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官网地址:https://www.keepalived.org/download.html
配置keepalived
cd keepalived-1.3.5/
./configure --prefix=/home/keepalived
yum -y install libnl libnl-devel
离线配置lvs+keepalived+nginx高可用
linux离线配置lvs+keepalived+nginx高可用教程1 环境配置1.1服务器配置1.2工具安装2 Keepalived服务器配置2.1配置Keepalived-master2.2配置Keepalived-backup2.3验证master、backup服务器是否配置成功3 nginx服务器配置3.1 nginx1（server4）服务器配置3.2 nginx2（server5）服务器配置 1 环境配置 1.1服务器配置 准备5台虚拟机，作用如下表： 作用名称（虚拟机名称） IP
Rocky Linux 9企业级FTP服务器搭建与优化指南
FTP协议作为经典的文件传输协议，通过客户端-服务器架构实现跨平台文件共享，其核心优势在于支持断点续传和目录浏览。在Linux系统中，vsftpd因其轻量级特性和与SELinux的深度集成，成为企业级部署的首选方案。通过合理配置被动模式端口范围、启用传输加密(FTPS)以及实施虚拟用户映射，可显著提升系统的安全性和合规性。在金融、制造等行业的数据交换场景中，结合DRBD+Keepalived的高可用架构能实现99.99%的服务可用性。本文以Rocky Linux 9为例，详细解析从基础安装到性能调优的全流程
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本文详细介绍了在银河麒麟KY10服务器上配置Keepalived实现高可用架构的全过程，包括在线安装、核心配置解析、健康检查机制及生产环境优化。通过实战案例展示如何构建稳定的Nginx双节点集群，帮助企业在国产操作系统上实现业务高可用。
深入解析Keepalived离线安装与依赖管理
Keepalived 是一个高可用性的解决方案，主要基于 VRRP 协议（Virtual Router Redundancy Protocol），旨在解决服务器间的故障转移和负载均衡问题。Keepalived 可以实现服务的无间断切换，确保关键性服务的持续可用性。高可用性（High Availability, HA）通常指的是系统能够持续运行的能力。在IT行业中，HA通常涉及一个系统、应用或基础设施设计能够在单一组件故障情况下继续运作。
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keepalived 离线 rpm 安装包
keepalived 离线 rpm 安装包， 附带前置依赖 rpm keepalived-2.0.10-11.el8.x86_64.rpm
打包可以在Hadoop 3.3.1和Hive 3.1.2上使用的Tez 0.10.1 minimal
编译打包可以在Hadoop 3.3.1和Hive 3.1.2上使用的Tez 0.10.1
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uniapp - 实现移动端H5微信公众号网页内置浏览器中预览和下载word文档(doc)、excel表格(xls)、ppt文稿、pdf文档等文件，微信内部打开预览和下载到本地详教程，兼容安卓苹果手机
uni-app，微信公众号，h5，移动端，web平台端，网页，网站，Uniapp微信公众号H5网站，内置浏览器下载文档，如何在微信浏览器中预览office文件，word文档，excel文档，pdf文档，ppt文档，office，微信内置浏览器下载文件，下载文件，在微信浏览器里面实现下载任意文件，H5需要在微信浏览器中下载excel文件，UniApp实现网页在微信内置浏览器中下载文件详细教程，安卓点击下载会提示打开浏览器，后端返回流文件，如何解决无法下载文件，跳转至浏览器下载文件，发送到电脑微信，安卓，苹果，
AIGC算法工程师 面试八股文
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小秋SLAM入门实战ubuntu所有文章汇总
ubuntu 终端如何停止快速打印的输出以及恢复命令 ubuntu 终端如何快速打开当前路径下的图形化窗口界面？ killall -9用途用法 ps -xu | grep这个命令的用途 linux 下 top命令显示的是内存是堆内存吗？ zip分割和合并 关闭除当前 GNOME 终端窗口以外的所有其他 GNOME 终端窗口 如何在安装系统后设置短密码 【如何使用getrusage获取消耗的CPU时间和进程使用的最大物理内存】 【webots安装使用教程】 Linux系统下查看当前运行的进程和线程的几种方法
小论文/大论文必备| YOLO26热力图可视化，支持8种常用热力图方法：GradCAM,GradCAMPlusPlus,XGradCAM,EigenCAM,HiResCAM等，助力小论文和大论文加分
本文介绍了如何为YOLO26模型添加热力图可视化功能，包括完整代码和使用教程。热力图能增强模型的可解释性，适用于论文中的实验对比和结果展示。文章提供了详细的代码实现，包含图像处理、热力图生成和结果可视化等功能，并指导用户如何创建Python文件、粘贴代码以及调整参数生成最佳热力图。通过设置不同层号，用户可以灵活控制热力图效果，最终结果将保存在指定文件夹中。
【AI大模型应用开发】1.0 Prompt Engineering（提示词工程）- 典型构成、原则与技巧，代码中加入Prompt
Prompt典型构成、原则与技巧，以及OpenAI API重要参数解释，助你轻松入门Prompt
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")
RU DOS版、EFI版32bit/64bit BIOS常用工具
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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。
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