Add to ORKGThe Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. There is an urgent demand to train models in a distributed environment. However, distributed training, especially model parallelism, often requires domain expertise in computer systems and architecture. It remains a challenge for AI researchers to implement complex distributed training solutions for their models. In this paper,...
The field of deep learning has been the focus of plenty of research and development over the last y...
As recent research demonstrates, the trend in model size across deep learning has rapidly increased,...
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...
In recent years, the number of parameters of one deep learning (DL) model has been growing much fast...
The scaling up of deep neural networks has been demonstrated to be effective in improving model qual...
This thesis is done as part of a service development task of distributed deep learning on the CSC pr...
Alpa automates model-parallel training of large deep learning (DL) models by generating execution pl...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
Transformer models have achieved state-of-the-art performance on various domains of applications and...
Scaling up model depth and size is now a common approach to raise accuracy in many deep learning (DL...
In this course, we will cover machine learning and deep learning and how to achieve scaling to high ...
Deep learning algorithms base their success on building high learning capacity models with millions ...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
Deep neural networks have gained popularity in recent years, obtaining outstanding results in a wide...
Neural networks are becoming more and more popular in scientific field and in the industry. It is mo...
The field of deep learning has been the focus of plenty of research and development over the last y...
As recent research demonstrates, the trend in model size across deep learning has rapidly increased,...
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...
In recent years, the number of parameters of one deep learning (DL) model has been growing much fast...
The scaling up of deep neural networks has been demonstrated to be effective in improving model qual...
This thesis is done as part of a service development task of distributed deep learning on the CSC pr...
Alpa automates model-parallel training of large deep learning (DL) models by generating execution pl...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
Transformer models have achieved state-of-the-art performance on various domains of applications and...
Scaling up model depth and size is now a common approach to raise accuracy in many deep learning (DL...
In this course, we will cover machine learning and deep learning and how to achieve scaling to high ...
Deep learning algorithms base their success on building high learning capacity models with millions ...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
Deep neural networks have gained popularity in recent years, obtaining outstanding results in a wide...
Neural networks are becoming more and more popular in scientific field and in the industry. It is mo...
The field of deep learning has been the focus of plenty of research and development over the last y...
As recent research demonstrates, the trend in model size across deep learning has rapidly increased,...
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...