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 Parallelis
Model Parallelism 则是将模型参数进行拆分,每张显卡仅存放部分参数。具体来讲,Model Parallelism 又可以进一步分为Pipeline Parallelism、 Tensor Parallelism等,这里对其进行简单介绍。 1D Tesor Parallelism 示意图 (1) Pipeline Parallelism:如果单卡能放得下完整的层 如上图所示,Pipeline Parallelism 是将模型按照层...
换成"data/model parallelism", 这里一个组是一个cpu或者一个gpu。第一个方案是data parallelism，第二...
Then I want to use data parallelism and do not use model parallelism, just like DDP. The load_in_8bit option in .from_pretrained() requires setting device_map option. With device_map='auto', it seems that the model is loaded on several gpus, as in naive model parallelism, which ...
Hi, thanks! I use vllm to inference the llama-7B model on single gpu, and tensor-parallel on 2-gpus and 4-gpus, we found that it is 10 times faster than HF on a single GPU, but using tensor parallelism, there is no significant increase i...
The SageMaker AI distributed data parallelism (SMDDP) library is a collective communication library and improves compute performance of distributed data parallel training.
Many techniques have been proposed on the data parallel model, two of them are: nested data parallelism approach and the pipeline parallelism. The nested data parallelism is characterized by dividing the problems into sub-problems that are of the same structure as the larger problem. Further ...
Data parallelism involves the use of different nodes to run the same portion of code on different batches of data. Model parallelism involves the development of more sophisticated models that distribute the computation of different model subparts among different worker nodes. Currently, the limiting fac...
Apache Spark enables the development of large-scale machine learning algorithms where data parallelism or model parallelism is essential [61]. These iterative algorithms can be handled efficiently by Spark core which is designed for efficient iterative computations. Implementing machine learning algorithms ...
dataparallelism ChrisOlston Yahoo!Research set-orientedcomputation datamanagementoperationstendtobe“set-oriented”,e.g.: applyf()toeachmemberofaset computeintersectionoftwosets easytoparallelize paralleldatamanagementisparallelcomputing’sbiggestsuccessstory ...