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[ERR] Node 192.168.1.77:7000 is not empty. Either the node already knows other nodes (check with CLUSTER NODES) or contains some key in database 0.
解决方法：
1)、将需要新增的节点下aof、rdb、nodes.conf等本地备份文件删除；
2)、同时将新Node的集群配置文件删除,即：删除你redis.conf里面cluster-config-file所在的文件；
3)、再次添加新节点如果还是报错，则登录新Node,./redis-cli–h x –p对数据库进行清除：
172.168.63.201:7001> flushdb #清空当前数据库
通过以上解决方法中的1)、2)或者3)之后再执行脚本，成功执行；
OBD诊断协议：OBDⅡ (SAE J1979) 和 OBDonUDS (SAE J1979-2) 和ZEV OBD技术架构的详细对比
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Redis+阿里云远程连接
1.redis.conf,修改ip127.0.0.1为0.0.0.0 2.ps -ef | grep redis, kill -9 ID 3.重启redis服务 4.vi etc/sysconfig/iptabls，增加当前redis端口 5.service iptables retart 6.阿里云安全组增加开放此端口 done!...
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PaddleOCR是一个与OCR相关的开源项目，不仅支持超轻量级中文OCR预测模型，总模型仅8.6M(单模型支持中英文数字组合识别、竖排文本识别、长文本识别，其中检测模型DB(4.1M)+识别模型CRNN(4.5M))，而且提供多种文本检测训练算法(EAST、DB)和多种文本识别训练算法(Rosetta、CRNN、STAR-Net、RARE)。本文将教大家通过PaddleOCR(CPU版...
[ERR] Node is not empty. Either the node already knows other nodes
添加服务器，提示非空。解决办法。
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Node XXX is not empty. Either the node already knows other nodes or contains some key in database 0
关于启动redis集群或者集群（主、从）节点添加时：[ERR] Node 192.168.1.115:7007 is not empty. Either the node already knows other nodes (check with CLUSTER NODES) or contains some key in database 0.的问题1.启动redis集群报该错误解决办法： 1)
Dify社区版多工作空间功能解锁：源码修改与多租户架构解析
多租户架构是现代SaaS平台实现数据隔离与资源管理的核心技术，其核心原理是通过租户模型将用户、应用和数据在逻辑上隔离。在AI应用开发平台中，这种架构能有效支持多团队、多项目并行开发，确保不同业务线的数据安全与独立迭代。Dify作为开源AI应用构建平台，其底层已设计完善的多租户模型，但社区版默认限制了单用户单工作空间。本文通过精准修改两处关键代码——账户初始化逻辑与系统功能开关，释放了平台固有的多工作空间管理能力。该方案无需改动数据库结构，直接利用现有RBAC权限体系，实现了类似企业版的**多租户隔离**体验
redis创建集群时显示错误： [ERR] Node xxx is not empty. Either the node already knows other no...
一、问题 在redis集群配置的最后一步，使用命令报错：[ERR] Node xxx.xxx.xxx.xxx:7004 is not empty. Either the node already knows other nodes (check with CLUSTER NODES) or contains some key in database 0. 二、解决方法 1、删除每个redis节点的备份文件，数据库文件和集群配置文件 比如说我有7001~7006 6个节点，那么每个节点中的appen
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Ascend AI处理器核心计算范式与PyTorch的桥接艺术：深入探索torch_npu的基石要真正理解，我们必须首先超越其作为PyTorch后端库的表面认知，深入其所服务的硬件——华为昇腾（Ascend）AI处理器的核心计算范式。这不仅仅是关于如何在NPU上运行PyTorch模型，更是关于NPU如何设计其计算，以及如何巧妙地将这种硬件特性映射到PyTorch的抽象层之上。1. 昇腾AI处理器的达芬奇架构：极致算力的源泉昇腾AI处理器并非传统的CPU或GPU。它采用了华为独创的达芬奇（Da Vinci）架
[ERR] Node 192.168.245.111:6379 is not empty. Either the node already knows other nodes (check with
报错信息：[ERR] Node 192.168.245.111:6379 is not empty. Either the node already knows other nodes (check with CLUSTER NODES) or contains some key in database 0. 背景：第二天启动redis集群就报错（第一天正常工作，然后关机） 原因: 可能是非正常关闭集群导致 1-数据清空 flushdb命令清除所有节点中（我的是6个节点，3主3从）的数据 ..
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[ERR] Node 192.168.4.57:6357 is not empty. Either the node already knows other nodes (check with CLU
原因主要是原有的添加在redis配置中，没有进行重置之前的配置[root@host57 ~]# redis-cli -h 192.168.4.57 -p 6357 192.168.4.57:6357> CLUSTER RESETOK192.168.4.57:6357> cluster infocluster_state:failcluster_slots_assigned:0clust...
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
win10 跳过U8环境检测的IE WebControls.rar
win10 安装u8的时候 提示IE WebControls 不通过，这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后，重新检测通过。
[ERR] Node is not empty. Either the node already knows other nodes (check with C
搭建redis集群整合出现问题： >>> Creating cluster[ERR] Node 192.168.211.128:6379 is not empty. Either the node already knows other nodes (check with CLUSTER NODES) or contains some key in database 0....
解决[ERR] Node xxx is not empty. Either the node already knows other nodes (check with CLUSTER NODES)
解决： [ERR] Node 192.168.114.100:7001 is not empty. Either the node already knows other nodes (check with CLUSTER NODES) or contains some key in database 0. 我是将 每个节点下的两个文件删除掉 然后依次执行，最后创建集群发现成功！！ ...
Node 192.168.248.12:7001 is not empty. Either the node already knows other nodes (check with CLUSTER...
[root@node00 src]#./redis-trib.rb add-node --slave --master-id4f6424e47a2275d2b7696bfbf8588e8c4c3a5b95 172.168.63.202:7001172.168.63.202:7000 ...... [OK] All nodes agree about slotsconfiguration...
Redis集群报错[ERR] Node 192.168.96.132:7000 is not empty. Either the node already knows other nodes 。。。
主要是非正常关闭redis导致 解决： 1.先拍个快照，保存一下当前记录 2.删除每个redis下的 appendonly.aof dump.rdb nodes.conf 就直接删除这三种文件（注意：redis.conf不用删） 3.重启redis集群就可以了 ...
【Redis连接超时】io.lettuce.core.RedisConnectionException: Unable to connect to 192.168.x.x:7000
今天临近下班了，线上开始频繁报警，各种Redis连接超时，顿时慌的一批，因为最近在优化系统高频查询时用到了Redis作为缓存，难道要出生产事故，额～～～ 一首凉凉送给自己。。。。。。 于是马上联系下运维看下什么情况，运维看了下监控情况，OPS（operation per second）确实增加了不少，见下图： 于是乎发现确实是自己的锅，二话不说，先回复线上优化查询前的版本，保证线上能够正确运行，...
彻底解决：[ERR] Node is not empty. Either the node already knows other nodes
彻底解决：[ERR] Node is not empty. Either the node already knows other nodes 今天在搭建Redis-cluster的时候爆了这样的错误，根据中文意思就是说这个节点不是空的，想表达的意思就是说搭建集群的时候，大家数据要一致，需要都为空。这里用到的办法是将所有Node的数据都清空。 参考了网上一些方法后，发现在实操的时候是有一点问题的，做了点改进，但是思路还是一样的， 总体来说就两个步骤： 第一步：删除 appendonly.aof 和 d
jsp
JSP JSP：九大对象、七大动作、三大指令。 在BIN目录里面启动服务器(startup),然后在浏览器里面输入http://localhost(服务器IP)：端口号（8080）/就可以看到自己的服务端. 如果要运行程序: 例如：http://192.168.1.77:8080/mywork/table.jsp 192.168.1.77...
[ERR] Node is not empty. Either the node already knows other nodes or contains some key in database
把redis.conf 配置文件里的，bind 本地ip修改为虚拟机ip ， 报出这个异常。 解决方法： 1：将每个节点下aof、rdb、nodes.conf本地备份文件删除；（有的同学可能找不到aof，aof是实时持久化，没有启动过就没有） 2：./redis-cli -h 192.168.156.61 -p 6379 192.168.156.61:7001> fl...
redis服务在宕机之后报错“Node xxxx:7000 is not empty. Either the node already knows other nodes (check with CL...
redis在启动集群时报错，xxxx:7000 isnot empty. Either the node already knows other nodes(check with CLUSTER NODES) or contains some key in database 0” 解决方案： 1、删除redis启动集群时生成的.aof文件和.rdb文件； 2、将对应节点的“nodes...
[ERR] Node is not empty. Either the node already knows other nodes (check with C
最近在给公司网管系统redis集群环境添加节点时候遇到一个问题，提示新增的Node不为空： [root@node00 src]# ./redis-trib.rb add-node --slave --master-id4f6424e47a2275d2b7696bfbf8588e8c4c3a5b95 172.168.63.202:7001172.168.63.202:7000 .....
mongodb
一、快速入门 数据结构介绍：人员信息 { "_id" : ObjectId("59f938235d93fc4af8a37114"), "username" : "lison", "country" : "in11digo", "address" : { "aCode" : "邮编...
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