haiscale (Highflyer AI Scale) is a lightweight, high-performance parallel training toolkit that integrates Huafang AI’s years of experience in parallel training research, development, and optimization, enabling PyTorch users to train models on large-scale clusters more efficiently and conveniently. haiscale includes the following tools: haiscale.ddp: a distributed data parallelism tool that uses hfreduce—a communication protocol developed in-house by Huanfang AI—as its backend, offering better multi-GPU scalability compared to NCCL.

The entire world is crunching the numbers on GPT’s training and inference costs, to the point where Microsoft couldn’t wait to share the good news that “inference delivers twice the performance per dollar.” The message this conveys is perhaps this: even the slightest waste of computing power can be fatal in this AI race. As this field continues to evolve, the efficiency of model training is increasingly becoming a challenge that researchers cannot afford to ignore. Whether from the perspective of accelerating training to test more parameters or reducing R&D costs, inefficient training has become unacceptable. What researchers need now is a set of user-friendly model performance analysis tools,

“Don’t miss the defining moment of AI”—this NVIDIA GTC tagline has been trending across the internet since Wednesday. As is customary, the conference featured a series of cutting-edge technology announcements by the familiar face of Jensen Huang, focusing on AI, chips, and other technologies. This year, Huanfang AI was once again invited to deliver a technical presentation at the NVIDIA GTC 2023 conference. Since 2019, to meet the massive computing power demands of our own research work, we have gradually built the Huanfang Firefly Deep Learning Computing Platform, which includes storage, networking, and related infrastructure, as well as time-sharing scheduling.

With the rapid development of large AI models, many companies have begun building large-scale deep learning training clusters based on GPUs. As computing power demands grow, an AI platform capable of centrally and efficiently managing GPU resources is increasingly becoming a key tool for AI teams to reduce costs and improve efficiency. Building an AI platform capable of managing the resources of a large-scale GPU cluster primarily involves addressing the following challenges: Resource Scheduling: As computing power continues to expand and the computational demands of training tasks vary widely, how can the relationship between tasks and computing power be managed to maximize cluster resource utilization?