Set up Memcached as a service

本文介绍如何在Redhat或CentOS上安装并配置Memcached服务。通过创建自定义的启动脚本,实现Memcached服务的自动启动,并确保其正常运行。文章还提供了启动Memcached实例的具体步骤及验证方法。

From: http://www.lullabot.com/articles/installing-memcached-redhat-or-centos (Lullabot)


Just having memcache installed will not do anything by itself, we need to actually start up some instances of it for our web server to connect to, and we need memcached to automatically start up when the server restarts.

For this we need to install a new script at /etc/init.d/memcached. For this I usually use a custom script that's a bit crude, since it assumes that memcached is being used exclusively for our web server. However, most of the time this is true and it works just fine.

Download the memcached script (rename to just "memcached").

So simply load this script into /etc/init.d. Then set the permissions on it to make it executable:

$ chmod 755 memcached

Then register the script to start up with the server:

$ chkconfig --add memcached

Now you can start up memcached as a service.

$ service memcached start

And you can confirm that memcached has fired up several instances by checking ps.

$ ps -e | grep memcached
22805 ?        00:00:59 memcached
22807 ?        00:00:58 memcached
22809 ?        00:01:16 memcached
22811 ?        00:00:55 memcached
22813 ?        00:00:01 memcached
22815 ?        00:01:02 memcached
22817 ?        00:00:27 memcached
22819 ?        00:00:35 memcached
22821 ?        00:00:01 memcached
22823 ?        00:00:01 memcached
22825 ?        00:00:01 memcached

And that's it! You may need to change the /etc/init.d/memcached file to match your needs depending on what you're using Memcached for. If you're using Memcached with Drupal, you can follow the instructions for changing your settings.php file by following the instructions provided with the Memcache module. Also make sure you configure your Firewall to prevent access to Memcache from external URLs.


我在cpanel 多PHP编辑器里只有这些 display_errors This determines whether errors should be printed to the screen as part of the output or if they should be hidden from the user. 已禁用 max_execution_time This sets the maximum time in seconds a script is allowed to run before it is terminated by the parser. This helps prevent poorly written scripts from tying up the server. The default setting is 30. 60 max_input_time This sets the maximum time in seconds a script is allowed to parse input data, like POST, GET and file uploads. 60 max_input_vars This sets the maximum number of input variables allowed per request and can be used to deter denial of service attacks involving hash collisions on the input variable names. 3000 memory_limit This sets the maximum amount of memory in bytes that a script is allowed to allocate. This helps prevent poorly written scripts for eating up all available memory on a server. Note that to have no memory limit, set this directive to -1. 6000M post_max_size Sets max size of post data allowed. This setting also affects file upload. To upload large files, this value must be larger than upload_max_filesize. Generally speaking, memory_limit should be larger than post_max_size. 1000M session.gc_maxlifetime This specifies the number of seconds after which data will be seen as "garbage" and potentially cleaned up. 1440 session.save_path session.save_path defines the argument which is passed to the save handler. If you choose the default files handler, this is the path where the files are created. /var/cpanel/php/sessions/ea-php74 upload_max_filesize The maximum size of an uploaded file. 1000M zlib.output_compression Whether to transparently compress pages. If this option is set to "On" in php.ini or the Apache configuration, pages are compressed if the browser sends an "Accept-Encoding: gzip" or "deflate" header. 已禁用
09-25
内容概要:本文介绍了基于贝叶斯优化的CNN-LSTM混合神经网络在时间序列预测中的应用,并提供了完整的Matlab代码实现。该模型结合了卷积神经网络(CNN)在特征提取方面的优势与长短期记忆网络(LSTM)在处理时序依赖问题上的强大能力,形成一种高效的混合预测架构。通过贝叶斯优化算法自动调参,提升了模型的预测精度与泛化能力,适用于风电、光伏、负荷、交通流等多种复杂非线性系统的预测任务。文中还展示了模型训练流程、参数优化机制及实际预测效果分析,突出其在科研与工程应用中的实用性。; 适合人群:具备一定机器学习基基于贝叶斯优化CNN-LSTM混合神经网络预测(Matlab代码实现)础和Matlab编程经验的高校研究生、科研人员及从事预测建模的工程技术人员,尤其适合关注深度学习与智能优化算法结合应用的研究者。; 使用场景及目标:①解决各类时间序列预测问题,如能源出力预测、电力负荷预测、环境数据预测等;②学习如何将CNN-LSTM模型与贝叶斯优化相结合,提升模型性能;③掌握Matlab环境下深度学习模型搭建与超参数自动优化的技术路线。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注贝叶斯优化模块与混合神经网络结构的设计逻辑,通过调整数据集和参数加深对模型工作机制的理解,同时可将其框架迁移至其他预测场景中验证效果。
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