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xvfb-run 은 X Virtual Frame Buffer (Xvfb) 를 사용하여 그래픽 환경이 없는 서버에서 X11 애플리케이션을 실행할 수 있게 해줍니다. Puppeteer와 같은 도구는 주로 "헤드리스(Headless)" 브라우저 모드를 제공하지만, 특정 상황에서는 가상 디스플레이 환경이 필요할 수 있습니다. xvfb-run 이 필요한 경우 그래픽 환경이 없는 서버에서 GUI가 필요한 애플리케이션 실행 그래픽 환경이 없는 Linux 서버에서 Puppeteer가 헤드리스 모드 가 아닌 표준 모드 로 실행되어야 하는 경우. 예를 들어, 디버깅 목적으로 브라우저의 그래픽 출력이 필요한 경우. Puppeteer가 헤드리스 모드에서 제대로 작동하지 않는 경우 일부 웹사이트나 애플리케이션은 헤드리스 브라우저에서 동작이 다를 수 있습니다. headless: false 모드로 실행할 때 Xvfb를 사용해야 할 수 있습니다. const browser = await puppeteer. launch ({ headless: false }) ; 그래픽 출력 확인이 필요한 경우 브라우저의 화면을 캡처하거나, 애니메이션 테스트, 또는 UI 렌더링을 확인하려고 할 때, Puppeteer는 실제 디스플레이 환경을 요구할 수 있습니다. Docker 컨테이너에서 실행 대부분의 Docker 컨테이너는 GUI가 없는 환경에서 실행되므로 Puppeteer를 실행하려면 가상 디스플레이 환경을 제공해야 합니다. Dockerfile에서 Xvfb와 함께 Puppeteer를 사용하는 경우가 흔합니다. 서버 환경에서 화면 크기나 DPI 설정이 필요한 경우 특정 화면 크기, 해상도, 색 깊이 등을 설정해야 하는 경우 Xvfb를 사용하면 간단히 처리할 수 있습니다. xvfb-run 이 필요하지 않은 경우 Puppeteer를 기본적으로 헤드리스 모드로 실행하는 경우 Puppeteer는 기본적으로 헤드리스 모드 로 설...

Here are some recommended boilerplates and frameworks for TypeScript server programming, depending on your requirements: 1. NestJS (Highly Recommended for Enterprise-Grade Applications) NestJS is a progressive Node.js framework built with TypeScript. It provides an opinionated structure and is excellent for building scalable and maintainable server-side applications. Features : Dependency injection Modular architecture Built-in support for WebSockets, GraphQL, REST APIs Easy integration with tools like Swagger, TypeORM, and Mongoose Getting Started : Install Nest CLI: npm i -g @nestjs/cli nest new project-name Boilerplate is automatically generated with ready-to-use configurations for controllers, services, and modules. Website : https://nestjs.com/ 2. Express with TypeScript Express.js is lightweight and minimal, making it ideal for developers who prefer flexibility over opinionated frameworks. Boilerplate : You can start with an official or community-maintai...

In Node.js, when you want to assign one object to another that is declared by a custom type or class, you typically need to ensure compatibility between the source object's structure and the target class's expectations. Here's a breakdown: Scenario 1: Assigning Plain Objects to Class Instances If you have a custom class and want to assign properties from a plain object to an instance of that class, you can use one of the following methods: Example: class CustomType { constructor (name, age) { this .name = name; this .age = age; } } // Plain object const plainObject = { name : "John" , age : 30 }; // Assign using Object.assign() const customInstance = new CustomType(); Object .assign(customInstance, plainObject); console .log(customInstance instanceof CustomType); // true console .log(customInstance); // CustomType { name: 'John', age: 30 } Scenario 2: Using a Constructor You can modify your class to accept an object in the c...

A Sparse Encoder refers to a variant of neural network architectures where sparsity is introduced in the encoding process. This can mean either: Sparse Input Representations : The input features to the encoder are sparse (many values are zero). Sparse Output Representations : The encoder is designed to produce sparse outputs where most of the encoded feature values are zero. Sparse encoders are often used to improve model interpretability, efficiency, and generalization. They can be applied in various contexts, including traditional neural networks, autoencoders, and even transformer-based models. Key Characteristics of Sparse Encoders Sparsity in Representations : The model learns a feature representation where only a subset of neurons is active for a given input. This mimics how biological neurons operate, promoting interpretability and reducing noise in representations. Reduced Computational Cost : Sparse operations often result in lower computational overhead since...

Let's create an example of a sparse output from a hypothetical sparse encoder. This encoder takes an input vector and outputs an encoded representation where most of the values are zero (i.e., sparse). Example: Sparse Encoder Output Input : A dense input vector: [ 0.9 , 0.1 , 0.8 , 0.6 , 0.2 , 0.3 ] Output : A sparse encoded vector (after applying sparsity constraints, e.g., thresholding small values): [ 0.9 , 0.0 , 0.8 , 0.6 , 0.0 , 0.0 ] Code Example (Python) Here's a Python code snippet that demonstrates how sparsity might be applied to an encoder's output: import numpy as np # Example input: Dense vector input_vector = np. array ([ 0.9 , 0.1 , 0.8 , 0.6 , 0.2 , 0.3 ]) # Sparsity threshold: Values below this are set to zero threshold = 0.5 # Applying sparsity sparse_output = np.where(input_vector > threshold, input_vector, 0.0 ) print ( "Dense Input: " , input_vector) print ( "Sparse Output: " , sparse_output) Output : Dense...

The error AttributeError: module 'numpy' has no attribute 'float' occurs because the numpy.float , numpy.int , and similar aliases were deprecated in NumPy 1.20 and removed in NumPy 1.24. This issue arises when a package or script uses these outdated aliases. Here’s how to resolve it: 1. Identify and Update the Affected Code If this error is in your own code, replace numpy.float with the standard Python float type or the equivalent NumPy type: Replace numpy.float with float or numpy.float64 . Replace numpy.int with int or numpy.int64 . Example: # Before x = numpy.float( 10.5 ) # After x = float( 10.5 ) # Standard Python float # or x = numpy.float64( 10.5 ) # NumPy float64 2. Update the Dependencies If the error is coming from a third-party library, check if there is a newer version of the library that supports recent NumPy versions. Upgrade it: pip install --upgrade <package_name> 3. Use an Older NumPy Version (Temporary Fix) If u...