When model using device cuda:1 or cuda:2 or whatever (Anyway, it's not cuda:0) to inference model , Profile just occupy cuda:0 GPU memory forever. Found Pytorch documentation that torch.cuda.synchronize can select device. So torch.cuda.synchronize maybe use cuda:0 by default when the par...
WACV 2024 Fatih Ilhan, Ka-Ho Chow, Sihao Hu, Tiansheng Huang, Selim Furkan Tekin, Wenqi Wei, Yanzhao Wu, Myungjin Lee, Ramana Kompella, Hugo Latapie, Gaowen Liu, and Ling Liu, "Adaptive Deep Neural Network Inference Optimization with EENet," IEEE/CVF Winter Conference on Applications of ...
WACV 2024Fatih Ilhan,Ka-Ho Chow, Sihao Hu, Tiansheng Huang, Selim Furkan Tekin, Wenqi Wei, Yanzhao Wu, Myungjin Lee, Ramana Kompella, Hugo Latapie, Gaowen Liu, and Ling Liu, "Adaptive Deep Neural Network Inference Optimization with EENet,"IEEE/CVF Winter Conference on Applications of Comput...
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The phylogenetic tree was inferred by the Bayesian Inference (BI) method using MrBayes version 3.11 [29,30]. To expedite the construction of the phylogenetic tree, the GTR + I + G was selected as the best-fit nucleotide substitution model using jModelTest version 2.1.10 [31]. The BI tree...
论文10:Stealing part of a production language model 作者:Nicholas Carlini、Daniel Paleka、Krishnamurthy Dvijotham、Thomas Steinke、Jonathan Hayase、A. Feder Cooper、Katherine Lee、Matthew Jagielski、Milad Nasresfahani、Arthur Conmy、Eric Wallace、David Rolnick、Florian Tramer ...
作者:Lillian Lee 机构:康奈尔大学 论文链接:https://aclanthology.org/P99-1004.pdf 论文简介:作者研究了分布相似性度量,目的是提高对未见共现事件的概率估计。他们的贡献有三个方面:对一系列广泛的度量方法进行实证比较;基于它们所包含的信息对相似性函数进行分类;引入了一种新的函数,该函数在评估潜在代理分布方面...
import lightseq.inference as lsimodel = lsi.QuantTransformer(pb_path, batch_size)result = model.infer(input) 此外LightSeq 还提供了 BERT、GPT、ViT 等模型的 python 接口,分别调用 QuantBert、QuantGpt 和 QuanVit 即可体验。 梯度通信量化 LightSeq 支持 Transformer 模型的梯度通信量化[5],使用 Fairseq 或...
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[7] Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017). [8] Yuzuru Okajima and Kunihiko Sadamasa. 2019. Deep neural networks constrained by decision rules. In Proceedings of the AAAI Conference...