Управление Docker контейнерами с помощью docker-py
docker-py – это официальная библиотека Python для Docker, предоставляющая API для взаимодействия с Docker Daemon. С её помощью можно автоматизировать процессы создания, запуска, остановки и удаления контейнеров, работы с образами, сетями и томами Docker.
import docker
# Создание клиента
client = docker.from_env()
# Запуск контейнера
container = client.containers.run("ubuntu:latest", "echo Hello, docker-py!", detach=True)
# Получение логов контейнера
print(container.logs().decode())
# Остановка и удаление контейнера
container.stop()
container.remove()
В данном примере мы создаем клиента Docker, используя переменные окружения текущей сессии. Затем мы запускаем контейнер из образа ubuntu:latest, выполняем в нем команду echo, выводим логи работы контейнера и в конце останавливаем и удаляем контейнер.
Управление образами с помощью docker-py:
# Получение списка всех образов
images = client.images.list()
# Вывод информации о каждом образе
for image in images:
print(f'ID: {image.id}, Теги: {image.tags}')
Для дальнейшего изучения и ознакомления с более продвинутыми возможностями рекомендуется обратиться к официальной документации.
#python#docker#dockerpy
#python#ai#faiss#gpt_oss#langchain#llama_index#llm#localstorage#offline_first#ollama#privacy#python#rag#retrieval_augmented_generation#vector_database#vector_search#vectors
LEANN is a tiny, powerful vector database that lets you turn your laptop into a personal AI assistant capable of searching millions of documents using 97% less storage than traditional systems without losing accuracy. It works by storing a compact graph and computing embeddings only when needed, saving huge space and keeping your data private on your device. You can search your files, emails, browser history, chat logs, live data from platforms like Slack and Twitter, and even codebases—all locally without cloud costs. This means fast, private, and efficient AI-powered search and retrieval on your own laptop.
https://github.com/yichuan-w/LEANN
#rust#ai#ai_engineering#anthropic#artificial_intelligence#deep_learning#genai#generative_ai#gpt#large_language_models#llama#llm#llmops#llms#machine_learning#ml#ml_engineering#mlops#openai#python#rust
TensorZero is a free, open-source tool that helps you build and improve large language model (LLM) applications by using real-world data and feedback. It gives you one simple API to connect with all major LLM providers, collects data from your app’s use, and lets you easily test and improve prompts, models, and strategies. You can see how your LLMs perform, compare different options, and make them smarter, faster, and cheaper over time—all while keeping your data private and under your control. This means you get better results with less effort and cost, and your apps keep improving as you use them[1][2][3].
https://github.com/tensorzero/tensorzero
#python#agent_skills#ai_agents#antigravity#automation#claude#claude_code#codex#composio#cursor#gemini_cli#mcp#rube#saas#skill#workflow_automation
Claude Skills are customizable workflows that boost productivity on Claude.ai, Claude Code, and API by handling tasks like document editing, code development, data analysis, app automation (emails, Slack, GitHub via Composio's 500+ integrations), and more. Install the connect-apps plugin, add your free Composio API key, and restart to enable real actions across 1000+ apps. This saves time, automates repetitive work, and lets you focus on high-value tasks for faster, consistent results everywhere you use Claude.
https://github.com/ComposioHQ/awesome-claude-skills
#python#ai#ai_agents#ai_coding#claude_code_plugin#claude_code_plugins#claude_code_plugins_marketplace#claude_marketplace#claude_plugin#claude_skills#docs#documentation#mcp#mcp_server#postgres#postgresql#skills
pg-aiguide helps AI coding tools create better PostgreSQL code with semantic search of official docs, best-practice skills for schemas/indexes, and extension info like TimescaleDB. Install it free as a public MCP server or Claude plugin in tools like Cursor/VS Code for one-click setup. It fixes AI's weak spots—outdated code, missing constraints (4x more), indexes (55% more), and modern PG17 features—producing robust, fast, maintainable schemas that save you debugging time and production fixes.
https://github.com/timescale/pg-aiguide
#python#ai_agent#automation#autonomous_agent#browser_automation#claude#computer_control#desktop_automation#gemini#lightweight#llm_agent#memory_system#python#self_evolving#skill_tree#task_automation
GenericAgent is a simple 3K-line AI agent framework that controls your computer—browser, files, mouse, screen, and phone—with just 9 tools and a 100-line loop. It learns from tasks like ordering food, checking stocks, or sending messages, saving them as reusable skills that grow into your unique skill tree over time. Install easily with git clone, pip, and an API key, then launch. This saves you hours on repetitive work, automates personal tasks, and builds smarter help tailored just for you.
https://github.com/lsdefine/GenericAgent
#python#apple_silicon#florence2#idefics#llava#llm#local_ai#mlx#molmo#paligemma#pixtral#vision_framework#vision_language_model#vision_transformer
MLX-VLM lets you run, chat with, and fine-tune Vision Language Models (VLMs) plus audio/video models on your Mac using MLX—install easily with `pip install -U mlx-vlm`. Use CLI for quick text/image/audio generation (e.g., `mlx_vlm.generate --model ... --image photo.jpg`), Gradio UI for chats, Python scripts, or a FastAPI server with OpenAI-compatible endpoints supporting multi-images/videos. Features like TurboQuant cut KV cache memory by 76%, and LoRA/QLoRA fine-tuning works on consumer hardware. You benefit by experimenting with powerful multimodal AI locally—fast, memory-efficient, no cloud costs, perfect for Mac users tweaking models affordably.
https://github.com/Blaizzy/mlx-vlm
#python#agentic_code#agentic_coding#ai_workflow_optimization#ai_workflows#anthropic#anthropic_claude#awesome#awesome_list#awesome_lists#awesome_resources#claude#claude_code#coding_agent#coding_agents#coding_assistant
This repository is a collection of resources to enhance your workflow with Claude Code, a powerful coding assistant. It includes **slash-commands**, **tooling**, **hooks**, and **CLAUDE.md files** that help you manage projects, automate tasks, and improve code quality. The repository is community-driven, allowing users to share and discover new ways to use Claude Code effectively. By contributing to this list, you can help others and improve your own productivity with Claude Code.
https://github.com/hesreallyhim/awesome-claude-code
#python#ai#context#embedded#faiss#knowledge_base#knowledge_graph#llm#machine_learning#memory#nlp#offline_first#opencv#python#rag#retrieval_augmented_generation#semantic_search#vector_database#video_processing
Memvid lets you store millions of text pieces inside a single MP4 video file using QR codes, making your data 50-100 times smaller than usual databases. You can search this video instantly in under 100 milliseconds without needing servers or internet after setup. It works offline, is easy to use with simple Python code, and supports PDFs and chat with your data. The upcoming version 2 will add features like continuous memory updates, shareable capsules, fast local caching, and better video compression, making your AI memory smarter, faster, and more flexible. This means you get a powerful, portable, and efficient way to manage and search huge knowledge bases quickly and easily.
https://github.com/Olow304/memvid
#jupyter_notebook#a2a#agentic_ai#dapr#dapr_pub_sub#dapr_service_invocation#dapr_sidecar#dapr_workflow#docker#kafka#kubernetes#langmem#mcp#openai#openai_agents_sdk#openai_api#postgresql_database#rabbitmq#rancher_desktop#redis#serverless_containers
The Dapr Agentic Cloud Ascent (DACA) design pattern helps you build powerful, scalable AI systems that can handle millions of AI agents working together without crashing. It uses Dapr technology with Kubernetes to efficiently manage many AI agents as lightweight virtual actors, ensuring fast response, reliability, and easy scaling. You can start small using free or low-cost cloud tools and grow to planet-scale systems. The OpenAI Agents SDK is recommended for beginners because it is simple, flexible, and gives you good control to develop AI agents quickly. This approach saves costs, avoids vendor lock-in, and supports resilient, event-driven AI workflows, making it ideal for developers aiming to create advanced, cloud-native AI applications[1][2][3][4].
https://github.com/panaversity/learn-agentic-ai
#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
#python#ant_colony_algorithm#artificial_intelligence#fish_swarms#genetic_algorithm#heuristic_algorithms#immune#immune_algorithm#optimization#particle_swarm_optimization#pso#simulated_annealing#travelling_salesman_problem#tsp
You can use scikit-opt, a Python library offering many heuristic optimization algorithms like Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony, Immune Algorithm, and Artificial Fish Swarm Algorithm. It supports user-defined functions to customize operators, allows continuing runs from previous iterations, and accelerates computations via vectorization, multithreading, multiprocessing, and caching. GPU support is in development. It helps solve complex optimization problems such as function minimization and the Traveling Salesman Problem efficiently, with easy installation and rich examples. This saves you time and effort in implementing and tuning optimization algorithms yourself.
https://github.com/guofei9987/scikit-opt