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[Ii]nfosec | ||
[Kk]eyrings? | ||
[Ll]oopback | ||
[Mm]emcached | ||
[Mm]oby | ||
[Nn]amespace | ||
[Oo]nboarding | ||
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The categories include: | ||
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- **API Management**: Tools for creating, publishing, analyzing, and securing | ||
APIs. | ||
- **Content Management System:** Software applications to create and manage | ||
digital content through templates, procedures, and standard formats. | ||
- **Data Science:** Tools and software to support analyzing data and generating | ||
actionable insights. | ||
- **Databases & Storage:** Systems for storing, retrieving, and managing data. | ||
- **Languages & Frameworks:** Programming language runtimes and frameworks. | ||
- **Integrations & Delivery:** Tools for Continuous Integration (CI) and | ||
Continuous Delivery (CD). | ||
- **Internet of Things:** Tools supporting Internet of Things (IoT) | ||
applications. | ||
- **Machine Learning & AI:** Tools and frameworks optimized for artificial | ||
intelligence and machine learning projects, such as pre-installed libraries | ||
and frameworks for data analysis, model training, and deployment. | ||
- **Message Queues:** Message queuing systems optimized for reliable, scalable, | ||
and efficient message handling. | ||
- **Monitoring & Observability:** Tools to track software and system performance | ||
through metrics, logs, and traces, as well as observability to explore the | ||
system’s state and diagnose issues. | ||
- **Networking:** Repositories that support data exchange and connecting | ||
computers and other devices to share resources. | ||
- **Operating Systems:** Software that manages all other programs on a computer | ||
and serves as an intermediary between users and the computer hardware, while | ||
overseeing applications and system resources. | ||
- **Security:** Tools to protect a computer system or network from theft, | ||
unauthorized access, or damage to their hardware, software, or electronic | ||
data, as well as from service disruption. | ||
- **Web Servers:** Software to serve web pages, HTML files, and other assets to | ||
users or other systems. | ||
- **Web Analytics:** Tools to collect, measure, analyze, and report on web data | ||
and website visitor engagement. |
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--- | ||
description: Learn about Docker Hub's library of images, extensions, and plugins. | ||
keywords: Docker Hub, Hub, content library | ||
title: Container image library | ||
linkTitle: Library | ||
weight: 20 | ||
--- | ||
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Docker Hub's container image library is the world's largest collection of | ||
container images, extensions, and plugins. It provides a central location to | ||
discover pre-built images and tools designed to streamline your container | ||
workflows, making it easier to share and collaborate. | ||
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In this section, learn about: | ||
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- [Search](./search.md): Discover how to browse and search Docker Hub's extensive resources. | ||
- [Trusted content](./trusted-content.md): Dive into Docker Official Images, | ||
Verified Publisher content, and Sponsored Open Source images, all vetted for | ||
security and reliability to streamline your workflows. | ||
- [Catalogs](./catalogs.md): Explore specialized collections like the generative AI catalog. | ||
- [Mirroring](./mirror.md): Learn how to create a mirror of Docker Hub's | ||
container image library as a pull-through cache. |
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--- | ||
description: Explore specialized Docker Hub collections like the Generative AI catalog. | ||
keywords: Docker Hub, Hub, catalog | ||
title: Docker Hub catalogs | ||
linkTitle: Catalogs | ||
weight: 60 | ||
--- | ||
|
||
Docker Hub catalogs are your go-to collections of trusted, ready-to-use | ||
container images and resources, tailored to meet specific development needs. | ||
They make it easier to find high-quality, pre-verified content so you can | ||
quickly build, deploy, and manage your applications with confidence. Catalogs in | ||
Docker Hub: | ||
|
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- Simplify content discovery: Organized and curated content makes it easy to | ||
discover tools and resources tailored to your specific domain or technology. | ||
- Reduce complexity: Trusted resources, vetted by Docker and its partners, | ||
ensure security, reliability, and adherence to best practices. | ||
- Accelerate development: Quickly integrate advanced capabilities into your | ||
applications without the hassle of extensive research or setup. | ||
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The generative AI catalog is the first catalog in Docker Hub, offering | ||
specialized content for AI development. | ||
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## Generative AI catalog | ||
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The [generative AI catalog](https://hub.docker.com/catalogs/gen-ai) makes it | ||
easy to explore and add AI capabilities to your applications. With trusted, | ||
ready-to-use content and comprehensive documentation, you can skip the hassle of | ||
sorting through countless tools and configurations. Instead, focus your time and | ||
energy on creating innovative AI-powered applications. | ||
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The generative AI catalog provides a wide range of trusted content, organized | ||
into key areas to support diverse AI development needs: | ||
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- Demos: Ready-to-deploy examples showcasing generative AI capabilities. These | ||
demos provide a hands-on way to explore AI tools and frameworks, making it | ||
easier to understand how they can be integrated into real-world applications. | ||
- Models: Pre-trained AI models for tasks like text generation, | ||
natural language processing (NLP), and conversational AI. These models | ||
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provide a foundation for | ||
AI applications without requiring developers to train models from scratch. | ||
- Applications and end-to-end platforms: Comprehensive platforms and tools that | ||
simplify AI application development, including low-code solutions and | ||
frameworks for building multi-agent and retrieval-augmented generation (RAG) | ||
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applications. | ||
- Model deployment and serving: Tools and frameworks that enable developers to | ||
efficiently deploy and serve AI models in production environments. These | ||
resources include pre-configured stacks for GPUs and other specialized | ||
hardware, ensuring performance at scale. | ||
- Orchestration: Solutions for managing complex AI workflows, such as workflow | ||
engines, large language model (LLM) application frameworks, and lifecycle management | ||
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tools, to help streamline development and operations. | ||
- Machine learning frameworks: Popular frameworks like TensorFlow and PyTorch | ||
that provide the building blocks for creating, training, and fine-tuning | ||
machine learning models. | ||
- Databases: Databases optimized for AI workloads, including vector databases | ||
for similarity search, time-series databases for analytics, and NoSQL | ||
solutions for handling unstructured data. |
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