Rajbhandari S, Ruwase O, Rasley J, et al. ZeRO-Infinity: Breaking the GPU MemoryWall for Extreme Scale Deep Learning: SC' 21, November 14–19, 2021, St. Louis, MO, USA[C], 2021. 在传统的数据并行(Data Parallelism,DP)过程中,每个节点的都需要保存一份完成的网络模型和对应的参数,这导致...
分布式机器学习中的数据并行(Data Parallelism)和模型并行(model parallelism) 前言: 现在的模型越来越复杂,参数越来越多,其训练集也在剧增。在一个很大的数据集集中训练一个比较复杂的模型往往需要多个GPU。现在比较常见的并行策略有:数据并行和模型并行,本文主要讨论这两个并行策略。 数据并行(Data Parallelism): 在现...
In this post, I want to have a look at a common technique for distributing model training: data parallelism.It allows you to train your model faster by repli...
比如一个 TPU v2-8, 假设 batch_size=128, 那么TensorFlow自动把模型拷贝(replicate)到8个TPU芯片上,同时把128条训练样本,切分成8份,每份16个sample, 开始训练。 值得注意的是 TPU v2-512, 这种 TPU pod, 跟多台GPU服务器组成的一个集群不一样,除了服务器之间通过以太网链接,TPU chip也通过ICI连接成一个2D...
4. Henggang Cui, GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized ...
Hybrid parallelism techniques where a mix of data and model parallelism techniques are used to split the workload of a layer across an array of processors are disclosed. When configuring the array, the bandwidth of the processors in one direction may be greater than the bandwidth in the other ...
1. GPU与CPU集群上Model/Data Parallelism不存在本质上的区别，更多是存在工程细节上的区别。对这些工程...
The data parallelism provides greater efficiency under multiple-node systems. Under these circumstances, there is more and more utilisation on multiple GPUs for better efficiency for computation and lower cost of time for accomplishing the project. However, some typical, ordinary, and common circumstance...
— Chaim Rand, Machine Learning Algorithm Developer, Mobileye. Using sharded data parallelism to train GPT-2 on Amazon SageMaker Let’s now learn how to train a GPT-2 model with sharded data parallel, with SMP encapsulating the complexity for you. Thi...
Parallelism in stochastic gradient descent To understand how distributed data and model parallel works really means to understand how they work in the stochastic gradient descent algorithm that performs parameter learning (or equivalently, model training) of a deep neural network. Specifically, we need ...