A Activity diagram used in UML 6/9 and SysML B Bachman diagram Booch used in software engineering Block diagram Block Definition Diagram BDD used in SysML C Carroll diagram Cartogram Catalytic cycle Chemical equation Curly arrow diagram Category theory diagrams Cause-and-effect diagram Chord diagram Circuit diagram Class diagram from UML 1/9 Collaboration diagram from UML 2.0 Communication diagram from UML 2.0 Commutative diagram Comparison diagram Component diagram from UML 3/9 Composite structure diagram from UML 2.0 Concept map Constellation diagram Context diagram Control flow diagram Contour diagram Cordier diagram Cross functional flowchart D Data model diagram Data flow diagram Data structure diagram Dendrogram Dependency diagram Deployment diagram from UML 9/9 Dot and cross diagram Double bubble map used in education Drakon-chart E Entity-Relationship diagram ERD Event-driven process chain Euler diagram Eye diagram a diagram of a received telecommunications signal Express-G Extended Functional Flow Block Diagram EFFBD F Family tree Feynman diagram Flow chart Flow process chart Flow diagram Fusion diagram Free body diagram G Gantt chart shows the timing of tasks or activities used in project management Grotrian diagram Goodman diagram shows the fatigue data example: for a wind turbine blades H Hasse diagram HIPO diagram I Internal Block Diagram IBD used in SysML IDEF0 IDEF1 entity relations Interaction overview diagram from UML Ishikawa diagram J Jackson diagram K Karnaugh map Kinematic diagram L Ladder diagram Line of balance Link grammar diagram M Martin ERD Message Sequence Chart Mind map used for learning, brainstorming, memory, visual thinking and problem solving Minkowski spacetime diagram Molecular orbital diagram N N2 Nassi Shneiderman diagram or structogram a representation for structured programming Nomogram Network diagram O Object diagram from UML 2/9 Organigram Onion diagram also known as "stacked Venn diagram" P Package diagram from UML 4/9 and SysML Parametric diagram from SysML PERT Petri net shows the structure of a distributed system as a directed bipartite graph with annotations Phylogenetic tree - represents a phylogeny evolutionary relationships among groups of organisms Piping and instrumentation diagram P&ID Phase diagram used to present solid/liquid/gas information Plant Diagram Pressure volume diagram used to analyse engines Pourbaix diagram Process flow diagram or PFD used in chemical engineering Program structure diagram R Radar chart Radial Diagram Requirement Diagram Used in SysML Rich Picture R-diagram Routing diagram S Sankey diagram represents material, energy or cost flows with quantity proportional arrows in a process network. Sentence diagram represents the grammatical structure of a natural language sentence. Sequence diagram from UML 8/9 and SysML SDL/GR diagram Specification and Description Language. SDL is a formal language used in computer science. Smith chart Spider chart Spray diagram SSADM Structured Systems Analysis and Design Methodology used in software engineering Star chart/Celestial sphere State diagram are used for state machines in software engineering from UML 7/9 Swim lane Syntax diagram used in software engineering to represent a context-free grammar Systems Biology Graphical Notation a graphical notation used in diagrams of biochemical and cellular processes studied in Systems biology System context diagram System structure Systematic layout planning T Timing Diagram: Digital Timing Diagram Timing Diagram: UML 2.0 TQM Diagram Treemap U UML diagram Unified Modeling Language used in software engineering Use case diagram from UML 5/9 and SysML V Value Stream Mapping Venn diagram Voronoi diagram W Warnier-Orr Williot diagram Y Yourdon-Coad see Edward Yourdon, used in software engineering
我们知道在训练模型时，gpu的训练速度固然重要，但是当显存小于我们想要训练的模型大小时，gpu再快也难以训练。 这时候我们就要使用一些特殊的方式来将显存的需求降低。 而在pyTorch中，我们就有这么一个官方已经为我们实现好了的方法，那就是本文的主题 ...
文章浏览阅读2w次，点赞29次，收藏96次。 本文详细介绍如何在PyTorch中使用单GPU或多GPU进行模型训练，包括指定GPU、保存和加载模型的方法，以及在GPU和CPU间迁移模型的具体步骤。
本文将针对如何使用PyTorch在多张显卡上加载模型进行介绍，并配以代码示例和图表来帮助理解。 一、背景介绍 在使用单张显卡训练大规模模型时，可能会遇到显存不足、训练速度慢等问题。 为了克服这些挑战，多显卡并行计算是一种有效的解决方案。
文章浏览阅读2.3w次，点赞27次，收藏101次。 本文详细介绍如何在PyTorch中利用多GPU进行模型训练，包括模型和数据放置、损失计算及反向传播处理、模型保存与加载等关键步骤，并提供常见问题解决方案。
在本文中，我们将介绍如何在PyTorch中使用多个GPU来加速深度学习模型的训练和推断。 深度学习模型通常具有大量的参数和复杂的计算图，使用多个GPU可以显著减少训练时间和提高模型性能。 PyTorch提供了简单且灵活的方式来利用多个GPU来加速计算。
说明 在模型训练的时候，往往使用的是多GPU的环境；但是在模型验证或者推理阶段，往往使用单GPU甚至CPU进行运算。 那么中间有个保存和加载的过程。 下面来总结一下。
Pytorch 如何加载pytorch模型中的检查点文件 在本文中，我们将介绍如何在Pytorch模型中加载检查点文件。 通过加载检查点文件，我们可以恢复模型的训练状态，继续之前的训练进程，或者使用已训练好的模型进行推理。 阅读更多：Pytorch 教程 1. 什么是检查点文件?
在本教程中，我们将从一个单 GPU 训练脚本开始，并将其迁移到单节点上的 4 个 GPU 上运行。 在此过程中，我们将讨论分布式训练中的重要概念，并在代码中实现它们。
pytorch提供了两种多gpu训练的方式，方案一：利用 nn.DataParallel 实现，实现简单，不涉及多进程，另一种是用采用分布式并行训练 torch.nn.parallel.DistributedDataParallel 和 torch.utils.data.distributed.DistributedSampler 结合多进程实现。
pytorch提供了两种多gpu训练的方式，方案一：利用 nn.DataParallel 实现，实现简单，不涉及多进程，另一种是用采用分布式并行训练 torch.nn.parallel.DistributedDataParallel 和 torch.utils.data.distributed.DistributedSampler 结合多进程实现。
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