#go#github_actions#kubernetes#operator
Actions Runner Controller (ARC) is a tool that helps you automatically manage and scale self-hosted GitHub Actions runners using Kubernetes. It creates runner scale sets that grow or shrink based on how many workflows you are running, making your CI/CD process more efficient and cost-effective. ARC uses containers for runners, so new instances can start or stop quickly and cleanly. You can install ARC easily with Helm on Kubernetes and customize runners with features like custom images, volumes, and scripts. This automation saves you time and resources by matching runner capacity to your actual workload needs[1][2][3].
https://github.com/actions/actions-runner-controller
Совсем лайтовая статья для новичков "10 главных конструкций языка R".
Содержание:
- Комментарии
- Переменные и векторы
- Внешние модули
- Ввод и вывод
- Присваивание и сравнение
- Условный оператор if
- Цикл for
- Функции
- Классы, методы и объекты
#статьи
#easy
#Easy#Credit#T#i#ch#nh#s
Join the Easy Credit - Tài chính số beta on ✈️#TestFlight
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Shared by Dimitri
#python#deepseek#demo#easy#embedding#flask#gpt#huggingface_transformers#llm#mcp#multimodal#openai#qwen#rag#sentence_transformers#ui#vllm#vlm
UltraRAG is a lightweight framework that makes building retrieval-augmented generation (RAG) systems simple and fast. It uses a low-code approach where you write just dozens of lines of YAML configuration instead of complex code to create sophisticated AI workflows with conditional logic and loops. The framework includes a visual development environment where you can drag-and-drop to build pipelines, adjust parameters in real-time, and instantly convert your logic into interactive chat applications. This means you can deploy powerful AI systems that ground answers in your own data—reducing hallucinations and improving accuracy—without needing extensive coding expertise or lengthy development cycles.
https://github.com/OpenBMB/UltraRAG