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npm run build error: Heap out of memory in Nuxt3

When running npm run build , an error may occur which looked like :<--- Last few GCs ---> [2656:000001DF58927940] 511573 ms: Mark-sweep 2013.9 (2091.6) -> 2012.7 (2091.4) MB, 1022.4 / 0.0 ms (average mu = 0.211, current mu = 0.009) allocation failure; scavenge might not succeed [2656:000001DF58927940] 513611 ms: Mark-sweep 2028.4 (2091.4) -> 2027.2 (2117.4) MB, 2021.5 / 0.0 ms (average mu = 0.083, current mu = 0.008) allocation failure; scavenge might not succeed <--- JS s ...

为什么有了uwsgi 还要 nginx 服务器？

有关nginx的简单介绍，详见Nginx服务器反向代理。uwsgi是python的一个通信协议，同时也是一种web服务器，而nginx则是高性能反向代理的web服务器。在Django项目服务器部署中，uwsgi几乎是不可替代的。然而部署好了uwsgi，其实django接口已经能够响应请求，为什么还要额外配置nginx服务器？因为，相比于直接将真实地址暴露在公网上，在外面套一层nginx安全性更高，具体如下：安全问题，程序不能直接被浏览器访问到，而是通过nginx,nginx只开放某个接口，uwsgi本身是内网接口，这样运维人员在nginx上加上安全性的限制，可以达到保护程序的作用。负载均衡问题，一个uwsgi很可能不够用，即使开了多个work也是不行，毕竟一台机器的cpu和内存都是有限的，有了nginx做代理，一个nginx可以代理多台uwsgi完成uwsgi的负载均衡。静态文件处理效率问题，用django或是uwsgi这种东西来负责静态文件的处理是很浪费的行为，而且他们本身对文件的处理也不如nginx好，所以整个静态文件的处理都直接由nginx完成，静态文件的访问完全不去经过uwsg ...

AI: Deep Learning vs Machine Learning

In the previous article Supervised and Unsupervised learning in machine learning, we explains the meaning of machine learning. As AI is making waves across industries, we often hear the terms "deep learning" and "machine learning" getting thrown around interchangeably. While both are crucial for AI advancements, they are indeed two different subjects with distinct characteristics. Deep learning is a branch of machine learning, whose methods are based on artificial neural networks (ANNs). In othe ...

AI Spark Big Model

IntroductionDeveloping Artificial Intelligence (AI) has paved the way for the introduction of several related fields that rely on the model to enhance their operation. Various fields within global economies have adopted the functional and relevant aspects of AI to enhance their operation and communicate fluency in service delivery to the significant global populace. For instance, global finance, agricultural, legal, education, research, and security organizations have ensured they infused the mo ...

Advantages of AI Spark Big Models and Their Global Economic Benefit

IntroductionAI has been revolutionizing in business and provides the best opportunities for efficiency and novelty. Among the most striking developments that have taken place regarding AI are state-of-the-art models with phenomenal performance, which come in very wide applications. Equipped with sophisticated algorithms and huge volumes of data, such models—like OpenAI's GPT-4—can do versatile tasks in speedy and accurate ways (Hadi et al 2023). The following essay elaborates on the advantages o ...

What is base change map $U\times_{X}X\rightarrow U\times_{Y}Y$?

Let $X,Y$ be schemes. Let $f:X\rightarrow Y$, $g:Y\rightarrow Y$ be morphisms of schemes. Then we can form the base change\begin{CD} X\times_{f,Y,g} Y @>>> Y\\ @VVV @VV{g}V \\X @>{f}>> Y \end{CD}Therefore, for any morphism $U\rightarrow X$, we can form the fibre product $U\times_{Y,g} Y$ as follows:\begin{CD}U\times_{Y,g}Y @>>> X\times_{f,Y,g} Y @>>> Y\\ @VVV @VVV @VV{g}V \\ U @>>> X @>{f}>> Y \end{CD}Let $X\rightarrow X$ be the identity morp ...

Vertically aligning CSS :before and :after content

I am trying to centre the link with the image, but can't seem to move the content vertically in any way.<h4>More Information</h4> <a href="#" class="pdf">File Name</a>The icon is 22 x 22px.pdf { font-size: 12px; } .pdf:before { padding:0 5px 0 0; content: url(../img/icon/pdf_small.png); } .pdf:after { content: " ( .pdf )"; font-size: 10px; } .pdf:hover:after { color: #000; }

A short introduction to category theory

This short note originated from a short talk of basic category theory in 2022. Category theory was first introduced by Samuel Eilenberg and Saunders Mac Lane in the 20th century. It rapidly became a strong tool in almost all domains of mathematics. Category theory studies the abstract structures of different mathematical objects, and it also has applications on computer science, including AI.1. CategoriesIn this section, we will first lay out the general definition of categories, then we give so ...

How to find similar strings with python?

Suppose that we have a string str together with a list of strings arr = [str1, str2, ... ]. How to find similar string of str in arr? To do this, we can calculate the similarity of str and elements in arr one by one. But how to calculate the degree of similarity between str and an element in arr? First, we need a measurement of the similarity of two strings. In fact, there are many methods that measure the similarity of strings. In this tutorial, we will use python to find similar strings of str ...

Vue - Go to anchor smoothly without changing url

In Vue.js, we can define an anchor by RouterLink or HTML tag <a> , while in Nuxt.js, we can additionally use NuxtLink which is similar to RouterLink .<a href="#content">My Link</a> <RouterLink to="#content">My Link</RouterLink> <NuxtLink to="#content">My Link</NuxtLink>However, on loading a page, the above ways would change the url which makes it slow to go to #content . To speed up anchor link and not change the url, there are two ways.The first way is ...

Nuxt 3 Error TypeError: Cannot read properties of null (reading 'shapeFlag')

I encountered such an error when using Nuxt 3.8 :TypeError: Cannot read properties of null (reading 'shapeFlag')The error is a bit misleading to me, because I have defined a Dialog component which is also named Flag . I used Flag in two different pages, one of which was ok, but the other one threw out such an error. I tried to add ClientOnly to Flag , but the error was still there.I thought that the issue is my UI library. I am using ElementUI Plus, and my Flag component is indeed defined by wra ...

数据清洗之异常值处理

在数据清洗过程中，异常值处理是一个非常重要的步骤。异常值，也称为离群值或异常点，是指与大多数观察值明显不同的观察值。这些值可能是由于测量错误、数据录入错误、设备故障或者真实现象的特殊情况而产生的。异常值可能会对数据分析和建模产生负面影响，因此需要进行适当的处理。下面是一些常见的异常值处理方法：删除异常值：最简单的方法是直接删除异常值。这种方法适用于异常值数量较少、对数据整体影响较小的情况。但是，如果异常值数量较多或者对数据整体影响较大，则不建议使用这种方法。替换异常值：可以用数据集的其他统计特征值（如均值、中位数、众数）来替换异常值。这种方法可以保持数据集的整体分布特征，并且相对于直接删除异常值，对数据的影响较小。分箱处理：将数据分成多个箱子（bins），然后用每个箱子的均值或中位数来代替箱子中的异常值。这种方法可以减少异常值对整体数据的影响，并且保持了数据的整体分布特征。基于模型的方法：可以使用回归、聚类或其他机器学习模型来识别和处理异常值。这些模型可以自动识别数据中的异常值，并提供相应的处理方案。使用离群值检测算法：可以使用一些专门用于检测异常值的算法，如孤立森林、局部异 ...

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