In a significant stride forward, Alibaba Cloud Machine Learning Platform for AI (PAI) has jointly penned a groundbreaking paper with Zhi Yang from Peking University, entitled “GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning”. This transformative study has been accepted by SIGMOD 2023, presenting promising enhancements in training performance and cluster resource utilization efficiency through elastic scaling of the deep learning data pre-processing pipeline. 📚
SIGMOD, an esteemed international conference in the database and data management systems arena, has been revolutionizing database technology since its inception in 1975. With its focus on the convergence of data management systems with machine learning and artificial intelligence, SIGMOD’s recognition of this paper affirms PAI’s leadership in the deep learning data processing direction. 👩🔬🌐
Over recent years, advancements in GPU accelerators and novel software optimization technologies have driven the computing efficiency of deep learning training to unprecedented heights. Nevertheless, as deep learning inherently involves multi-stage, multi-resource tasks, data pre-processing pipelines have surfaced as a new performance bottleneck. These pipelines, though stateless and inherently elastic, necessitate data enhancement and feature conversion processes, which put increasing pressure on the GPU. 🎛️💻
“GoldMiner” addresses this bottleneck by separating the data pre-processing pipeline from the model training part. Through automatic computing graph analysis, stateless data pre-processing computing is identified and subsequently subjected to efficient parallel acceleration and elastic scaling. In partnership with the cluster scheduler, GoldMiner further leverages the resource elasticity of data pre-processing, markedly improving cluster scheduling efficiency. Results indicate that GoldMiner can boost training performance by up to 12.1x and GPU cluster utilization by up to 2.5x. 🔧📈
Currently, PAI is integrating GoldMiner with PAI-DLC, offering users enhanced data pre-processing and acceleration capabilities. As a lightweight and cost-effective cloud-native machine learning solution, PAI serves enterprise customers and developers across the board, from interactive modeling with PAI-DSW, visual modeling with PAI-Designer, and distributed training with PAI-DLC, to the online deployment of PAI-EAS models. This integration with GoldMiner is set to augment efficiency and reduce costs for developers and enterprises. 🏭💡
Additional Paper Details:
- Title: GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning
- Authors: Hanyu Zhao, Zhi Yang, Yu Cheng, Chao Tian, Shiru Ren, Wencong Xiao, Man Yuan, Langshi Chen, Kaibo Liu, Yang Zhang, Yong Li, and Wei Lin
- Access the Paper Here
