Distributed training is a popular method for training large neural networks efficiently by processing large amounts of data. However, meeting the requirements of different neural network models, computing resources, and their dynamic changes during a training job is a significant challenge. This challenge is even more significant in industrial applications and production environments.
The End-to-End Adaptive Distributed Training Framework
In this study, a systematic approach has been designed
FastMoE is a powerful distributed training system built on PyTorch that accelerates the training process of massive models with commonly used accelerators. This system is designed to provide a hierarchical interface to ensure the flexibility of model designs and the adaptability of different applications, such as Transformer-XL and Megatron-LM.
What is FastMoE?
FastMoE stands for Fast Mixture of Experts, a training system that distributes training for models across multiple nodes. Its primary
What is PipeTransformer?
PipeTransformer is a novel method for training artificial intelligence models, specifically Transformer models, in a distributed and efficient manner. The ultimate goal of PipeTransformer is to speed up the time it takes to train these models, which can be used for a variety of tasks, such as natural language processing and image recognition.
How Does PipeTransformer Work?
One of the key features of PipeTransformer is its use of an adaptive on-the-fly freeze algorith