Model parallelism has become necessary to train large neural networks.
H...
Distributed synchronized GPU training is commonly used for deep learning...
Recent expeditious developments in deep learning algorithms, distributed...
Mixture-of-Experts (MoE) models can achieve promising results with outra...
In this work, we construct the largest dataset for multimodal pretrainin...
Data parallelism (DP) has been a common practice to speed up the trainin...
Synchronized stochastic gradient descent (SGD) optimizers with data
para...
In this paper, we present BigDL, a distributed deep learning framework f...
Analysing sentiment of tweets is important as it helps to determine the
...