About the Docker Hub

DockerHub作为云端注册服务,提供应用程序和服务容器的构建与分发。它具备图像存储库、自动化构建、Webhooks、组织管理和GitHub及Bitbucket集成等功能。

About the Docker Hub

The Docker Hub is a cloud-based registry service forbuilding and shipping application or service containers. It provides a centralized resource for containerimage discovery, distribution and change management, user and teamcollaboration, and workflow automation throughout the development pipeline.

Getting started with Docker Hub

Specifically, Docker Hub provides the following major features and functions:

  • Image Repositories: Find, manage, and push and pull images from community, official, and private image libraries.
  • Automated Builds: Automatically create new images when you make changes to a source GitHub or Bitbucket repository.
  • Webhooks: A feature of Automated Builds, Webhooks let you trigger actions after a successful push to a repository.
  • Organizations: Create work groups to manage user access to image repositories.
  • GitHub and Bitbucket Integration: Add the Hub and your Docker Images to your current workflows.

Create a Docker Hub account

To explore Docker Hub, you’ll need to create an account by following thedirections in Hub Accounts. You can create an account and use the Hub with one private repo for free. If you need more private repos, you can upgrade from your free account to a paid plan. To learn more, log in to the Hub and go to Billing & Plans, which you access via the Settings menu (gear icon at upper right).

Work with Docker image repositories

The Docker Hub provides you and your team with a place to build and ship Docker images.

You can configure Docker Hub repositories in two ways:

  • Repositories, which allow you to push images at will from your local Docker daemon to the Hub, and
  • Automated Builds, which allow you to configure GitHub or Bitbucket totrigger the Hub to rebuild repositories when changes are made to the repository.

You can create public repositories which can be accessed by any other Hub user, or you can create private repositories with limited access you control.

Docker commands and Docker Hub

Docker itself provides access to Docker Hub services via the docker search,pull, login, and push commands.

Explore repositories

There are two ways you can search for public repositories and images availableon the Docker Hub. You can “Search” on the Docker Hub website, oryou can docker search for all the repositories and images using the Docker commandlinetool:

$ docker search ubuntu

Both will show you a list of the currently available public repositories on theDocker Hub which match the provided keyword.

A private repository won’t be listed in the repositorysearch results. To see all the repositories you can access and their status,view your “Dashboard” page on Docker Hub.

You can find more information on working with Docker images in the Docker userguide.

Use Official Repositories

The Docker Hub contains a number of OfficialRepositories. These are public,certified repositories from vendors and contributors to Docker. Theycontain Docker images from vendors like Canonical, Oracle, and Red Hatthat you can use as the basis to building your applications and services.

With Official Repositories you know you’re using an optimized andup-to-date image that was built by experts to power your applications.

Note:If you would like to contribute an Official Repository for yourorganization or product, see the documentation on Official Repositories on DockerHub for more information.

Create organization

Learn how to create a Docker Hubaccount and manage your organizations and teams.

【轴承故障诊断】基于融合鱼鹰和柯西变异的麻雀优化算法OCSSA-VMD-CNN-BILSTM轴承诊断研究【西储大学数据】(Matlab代码实现)内容概要:本文提出了一种基于融合鱼鹰和柯西变异的麻雀优化算法(OCSSA)优化变分模态分解(VMD)参数,并结合卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)的轴承故障诊断模型。该方法利用西储大学公开的轴承数据集进行验证,通过OCSSA算法优化VMD的分解层数K和惩罚因子α,有效提升信号分解精度,抑制模态混叠;随后利用CNN提取故障特征的空间信息,BiLSTM捕捉时间序列的动态特征,最终实现高精度的轴承故障分类。整个诊断流程充分结合了信号预处理、智能优化与深度学习的优势,显著提升了复杂工况下轴承故障诊断的准确性与鲁棒性。; 适合人群:具备一定信号处理、机器学习及MATLAB编程基础的研究生、科研人员及从事工业设备故障诊断的工程技术人员。; 使用场景及目标:①应用于旋转机械设备的智能运维与故障预警系统;②为轴承等关键部件的早期故障识别提供高精度诊断方案;③推动智能优化算法与深度学习在工业信号处理领域的融合研究。; 阅读建议:建议读者结合MATLAB代码实现,深入理解OCSSA优化机制、VMD参数选择策略以及CNN-BiLSTM网络结构的设计逻辑,通过复现实验掌握完整诊断流程,并可进一步尝试迁移至其他设备的故障诊断任务中进行验证与优化。
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