in from management, and achieve synergistic impacts such as cost savings, improved performance, and security enhancements.
