virtual-machine threshold scaling autoscale autoscaling resource-manager proxmox proxmox-infrastructure Updated Jan 21, 2025 Python sileod / tasksource Star 175 Code Issues Pull requests Datasets collection and preprocessings framework for NLP extreme multitask learning nlp benchmark text-classificatio...
Dealing with packet loss Switch-side Worker-side Dealing with floating-point numbers 定点数与浮点数的表示方法 Mixed Precision 论文中的方法 Scaling Distributed Machine Learning with In-Network Aggregation 摘要 此片论文主要设计了一种交换机SwitchML,通过将网络上多个worker的模型更新在交换机中aggregate来降低...
文章是李沐发表的关于机器学习系统的文章,提出了业界较为通用的经典机器学习/深度学习算法的大规模分布式系统架构——参数服务器(Parameter Server)。 大体上看,ps是一个数据并行的系统框架,数据和梯度计算都放在worker节点上,而server节点负责保存和维护全局共享的参数,其中,参数以稀疏/稠密的向量/矩阵表示。该框架通过...
Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of ...
副标题:Distributed ML with MLlib, TensorFlow, and PyTorch 出版年:2023-4-1 页数:291 定价:USD 77.89 装帧:Paperback ISBN:9781098106829 豆瓣评分 评价人数不足 评价: 写笔记 写书评 加入购书单 分享到 推荐 内容简介· ··· Learn how to build end-to-end scalable machine learning solutions with Apac...
The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a numerical optimization problem. In this context, Stochast... J Keuper,Pfreundt, Franz-Josef - 《Computer Science》 被引量: 18发表: 2015年 Scaling Bayesian Network Parameter Learning with Expectation...
Scaling distributed machine learning with the parameter server Big data may contain big values, but also brings lots of challenges to the computing theory, architecture, framework, knowledge discovery algorithms, and domain specific tools and applications. Beyond the 4-V or 5-V characters of big ...
本期论文:Scaling Distributed Machine Learning with the Parameter Server 背景 参数服务器是一种编程框架,用于简化分布式机器学习程序的编写,其中重点在于对大规模参数的分布式存储和协同的支持。 机器学习任务相比于其他计算任务而言,具有以下特点: 迭代性:模型的更新并非一次完成,需要多次迭代 ...
with a larger value and providing longer fine-tuning text. In this work, we first observe that fine-tuning a RoPE-based LLM with either a smaller or larger base in pre-training context length could significantly enhance its extrapolation performance. After that, we propose \textbf{\textit{Scal...
when data fits in or exceeds RAM (we tested datasets up to 190GB); (2) an approach, called M3, that enables existing machine learning algorithms to work with out-of-core datasets through memory mapping, achieving a speed that is significantly faster than a 4-instance Spark cluster, and co...