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| [[File:Infocepo-picture.png|thumb|right|Discover cloud and AI on infocepo.com]] | | [[File:Infocepo-picture.png|thumb|right|Discover cloud and AI on infocepo.com]] |
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| = infocepo.com – Cloud, AI & Labs = | | = infocepo.com – Cloud, AI |
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| Bienvenue sur le portail '''infocepo.com'''.
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| Ce wiki documente l’écosystème '''Cloud, IA, automatisation et lab''' d’Infocepo.
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| Il s’adresse aux :
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| * administrateurs systèmes,
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| * ingénieurs cloud,
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| * développeurs,
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| * étudiants,
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| * curieux qui veulent apprendre en pratiquant.
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| L’objectif est simple : transformer la théorie en '''scripts réutilisables, schémas, architectures, APIs et laboratoires concrets'''.
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| __TOC__
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| ----
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| = Accès rapide =
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| == Portail principal ==
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| * [https://infocepo.com infocepo.com]
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| == Assistant IA ==
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| * [https://chat.infocepo.com Chat assistant]
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| == Liste des pages du wiki ==
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| * [[Special:AllPages|Toutes les pages]]
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| == Vue d’ensemble ==
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| [[File:Ailab-architecture.png|thumb|'''Infra architecture overview''']]
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| = Démarrer rapidement =
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| == Parcours recommandés ==
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| ; 1. Construire un assistant IA privé
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| * Déployer une stack type '''Hermes WebUI + Ollama + GPU'''
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| * Ajouter un modèle de chat et un modèle de résumé
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| * Brancher des données internes via '''RAG + embeddings'''
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| ; 2. Lancer un lab cloud
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| * Créer un petit cluster Kubernetes, OpenStack ou bare-metal
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| * Mettre en place un pipeline de déploiement (Helm, Ansible, Terraform…)
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| * Ajouter un service IA : transcription, résumé, chatbot, OCR…
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| ; 3. Préparer un audit ou une migration
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| * Inventorier les serveurs avec '''ServerDiff.sh'''
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| * Concevoir l’architecture cible
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| * Automatiser la migration avec des scripts reproductibles
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| == Vue d’ensemble du contenu ==
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| * '''Guides IA & outils''' : assistants, modèles, évaluation, GPU, RAG
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| * '''Cloud & infrastructure''' : Kubernetes, OpenStack, HA, HPC, DevSecOps
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| * '''Labs & scripts''' : audit, migration, automatisation
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| * '''Comparatifs''' : Kubernetes vs OpenStack vs AWS vs bare-metal, etc.
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| ----
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| = Vision =
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| [[File:Automation-full-vs-humans.png|thumb|right|The world after automation]]
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| Le but à long terme est de construire un environnement où :
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| * les assistants IA privés accélèrent la production,
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| * les tâches répétitives sont automatisées,
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| * les déploiements sont industrialisés,
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| * l’infrastructure reste '''compréhensible, portable et réutilisable'''.
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| Chaque sortie IA est mesurée en temps réel, les modèles faibles sont remplacés automatiquement, et les tâches sans valeur sont coupées. L'autonomie vient de mécanismes techniques qui fonctionnent quand personne ne regarde — pas de règles écrites dans un doc.
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| [[File:SUMMARY-DIAGRAM-7311e6b1-aede-4989-ade2-a42d1a6e0ff2.png|thumb|right|Main page summary]]
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| ----
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| = Catalogue rapide des services =
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| {| class="wikitable"
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| |+ Services principaux
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| ! Catégorie !! Service !! Rôle
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| |-
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| | API || [https://api-nothink.ailab.infocepo.com/docs LLM] || Modèles de chat, code, RAG, OCR
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| |-
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| | API || [https://api-audio2txt.ailab.infocepo.com/docs STT] || Transcription audio
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| |-
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| | API || [https://api-tts-omnivoice.ailab.infocepo.com/docs TTS] || Synthèse vocale
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| |-
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| | API || [https://github.com/ynotopec/api-realtime-ai realtime-ai] || Temps réel WebSocket / WebRTC
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| |-
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| | API || [https://api-nothink.ailab.infocepo.com/docs IMAGE2TXT] || OCR / VLM via endpoint dédié
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| |-
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| | API || [https://api-summary.ailab.infocepo.com:wait-2026-12/docs summary] || Résumé de textes longs
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| |-
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| | API || [https://api-embedding.ailab.infocepo.com/docs EMBEDDINGS] || Embeddings pour RAG
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| |-
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| | API || [https://chromadb.ailab.infocepo.com:wait-2026-12 ChromaDB] || Base vecteur
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| |-
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| | API || [https://api-txt2image.ailab.infocepo.com/docs TXT2IMAGE] || Génération d’images
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| |-
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| | API || [https://api-diarization.ailab.infocepo.com/docs diarization] || Segmentation locuteurs
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| |-
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| | Observabilité || [https://grafana.ailab.infocepo.com:wait-2026-12 monitoring] || Dashboards techniques
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| |-
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| | Observabilité || [https://uptime-kuma.ailab.infocepo.com:wait-2026-12/status/ai status] || Disponibilité des services
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| |-
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| | Observabilité || [https://web-stat.c1.ailab.infocepo.com:wait-2026-12 web-stat] || Statistiques web
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| |-
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| | Observabilité || [https://api.ailab.infocepo.com:wait-2026-09/ui LLM-stat] || Vue API / usage
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| |-
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| | Outils || [https://datalab.ailab.infocepo.com:wait-2026-12 dataLab] || Environnement de travail hors-production
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| |-
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| | Outils || [https://translate-rt.ailab.infocepo.com realtime translation] || Traduction
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| |-
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| | Outils || [https://demos.ailab.infocepo.com Demos] || Démonstrateurs
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| |}
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| ----
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| = Sélection AI & architecture par couche =
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| Le tableau de bord [https://ai.arch.infocepo.com '''Sélection AI'''] présente l'architecture par couche des modèles IA déployés :
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| {| class="wikitable"
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| ! Couche !! Modèle !! Alias(s) !! Taille
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| |-
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| | rowspan="7" style="text-align:center; vertical-align:middle; background:#f0f0f0;"| '''HYBRID-CLOUD'''
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| | '''Qwen3.6-35B''' || flash, default, thinking, vision || Option 1, 35B MoE / 3B actif
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| |-
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| | '''Qwen3.8-Flash-Next''' || flash, default, thinking, vision, max || Option 2, 125B MoE, 51B n-grames internes, 6B actif, 1M contexte
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| |-
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| | '''MiniMax-H3''' || video || 33B dense
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| |-
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| | '''BGE-M3''' || embedding || 568M
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| |-
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| | '''BGE-reranker-v2-m3''' || reranker || 568M
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| |-
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| | '''FLUX.2-klein-4B''' || image || 4B
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| | '''GLiNER2.5-Multi-V1''' || privacy || 287M, mDeBERTa-v3-base — privacy filter: [https://api-llm-privacy-proxy-gliner2.infocepo.com api-llm-privacy-proxy-gliner2]
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| |-
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| | '''Public-Cloud''' || '''GLM-5.3-Flash''' || max || 320B MoE / 18B actif — 1M contexte, multimodal
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| |-
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| | rowspan="2" style="text-align:center; vertical-align:middle; background:#f0f0f0;"| '''LOCAL'''
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| | '''Qwen3.5-9B''' || flash, default, thinking, vision || Option 1, 9B dense
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| |-
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| | '''Qwen3.8-27B''' || flash, default, thinking, vision, max || Option 2, 27B dense
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| |}
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| == Frameworks ==
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| {| class="wikitable"
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| ! Outil !! Usage
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| |-
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| | '''Hermes''' || Usage général, raisonnement
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| |-
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| | '''OpenCode''' || DevSecOps
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| |-
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| | '''Kilo Code''' || Développement pur
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| |}
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| == Mémoire ==
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| Pour la couche d'embedding vectoriel (RAG, mémoire sémantique) :
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| * '''BGE-M3''' — 568M paramètres, architecture XLM-RoBERTa. Multi-fonction (dense + sparse + multi-vector), 100+ langues, licence MIT. Idéal comme moteur de mémoire à échelle.
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| = Nouveautés =
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| == Nouveautés implémentés ==
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| * [https://github.com/ynotopec/api-audio2txt '''api-audio2txt'''] (2026-09-25) : Fast audio-to-text transcription API with real-time Whisper model support
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| * [https://github.com/ynotopec/translate-rt '''translate-rt'''] (2026-09-24) : Real-time translation API — multilingual translation with low latency
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| * [https://github.com/ynotopec/api-txt2image '''api-txt2image'''] (2026-09-20) : Text-to-image generation API for AI-powered image creation
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| * [https://github.com/ynotopec/api-rag '''api-rag'''] (2026-09-19) : RAG API — semantic search over knowledge bases with retrieval-augmented generation
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| * [https://github.com/ynotopec/gouv-fr-code-package-mi '''gouv-fr-code-package-mi'''] (2026-09-19) : gouv-fr-code-package-mi
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| * [https://github.com/ynotopec/agent-saas '''agent-saas'''] (2026-09-17) : SaaS AI agent platform — deploy autonomous AI agents as a service
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| * [https://github.com/ynotopec/api-llm-privacy-proxy-gliner2-5 '''api-llm-privacy-proxy-gliner2-5'''] (2026-09-16) : api-llm-privacy-proxy-gliner2-5
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| * [https://github.com/ynotopec/qwen36-sglang '''qwen36-sglang'''] (2026-09-15) : Qwen 3.6 deployment with SGLang for high-performance inference
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| * [https://github.com/ynotopec/omnivoice-tts '''omnivoice-tts'''] (2026-09-13) : omnivoice-tts
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| * [https://github.com/ynotopec/api-llm-privacy-proxy-gliner2 '''api-llm-privacy-proxy-gliner2'''] (2026-09-10) : GLiNER2-powered LLM privacy proxy for named entity detection and filtering
| |
| * [https://github.com/ynotopec/tricoteuses-k8s '''tricoteuses-k8s'''] (2026-09-08) : Kubernetes deployment for tricoteuses-juridique
| |
| * [https://github.com/ynotopec/dashboard-superset-mcp '''dashboard-superset-mcp'''] (2026-09-03) : Automate Apache Superset dashboard creation via MCP server 6.1.0+
| |
| * [https://github.com/ynotopec/superset-k8s '''superset-k8s'''] (2026-09-01) : superset-k8s
| |
| * [https://github.com/ynotopec/dind-build '''dind-build'''] (2026-08-31) : Docker-in-Docker build factory on Kubernetes — build, push, pull via K8s DinD pod
| |
| * [https://github.com/ynotopec/quality-gate '''quality-gate'''] (2026-08-28) : quality-gate
| |
| * [https://github.com/ynotopec/api-convert2md '''api-convert2md'''] (2026-08-26) : Document-to-Markdown conversion API — clean markdown from various formats
| |
| * [https://github.com/ynotopec/trafilatura-local '''trafilatura-local'''] (2026-08-23) : trafilatura-local
| |
| * [https://github.com/ynotopec/models-todo '''models-todo'''] (2026-08-19) : models-todo
| |
| * [https://github.com/ynotopec/minimax-music3 '''minimax-music3'''] (2026-08-18) : minimax-music3
| |
| * [https://github.com/ynotopec/hermes-img-gen-infocepo '''hermes-img-gen-infocepo'''] (2026-08-15) : hermes-img-gen-infocepo
| |
| * [https://github.com/ynotopec/api-llm-custom '''api-llm-custom'''] (2026-08-13) : Custom LLM proxy API — route requests to any language model backend
| |
| * [https://github.com/ynotopec/infocepo-infra-mcp '''infocepo-infra-mcp'''] (2026-08-12) : infocepo-infra-mcp
| |
| * [https://github.com/ynotopec/api-mcp-openai '''api-mcp-openai'''] (2026-08-11) : AI MCP OpenAI Integration
| |
| * [https://github.com/ynotopec/api-embedding '''api-embedding'''] (2026-08-07) : Text embedding API — convert text to vector embeddings for semantic search
| |
| * [https://github.com/ynotopec/api-reranker '''api-reranker'''] (2026-08-06) : Cross-encoder reranking API for improving search retrieval quality
| |
| * [https://github.com/ynotopec/nemotron-embed '''nemotron-embed'''] (2026-07-31) : nemotron-embed
| |
| * [https://github.com/ynotopec/coder-brain '''coder-brain'''] (2026-07-28) : AI-powered code analysis and generation engine
| |
| | |
| | |
| * [https://github.com/vllm-project/vllm '''vllm'''] (2026-09-29) : A high-throughput and memory-efficient inference and serving engine for LLMs (93k⭐)
| |
| * [https://github.com/OpenHands/OpenHands '''OpenHands'''] (2026-09-29) : AI-Driven Development: open-source AI agent platform (89k⭐)
| |
| * [https://github.com/LibreChat-AI/LibreChat '''LibreChat'''] (2026-09-29) : Enhanced ChatGPT Clone: Features Agents, MCP, Skills, DeepSeek, Anthropic, AWS, OpenAI, Responses API (45k⭐)=== Top tasks ===
| |
| * [https://github.com/n8n-io/n8n '''n8n'''] (2026-09-27) : Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or use cloud. (206k⭐)
| |
| * [https://github.com/QwenLM/qwen-code '''qwen-code'''] (2026-09-27) : An open-source AI coding agent that lives in your terminal. (28k⭐)
| |
| * [https://github.com/vercel/ai '''ai'''] (2026-09-27) : The AI Toolkit for TypeScript. From the creators of Next.js, the AI SDK is a free open-source library for building AI-powered chatbots, assistants and more. (26k⭐)
| |
| * [https://github.com/Significant-Gravitas/AutoGPT '''AutoGPT'''] (2026-09-21) : AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters. (187k⭐)
| |
| * [https://github.com/labring/FastGPT '''FastGPT'''] (2026-09-21) : FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities ... (29k⭐)
| |
| * [https://github.com/Tencent/WeKnora '''WeKnora'''] (2026-09-21) : Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and sel... (28k⭐)
| |
| * [https://github.com/NousResearch/hermes-agent '''hermes-agent'''] (2026-09-15) : The agent that grows with you (245k⭐)
| |
| * [https://github.com/ggml-org/llama.cpp '''llama.cpp'''] (2026-09-15) : LLM inference in C/C++ (128k⭐)
| |
| * [https://github.com/supabase/supabase '''supabase'''] (2026-09-15) : The Postgres development platform. Supabase gives you a dedicated Postgres database to build your... (109k⭐)
| |
| * [https://github.com/BerriAI/litellm '''litellm'''] (2026-09-13) : Passerelle AI qui appelle 100+ APIs LLM. Rust core avec SDK Python. (58k⭐)
| |
| * [https://github.com/CherryHQ/cherry-studio '''cherry-studio'''] (2026-09-13) : Studio de productivité IA avec chat intelligent, agents autonomes et 300+ assistants. (51k⭐)
| |
| * [https://github.com/ruvnet/ruflo '''ruflo'''] (2026-09-13) : Harnais d'agents intelligent multi-joueurs. Déployer des essaims coordonnés avec mémoire locale. (72k⭐)
| |
| * [https://github.com/openclaw/openclaw '''openclaw'''] (2026-09-12) : Plateforme IA autonome multi-plateforme. Tout système d'exploitation. (389k⭐)
| |
| * [https://github.com/affaan-m/ECC '''ECC'''] (2026-09-12) : Optimisation performance agents IA - compétences, mémoire, sécurité. (256k⭐)
| |
| * [https://github.com/tt-a1i/archify '''archify'''] (2026-09-11) : Agent skill for architecture, workflow, data-flow, and lifecycle diagrams — self-contained HTML with motion and crisp export. (58k⭐, 12k/semaine)
| |
| * [https://github.com/mksglu/context-mode '''context-mode'''] (2026-09-11) : Context window optimization for AI coding agents. Sandboxes tool output, persists session memory, enforces routing via MCP. (22k⭐, 1.6k/semaine)
| |
| * [https://github.com/openai/plugins '''plugins'''] (2026-09-11) : OpenAI Plugins — plugin ecosystem for OpenAI models. (6k⭐, 1k/semaine)
| |
| * [https://github.com/microsoft/markitdown '''markitdown'''] (2026-09-11) : Conversion fichiers et documents bureautiques vers Markdown — pipeline de données pour pipelines IA. (182k⭐, 4.5k/semaine)
| |
| * [https://github.com/ayghri/i-have-adhd '''i-have-adhd'''] (2026-09-11) : Agent skill pour sortie ADHD-friendly — formatage clair et direct des réponses d'agents IA. (38k⭐, 10k/semaine)
| |
| * [https://github.com/cathrynlavery/diagram-design '''diagram-design'''] (2026-09-11) : 38 types de diagrammes éditoriaux pour agents de codage (Claude, Codex, Pi). HTML + SVG auto-contenu. (37k⭐, 7k/semaine)
| |
| * [https://github.com/anthropics/skills '''skills'''] (2026-09-11) : Repository public officiel des compétences d'agents Anthropic. (175k⭐, 2.2k/semaine)
| |
| * [https://github.com/microsoft/presidio '''Presidio'''] : anonymisation / masquage PII, socle RGPD obligatoire.
| |
| * [https://github.com/temporalio/temporal '''Temporal'''] : orchestration de workflows critiques et fiables.
| |
| * [https://github.com/diegosouzapw/OmniRoute '''OmniRoute'''] : passerelle IA unifiée, auto-fallback, compression de tokens.
| |
| * [https://github.com/NVIDIA-NeMo/Guardrails '''NeMo Guardrails'''] : garde-fous et politiques de sécurité IA.
| |
| * [https://github.com/langfuse/langfuse '''Langfuse'''] : observabilité, evals et metrics pour pipelines IA.
| |
| * [https://github.com/vllm-project/LMCache '''LMCache'''] : couche KV cache ultra-rapide, latence d'inférence réduite.
| |
| * [https://github.com/meshery/meshery '''Meshery'''] : gestion cloud native multi-cluster, observabilité.
| |
| * [https://github.com/VectifyAI/PageIndex '''PageIndex'''] : RAG vectorless à raisonnement, alternative aux embeddings.
| |
| * '''Coût unitaire par tâche''' : chaque API doit exposer son prix/ml/token en temps réel — pas une facture mensuelle.
| |
| * '''Qualité auto''' : précision/hallucination rate branchés sur les sorties IA, avec rollback automatique si qualité < seuil.
| |
| * [https://github.com/unslothai/unsloth '''unsloth'''] (2026-09-14) : Interface locale pour exécuter et entraîner des LLMs et modèles de diffusion. Optimisation d'inférence. (76k⭐)
| |
| * [https://github.com/All-Hands-AI/OpenHands '''OpenHands'''] (2026-09-14) : Développement piloté par IA — agent autonome qui code, debug et itère. (87k⭐)
| |
| * [https://github.com/ClickHouse/ClickHouse '''ClickHouse'''] (2026-09-16) : Base de données analytique temps réel. (49k⭐)
| |
| * [https://github.com/volcengine/OpenViking '''OpenViking'''] (2026-09-16) : Base de contexte auto-évolutive pour agents IA. Mémoire, RAG et compétences unifiés. (37k⭐)
| |
| * [https://github.com/Mintplex-Labs/anything-llm '''anything-llm'''] (2026-09-16) : Plateforme LLM locale tout-en-un. Vector store, RAG, et assistants autonomes. (66k⭐)
| |
| * [https://github.com/livekit/livekit '''livekit'''] (2026-09-17) : End-to-end realtime stack for connecting humans and AI (21.0k⭐)
| |
| * [https://github.com/infiniflow/ragflow '''ragflow'''] (2026-09-17) : RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent c... (90.8k⭐)
| |
| * [https://github.com/alibaba/open-code-review '''open-code-review'''] (2026-09-17) : Fast, efficient, battle-tested at Alibaba's scale. Hybrid architecture code review tool: deterministic pipelines + LL... (32.4k⭐)
| |
| * [https://github.com/jumpserver/jumpserver '''jumpserver'''] (2026-09-18) : JumpServer is an Open-source Privileged Access Management (PAM) platform with AI-powered capabilities, providing DevOps and IT teams a unified workspace to securely access SSH, RDP, Kubernetes, databases, websites, RemoteApp, VirtualApp, and more. (32k⭐)
| |
| * [https://github.com/mastra-ai/mastra '''mastra'''] (2026-09-18) : Mastra is the modern TypeScript framework for AI-powered applications and agents. (28k⭐)
| |
| * [https://github.com/bytedance/deer-flow '''deer-flow'''] (2026-09-20) : An open-source long-horizon SuperAgent harness that researches, codes, and creat (82.7k⭐)
| |
| * [https://github.com/esengine/DeepSeek-Reasonix '''DeepSeek-Reasonix'''] (2026-09-20) : DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cach (35.6k⭐)
| |
| * [https://github.com/zeroclaw-labs/zeroclaw '''zeroclaw'''] (2026-09-20) : Fast, small, and fully autonomous AI personal assistant infrastructure, any OS, (32.8k⭐)
| |
| * [https://github.com/vllm-project/vllm '''vllm'''] (2026-09-23) : High-throughput LLM inference engine — 92.5k⭐
| |
| * [https://github.com/promptfoo/promptfoo '''promptfoo'''] (2026-09-23) : AI testing, red teaming & pentesting for prompts, agents, and RAGs — 25.4k⭐
| |
| * [https://github.com/elizaOS/eliza '''eliza'''] (2026-09-23) : Open source agentic operating system — 19.4k⭐
| |
| * [https://github.com/n8n '''n8n'''] (2026-09-24) : Fair-code workflow automation platform with native AI capabilities (206k⭐)
| |
| * [https://github.com/dify '''dify'''] (2026-09-24) : Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace (157k⭐)
| |
| * [https://github.com/lobehub '''lobehub'''] (2026-09-24) : LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team (83k⭐)
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| === Backlog / Veille Technologique ===
| |
| | |
| === Agents IA & Orchestration ===
| |
| | |
| * [https://github.com/langchain-ai/langchain '''LangChain'''] : Framework pour applications basées sur les LLM. Le plus mature. (145k⭐).
| |
| * [https://github.com/langgenius/dify '''Dify'''] : Plateforme de développement d'applications IA (LLM Ops). Déployable en self-host. (154k⭐).
| |
| * [https://github.com/vllm-project/vllm '''vllm'''] : Moteur d'inférence haute performance pour LLMs. (91k⭐).
| |
| * [https://github.com/open-webui/open-webui '''open-webui'''] : Interface web IA (Ollama, OpenAI API, MCP). (151k⭐).
| |
| * [https://github.com/ollama/ollama '''ollama'''] : Get up and running with Kimi-K2.6, GLM-5.2, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (180k⭐).
| |
| * [https://github.com/n8n-io/n8n '''n8n'''] : Fair-code workflow automation with native AI. 400+ intégrations. (203k⭐).
| |
| * [https://github.com/milvus-io/milvus '''milvus'''] : Base de données vectorielle haute performance. (46k⭐).
| |
| | |
| === Audio & TTS ===
| |
| * [https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct '''Qwen3-Omni-30B-A3B-Instruct'''] — Modèle multimodal Qwen (audio + texte + image).
| |
| | |
| === Génération & Édition d'Images ===
| |
| * [https://github.com/Comfy-Org/ComfyUI '''ComfyUI''' ] : GUI et backend diffusion model le plus modulaire. (131k⭐)
| |
| | |
| === RAG & Traitement de Documents ===
| |
| | |
| * [https://github.com/paperless-ngx/paperless-ngx '''paperless-ngx'''] : Document management : scan, index, archive. ML, OCR. (45k⭐).
| |
| * [https://github.com/graphify-labs/graphify '''graphify'''] : Transforme codebase en graphe de connaissances interrogeable. (115k⭐).
| |
| * [https://github.com/HKUDS/LightRAG '''LightRAG'''] : RAG simple et rapide. (39k⭐).
| |
| * [https://github.com/microsoft/graphrag '''graphrag'''] : RAG modulaire basé sur les graphes. (35k⭐).
| |
| * [https://github.com/headroomlabs-ai/headroom '''headroom'''] : Compress tool outputs, logs, files et RAG chunks. 20-95% token savings. (69k⭐).
| |
| * [https://github.com/mem0ai/mem0 '''Mem0'''] : — Mémorie à long terme pour agents IA. (64k⭐).
| |
| | |
| === APIs à Développer ===
| |
| * '''Classificateur IA''' — Classification de contenu.
| |
| * '''Résumé mutualisé''' — API de résumé de texte partagée.
| |
| * '''NER''' — Reconnaissance d'entités nommées.
| |
| * '''Compressor''' — Compression de contenu.
| |
| | |
| === Infrastructure & Backend ===
| |
| * [https://github.com/vllm-project/semantic-router '''Semantic Router'''] — Routage sémantique de requêtes vLLM.
| |
| | |
| === Outils Dev ===
| |
| * [https://github.com/Aider-AI/aider '''Aider'''] — Assistant de codage IA en ligne de commande.
| |
| * [https://github.com/continuedev/continue '''Continue'''] — Extension IDE IA (VS Code, JetBrains). (35k⭐)
| |
| | |
| ----
| |
| = Assistants IA & outils cloud =
| |
| | |
| == Assistants IA ==
| |
| | |
| ; '''Assistants IA auto-hébergés'''
| |
| * [https://github.com/nesquena/hermes-webui '''Hermes WebUI'''] + [https://ollama.com '''Ollama'''] + GPU
| |
| : Stack typique pour assistant privé, API OpenAI-compatible et expérimentation locale.
| |
| | |
| == Développement, modèles & veille ==
| |
| | |
| ; '''Découverte de modèles'''
| |
| * [https://huggingface.co/models '''Models Trending''']
| |
| | |
| ; '''Évaluation & benchmarks'''
| |
| * [https://arena.ai/leaderboard/code '''Agentic Evaluation''']
| |
| | |
| ; '''Outils de développement & fine-tuning'''
| |
| * [https://github.com/trending?since=weekly '''Project Trending''']
| |
| * [https://grok.com '''News search''']
| |
| | |
| == Matériel IA & GPU ==
| |
| * NVIDIA GH200
| |
| * DGX Spark
| |
| * [https://www.mouser.fr/ProductDetail/BittWare/RS-GQ-GC1-0109?qs=ST9lo4GX8V2eGrFMeVQmFw%3D%3D '''GROQ LLM accelerator''']
| |
| | |
| ----
| |
| | |
| = API Realtime AI (DEV) =
| |
| | |
| '''Statut :''' environnement DEV, remplaçante prévue de l’API OpenAI pour les cas temps réel.
| |
| | |
| == Configuration ==
| |
| {| class="wikitable"
| |
| ! Variable !! Valeur
| |
| |-
| |
| | OPENAI_API_BASE || <code>wss://api-realtime-ai.ailab.infocepo.com:wait-2026-12/v1</code>
| |
| |-
| |
| | OPENAI_API_KEY || <code>sk-XXXXX</code>
| |
| |}
| |
| | |
| == Dépôt GitHub ==
| |
| * [https://github.com/ynotopec/api-realtime-ai '''ynotopec/api-realtime-ai''']
| |
| | |
| == Page de test ==
| |
| * <code>external-test/half-duplex.html</code> — annulation d’écho + mode half-duplex.
| |
| | |
| == Compatibilité ==
| |
| Remplacer l’URL OpenAI par <code>$OPENAI_API_BASE</code> pour tester compatibilité et performances.
| |
| | |
| ----
| |
| | |
| = API LLM (OpenAI compatible) =
| |
| | |
| * URL de base : <code>https://api-nothink.ailab.infocepo.com/v1</code>
| |
| * Création du token : [https://llm-token.ailab.infocepo.com:wait-2026-12 OPENAI_API_KEY]
| |
| * Documentation : [https://api-nothink.ailab.infocepo.com/docs Documentation API]
| |
| | |
| == Liste des modèles ==
| |
| <pre>
| |
| curl -X GET \
| |
| 'https://api-nothink.ailab.infocepo.com/v1/models' \
| |
| -H 'Authorization: Bearer sk-XXXXX' \
| |
| -H 'accept: application/json' \
| |
| | jq | sed -rn 's#^.*id.*: "(.*)".*$#* \1#p' | sort -u
| |
| </pre>
| |
| | |
| == Modèles ouverts & endpoints internes ==
| |
| | |
| ''Dernière mise à jour : 2026-06-30''
| |
| | |
| Les modèles ci-dessous correspondent à des '''endpoints logiques''' exposés derrière une passerelle.
| |
| | |
| {| class="wikitable"
| |
| ! Endpoint !! Description / usage principal
| |
| |-
| |
| | '''ai-thinking''' || '''qwen3.6 fp8''' – thinking
| |
| |-
| |
| | '''ai-fast''' || '''qwen3.6 fp8''' en mode '''fast''' – vision/OCR/ai-default
| |
| |-
| |
| | '''ai-embedding''' || '''bge-m3''' – recherche sémantique
| |
| |-
| |
| | '''ai-stt''' || '''whisper3-turbo''' – transcription vocale multilingual
| |
| |-
| |
| | '''ai-tts''' || '''OmniVoice''' – TTS multilingual
| |
| |-
| |
| | '''ai-image''' || '''OpenDalle''' – image génération
| |
| |}
| |
| | |
| == Exemple bash ==
| |
| <pre>
| |
| export OPENAI_API_MODEL="ai-default"
| |
| export OPENAI_API_BASE="https://api-nothink.ailab.infocepo.com/v1"
| |
| export OPENAI_API_KEY="sk-XXXXX"
| |
| | |
| promptValue="Quel est ton nom ?"
| |
| jsonValue='{
| |
| "model": "'${OPENAI_API_MODEL}'",
| |
| "messages": [{"role": "user", "content": "'${promptValue}'"}],
| |
| "temperature": 0
| |
| }'
| |
| | |
| curl ${OPENAI_API_BASE}/chat/completions \
| |
| -H "Content-Type: application/json" \
| |
| -H "Authorization: Bearer $OPENAI_API_KEY" \
| |
| -d "${jsonValue}" 2>/dev/null | jq '.choices[0].message.content'
| |
| </pre>
| |
| | |
| == Vue infra LLM ==
| |
| [[File:Litellm-proxy-mermaid-diagram-2024-03-24-205202.png|thumb|right]]
| |
| | |
| '''DEV (au choix)'''
| |
| * '''A.''' <code>LiteLLM → vLLM/SgLang</code> : tests perf / compatibilité
| |
| * '''B.''' <code>LiteLLM → Ollama</code> : simple, rapide à itérer
| |
| * '''C.''' <code>Ollama</code> direct : POC ultra-léger
| |
| | |
| '''DEV – modèle FR / résumé'''
| |
| * <code>LiteLLM → Ollama /v1</code>
| |
| | |
| '''PROD'''
| |
| * '''Standard :''' <code>LiteLLM → vLLM/SgLang</code>
| |
| * '''Pont DEV→PROD :''' <code>LiteLLM (DEV) → LiteLLM (PROD) → vLLM/SgLang</code>
| |
| | |
| '''Notes :'''
| |
| * '''LiteLLM''' = passerelle unique (clés, quotas, logs)
| |
| * '''vLLM/SgLang''' = performance / stabilité en charge
| |
| * '''Ollama''' = simplicité de prototypage
| |
| | |
| ----
| |
| | |
| = API Image to Text =
| |
| | |
| * Utilise l’API LLM avec un endpoint adapté à l’OCR / VLM.
| |
| * Modèle recommandé : <code>ai-vision</code>
| |
| | |
| == Exemple bash ==
| |
| <pre>
| |
| OPENAI_API_KEY=sk-XXXXX
| |
| | |
| base64 -w0 "/path/to/image.png" > img.b64
| |
| | |
| jq -n --rawfile img img.b64 \
| |
| '{
| |
| model: "ai-vision",
| |
| messages: [
| |
| {
| |
| role: "user",
| |
| content: [
| |
| { "type": "text", "text": "Décris cette image." },
| |
| {
| |
| "type": "image_url",
| |
| "image_url": { "url": ("data:image/png;base64," + ($img | rtrimstr("\n"))) }
| |
| }
| |
| ]
| |
| }
| |
| ]
| |
| }' > payload.json
| |
| | |
| curl https://api-nothink.ailab.infocepo.com/v1/chat/completions \
| |
| -H "Authorization: Bearer $OPENAI_API_KEY" \
| |
| -H "Content-Type: application/json" \
| |
| --data-binary @payload.json
| |
| </pre>
| |
| | |
| == Exemple Python ==
| |
| <pre>
| |
| import base64
| |
| import json
| |
| import requests
| |
| import os
| |
| | |
| API_KEY = os.getenv("OPENAI_API_KEY")
| |
| MODEL = "ai-vision"
| |
| IMG_PATH = "/path/to/image.png"
| |
| API_URL = "https://api-nothink.ailab.infocepo.com/v1/chat/completions"
| |
| | |
| with open(IMG_PATH, "rb") as f:
| |
| img_b64 = base64.b64encode(f.read()).decode("utf-8")
| |
| | |
| payload = {
| |
| "model": MODEL,
| |
| "messages": [
| |
| {
| |
| "role": "user",
| |
| "content": [
| |
| {"type": "text", "text": "Décris cette image."},
| |
| {
| |
| "type": "image_url",
| |
| "image_url": {"url": f"data:image/png;base64,{img_b64}"}
| |
| }
| |
| ]
| |
| }
| |
| ]
| |
| }
| |
| | |
| headers = {
| |
| "Authorization": f"Bearer {API_KEY}",
| |
| "Content-Type": "application/json"
| |
| }
| |
| | |
| response = requests.post(API_URL, headers=headers, data=json.dumps(payload))
| |
| | |
| if response.ok:
| |
| print(json.dumps(response.json(), indent=2, ensure_ascii=False))
| |
| else:
| |
| print(f"Erreur {response.status_code}: {response.text}")
| |
| </pre>
| |
| | |
| ----
| |
| | |
| = API STT =
| |
| | |
| * URL : <code>https://api-audio2txt.ailab.infocepo.com/v1</code>
| |
| * Clé : <code>OPENAI_API_KEY=sk-XXXXX</code>
| |
| * Modèle : <code>whisper-1</code>
| |
| * Documentation : [https://api-audio2txt.ailab.infocepo.com/docs API STT docs]
| |
| | |
| == Exemple Python ==
| |
| <pre>
| |
| import requests
| |
| | |
| OPENAI_API_KEY = 'sk-XXXXX'
| |
| | |
| url = 'https://api-audio2txt.ailab.infocepo.com/v1/audio/transcriptions'
| |
| headers = {
| |
| 'Authorization': f'Bearer {OPENAI_API_KEY}',
| |
| }
| |
| files = {
| |
| 'file': ('file.opus', open('/path/to/file.opus', 'rb')),
| |
| 'model': (None, 'whisper-1')
| |
| }
| |
| | |
| response = requests.post(url, headers=headers, files=files)
| |
| print(response.json())
| |
| </pre>
| |
| | |
| == Exemple curl ==
| |
| <pre>
| |
| [ ! -f /tmp/test.ogg ] && wget "https://upload.wikimedia.org/wikipedia/commons/1/17/Fables_de_La_Fontaine_Livre_1_01.ogg" -O /tmp/test.ogg
| |
| | |
| export OPENAI_API_KEY=sk-XXXXX
| |
| | |
| curl https://api-audio2txt.ailab.infocepo.com/v1/audio/transcriptions \
| |
| -H "Authorization: Bearer $OPENAI_API_KEY" \
| |
| -F model="whisper-1" \
| |
| -F file="@/tmp/test.ogg"
| |
| </pre>
| |
| | |
| == Notes ==
| |
| * Plusieurs formats audio sont acceptés.
| |
| * Le flux final est normalisé en '''16 kHz mono'''.
| |
| * Pour une qualité optimale : privilégier '''OPUS 16 kHz mono'''.
| |
| | |
| == UI ==
| |
| * [https://translate-rt.ailab.infocepo.com translate-rt]
| |
| | |
| ----
| |
| | |
| = API TTS =
| |
| | |
| * URL : <code>https://api-tts-omnivoice.ailab.infocepo.com/v1</code>
| |
| * Clé : <code>OPENAI_API_KEY=sk-XXXXX</code>
| |
| * Documentation : [https://api-tts-omnivoice.ailab.infocepo.com/docs API TTS docs]
| |
| | |
| == Exemple ==
| |
| <pre>
| |
| export OPENAI_API_KEY=sk-XXXXX
| |
| | |
| curl https://api-tts-omnivoice.ailab.infocepo.com/v1/audio/speech \
| |
| -H "Authorization: Bearer $OPENAI_API_KEY" \
| |
| -H "Content-Type: application/json" \
| |
| -d '{
| |
| "model": "gpt-4o-mini-tts",
| |
| "input": "Bonjour, ceci est un test de synthèse vocale.",
| |
| "voice": "coral",
| |
| "instructions": "Speak in a cheerful and positive tone.",
| |
| "response_format": "opus"
| |
| }' | ffplay -i -
| |
| </pre>
| |
| | |
| ----
| |
| | |
| = API Text to Image =
| |
| | |
| * URL : <code>https://api-txt2image.ailab.infocepo.com/v1</code>
| |
| * Clé API : <code>OPENAI_API_KEY=sk-...</code>
| |
| * Documentation : [https://api-txt2image.ailab.infocepo.com/docs API TXT2IMAGE docs]
| |
| | |
| == Exemple ==
| |
| <pre>
| |
| export OPENAI_API_KEY=EMPTY
| |
| | |
| curl https://api-txt2image.ailab.infocepo.com/v1/images/generations \
| |
| -H "Content-Type: application/json" \
| |
| -H "Authorization: Bearer $OPENAI_API_KEY" \
| |
| -d '{
| |
| "prompt": "a photo of a happy corgi puppy sitting and facing forward, studio light, longshot",
| |
| "n": 1,
| |
| "size": "1024x1024"
| |
| }'
| |
| </pre>
| |
| | |
| ----
| |
| | |
| = API Diarization =
| |
| | |
| * Documentation : [https://api-diarization.ailab.infocepo.com/docs API Diarization docs]
| |
| | |
| == Exemple ==
| |
| <pre>
| |
| wget "https://upload.wikimedia.org/wikipedia/commons/6/60/Mike_Peters_on_Politics_and_Emotion_%28Interview_1984%29.mp3" -O /tmp/test.mp3
| |
| | |
| curl -X POST "https://api-diarization.ailab.infocepo.com/upload-audio/" \
| |
| -H "Authorization: Bearer token1" \
| |
| -F "file=@/tmp/test.mp3"
| |
| </pre>
| |
| | |
| ----
| |
| | |
| = API Summary =
| |
| | |
| * Documentation : [https://api-summary.ailab.infocepo.com:wait-2026-12/docs API Summary docs]
| |
| | |
| == Exemple ==
| |
| <pre>
| |
| text="The tower is 324 metres tall and is one of the most recognizable monuments in the world."
| |
| | |
| json_payload=$(jq -nc --arg text "$text" '{"text": $text}')
| |
| | |
| curl -X POST https://api-summary.ailab.infocepo.com:wait-2026-12/summary/ \
| |
| -H "Content-Type: application/json" \
| |
| -d "$json_payload"
| |
| </pre>
| |
| | |
| ----
| |
| | |
| = API Text Embeddings =
| |
| | |
| * URL : <code>https://api-embedding.ailab.infocepo.com/v1</code>
| |
| * Documentation : [https://api-embedding.ailab.infocepo.com/docs Documentation]
| |
| | |
| == Exemple ==
| |
| <pre>
| |
| curl https://api-embedding.ailab.infocepo.com/v1/embeddings \
| |
| -X POST \
| |
| -d '{"model":"bge-m3","input":"What is Deep Learning?"}' \
| |
| -H 'Content-Type: application/json'
| |
| </pre>
| |
| | |
| ----
| |
| | |
| = API DB Vectors (ChromaDB) =
| |
| | |
| == Production ==
| |
| * URL : <code>https://chromadb.ailab.infocepo.com:wait-2026-12</code>
| |
| * Token : <code>XXXXX</code>
| |
| | |
| == Lab ==
| |
| <pre>
| |
| export CHROMA_HOST=https://chromadb.c1.ailab.infocepo.com:wait-2026-12
| |
| export CHROMA_PORT=443
| |
| export CHROMA_TOKEN=XXXX
| |
| </pre>
| |
| | |
| == Exemple curl ==
| |
| <pre>
| |
| curl -v "${CHROMA_HOST}"/api/v1/collections \
| |
| -H "Authorization: Bearer ${CHROMA_TOKEN}"
| |
| </pre>
| |
| | |
| == Exemple Python ==
| |
| <pre>
| |
| import chromadb
| |
| from chromadb.config import Settings
| |
| | |
| def chroma_http(host, port=80, token=None):
| |
| return chromadb.HttpClient(
| |
| host=host,
| |
| port=port,
| |
| ssl=host.startswith('https') or port == 443,
| |
| settings=(
| |
| Settings(
| |
| chroma_client_auth_provider='chromadb.auth.token.TokenAuthClientProvider',
| |
| chroma_client_auth_credentials=token,
| |
| ) if token else Settings()
| |
| )
| |
| )
| |
| | |
| client = chroma_http(CHROMA_HOST, CHROMA_PORT, CHROMA_TOKEN)
| |
| collections = client.list_collections()
| |
| print(collections)
| |
| </pre>
| |
| | |
| == Déployer sa propre instance ==
| |
| <pre>
| |
| export nameSpace=your_namespace
| |
| domainRoot=ailab.infocepo.com
| |
| | |
| helm repo add chroma https://amikos-tech.github.io/chromadb-chart/
| |
| helm repo update
| |
| | |
| helm upgrade --install chromadb chroma/chromadb -n ${nameSpace} \
| |
| --set chromadb.apiVersion="0.4.24" \
| |
| --set ingress.enabled=true \
| |
| --set ingress.hosts[0].host="${nameSpace}-chromadb.${domainRoot}" \
| |
| --set ingress.hosts[0].paths[0].path=/ \
| |
| --set ingress.hosts[0].paths[0].pathType=ImplementationSpecific \
| |
| --set ingress.annotations."cert-manager\.io/cluster-issuer"=letsencrypt-prod \
| |
| --set ingress.tls[0].secretName=${nameSpace}-chromadb.${domainRoot}-tls \
| |
| --set ingress.tls[0].hosts[0]="${nameSpace}-chromadb.${domainRoot}"
| |
| | |
| kubectl -n ${nameSpace} patch ingress/chromadb --type=json \
| |
| -p '[{"op":"add","path":"/metadata/annotations/nginx.ingress.kubernetes.io~1proxy-body-size","value":"0"}]'
| |
| </pre>
| |
| | |
| == Récupérer le token ==
| |
| <pre>
| |
| kubectl --namespace ${nameSpace} get secret chromadb-auth \
| |
| -o jsonpath="{.data.token}" | base64 --decode && echo
| |
| </pre>
| |
| | |
| ----
| |
| | |
| = Registry =
| |
| | |
| * URL : [https://registry.ailab.infocepo.com:wait-2026-09 registry.ailab.infocepo.com:wait-2026-09]
| |
| * Login : <code>user</code>
| |
| * Password : <code>XXXXX</code>
| |
| | |
| == Exemple ==
| |
| <pre>
| |
| curl -u "user:XXXXX" https://registry.ailab.infocepo.com:wait-2026-09/v2/_catalog
| |
| </pre>
| |
| | |
| == Exemple K8S ==
| |
| <pre>
| |
| deploymentName=
| |
| nameSpace=
| |
| | |
| kubectl -n ${nameSpace} create secret docker-registry pull-secret \
| |
| --docker-server=registry.ailab.infocepo.com:wait-2026-09 \
| |
| --docker-username=user \
| |
| --docker-password=XXXXX \
| |
| --docker-email=contact@example.com
| |
| | |
| kubectl -n ${nameSpace} patch deployment ${deploymentName} \
| |
| -p '{"spec":{"template":{"spec":{"imagePullSecrets":[{"name":"pull-secret"}]}}}}'
| |
| </pre>
| |
| | |
| ----
| |
| | |
| = Stockage objet externe (S3) =
| |
| | |
| * Endpoint : <code>https://s3.ailab.infocepo.com:wait-2026-09</code>
| |
| * Access key : <code>XXXX</code>
| |
| * Secret key : <code>XXXX</code>
| |
| | |
| Un bucket nommé <code>ORG</code> a été créé pour stocker des documents de démonstration.
| |
| | |
| ----
| |
| | |
| = RAG optimisation =
| |
| | |
| * Embeddings : <code>BAAI/bge-m3</code>
| |
| * <code>chunk_size=1000</code>
| |
| * <code>chunk_overlap=100</code>
| |
| * LLM : <code>qwen3.6</code>
| |
| * Pour les PDF mixtes : '''PDF → image → OCR / VLM''' peut améliorer les résultats.
| |
| | |
| ----
| |
| | |
| = Workflow =
| |
| | |
| Autoroute directe : code → CI/CD → deploy → monitoring. Pas de comité, pas de responsable désigné — chaque étape se déclenche automatiquement si la précédente passe. Si ça rate, alerte directe sans passer par un humain sauf décision explicite de fallback.
| |
| | |
| ----
| |
| | |
| = Environnements =
| |
| | |
| == Hors production ==
| |
| * Utiliser [https://datalab.ailab.infocepo.com:wait-2026-12 datalab]
| |
| * Support : canal Mattermost Offre IA
| |
| * Le pseudo utilisateur doit respecter la convention interne
| |
| * Demander si besoin un accès Linux + Kubernetes
| |
| | |
| == Production (best-effort) ==
| |
| * Publier le code applicatif, les secrets (format SOPS), le Dockerfile et le code infra (Helm ou manifests K8S) sur Git
| |
| * Demander un namespace
| |
| * Lire la documentation de surveillance associée
| |
| | |
| == Limites de l’infrastructure ==
| |
| * Les charges GPU sont intentionnellement limitées en journée.
| |
| * Coût unitaire mesuré automatiquement (dashboards temps réel, pas Excel).
| |
|
| |
| ----
| |
| | |
| = Cloud Lab & projets d’audit =
| |
| | |
| [[File:Infocepo.drawio.png|400px|Cloud Lab reference diagram]]
| |
| | |
| Le '''Cloud Lab''' fournit des scénarios reproductibles : audit d’infrastructure, migration cloud, automatisation, haute disponibilité.
| |
| | |
| == Projet d’audit ==
| |
| ; '''[[ServerDiff.sh]]'''
| |
| Script Bash d’audit permettant de :
| |
| * détecter les dérives de configuration,
| |
| * comparer plusieurs environnements,
| |
| * préparer un plan de migration ou de remédiation.
| |
| | |
| == Exemple de migration cloud ==
| |
| [[File:Diagram-migration-ORACLE-KVM-v2.drawio.png|400px|Cloud migration diagram]]
| |
| | |
| {| class="wikitable"
| |
| ! Tâche !! Description !! Durée (jours)
| |
| |-
| |
| | Audit infrastructure || 82 services, audit automatisé via '''ServerDiff.sh''' || 1.5
| |
| |-
| |
| | Diagramme d’architecture || Conception visuelle et documentation || 1.5
| |
| |-
| |
| | Contrôles de conformité || 2 clouds, 6 hyperviseurs, 6 To RAM || 1.5
| |
| |-
| |
| | Installation plateforme cloud || Déploiement des environnements cibles || 1.0
| |
| |-
| |
| | Vérification de stabilité || Premiers tests fonctionnels || 0.5
| |
| |-
| |
| | Étude d’automatisation || Identification des tâches répétitives || 1.5
| |
| |-
| |
| | Développement des templates || 6 templates, 8 environnements, 2 clouds / OS || 1.5
| |
| |-
| |
| | Diagramme de migration || Illustration du processus || 1.0
| |
| |-
| |
| | Écriture du code de migration || 138 lignes (voir '''MigrationApp.sh''') || 1.5
| |
| |-
| |
| | Stabilisation || Validation de la reproductibilité || 1.5
| |
| |-
| |
| | Benchmark cloud || Comparaison vs legacy || 1.5
| |
| |-
| |
| | Réglage des temps d’arrêt || Calcul du downtime || 0.5
| |
| |-
| |
| | Chargement VM || 82 VMs : OS, code, 2 IP par VM || 0.1
| |
| |-
| |
| ! colspan=2 align="right"| '''Total''' !! 15 jours.homme
| |
| |}
| |
| | |
| === Vérifications de stabilité (HA minimale) ===
| |
| {| class="wikitable"
| |
| ! Action !! Résultat attendu
| |
| |-
| |
| | Extinction d’un nœud || Tous les services redémarrent automatiquement sur les autres nœuds
| |
| |-
| |
| | Extinction / redémarrage simultané de tous les nœuds || Les services repartent correctement après reboot
| |
| |}
| |
| | |
| | |
| '''Autonomie testée''' : vérifier que la migration fonctionne seule en tirant le repo et lançant le script, sans personne qui connaît le système de tête.
| |
| | |
| ----
| |
| | |
| = Architecture web & bonnes pratiques =
| |
| | |
| [[File:WebModelDiagram.drawio.png|400px|Reference web architecture]]
| |
| | |
| Principes de conception :
| |
| | |
| * privilégier une infrastructure '''simple, modulaire et flexible''',
| |
| * rapprocher le contenu du client (GDNS ou équivalent),
| |
| * utiliser des load balancers réseau (LVS, IPVS),
| |
| * comparer les coûts et éviter le '''vendor lock-in''',
| |
| * pour TLS :
| |
| ** '''HAProxy''' pour les frontends rapides,
| |
| ** '''Envoy''' pour les cas avancés (mTLS, HTTP/2/3),
| |
| * pour le cache :
| |
| ** '''Varnish''', '''Apache Traffic Server''',
| |
| * favoriser les stacks open-source,
| |
| * utiliser files, buffers, queues et quotas pour lisser les pics.
| |
| | |
| == Références ==
| |
| * [https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia infrastructure]
| |
| * [https://github.com/systemdesign42/system-design System Design GitHub]
| |
| | |
| ----
| |
| | |
| = Comparatif des grandes plateformes cloud =
| |
| | |
| {| class="wikitable"
| |
| ! Fonctionnalité !! Kubernetes !! OpenStack !! AWS !! Bare-metal !! HPC !! CRM !! oVirt
| |
| |-
| |
| | '''Outils de déploiement''' || Helm, YAML, ArgoCD, Juju || Ansible, Terraform, Juju || CloudFormation, Terraform, Juju || Ansible, Shell || xCAT, Clush || Ansible, Shell || Ansible, Python
| |
| |-
| |
| | '''Méthode de bootstrap''' || API || API, PXE || API || PXE, IPMI || PXE, IPMI || PXE, IPMI || PXE, API
| |
| |-
| |
| | '''Contrôle routeur''' || Kube-router || Router/Subnet API || Route Table / Subnet API || Linux, OVS || xCAT || Linux || API
| |
| |-
| |
| | '''Contrôle firewall''' || Istio, NetworkPolicy || Security Groups API || Security Group API || Linux firewall || Linux firewall || Linux firewall || API
| |
| |-
| |
| | '''Virtualisation réseau''' || VLAN, VxLAN || VPC || VPC || OVS, Linux || xCAT || Linux || API
| |
| |-
| |
| | '''DNS''' || CoreDNS || DNS-Nameserver || Route 53 || GDNS || xCAT || Linux || API
| |
| |-
| |
| | '''Load balancer''' || Kube-proxy, LVS || LVS || Network Load Balancer || LVS || SLURM || Ldirectord || N/A
| |
| |-
| |
| | '''Stockage''' || Local, cloud, PVC || Swift, Cinder, Nova || S3, EFS, EBS, FSx || Swift, XFS, EXT4, RAID10 || GPFS || SAN || NFS, SAN
| |
| |}
| |
| | |
| Cette table sert de point de départ pour choisir la bonne stack selon :
| |
| * le niveau de contrôle souhaité,
| |
| * le contexte (on-prem, cloud public, HPC…),
| |
| * les outils d’automatisation existants.
| |
| | |
| ----
| |
| | |
| = Haute disponibilité, HPC & DevSecOps =
| |
| | |
| == Haute disponibilité avec Corosync & Pacemaker ==
| |
| [[File:HA-REF.drawio.png|400px|HA cluster architecture]]
| |
| | |
| Principes :
| |
| * clusters multi-nœuds ou multi-sites,
| |
| * fencing via IPMI,
| |
| * provisioning PXE / NTP / DNS / TFTP,
| |
| * pour 2 nœuds : attention au split-brain,
| |
| * 3 nœuds ou plus recommandés en production.
| |
| | |
| === Ressources fréquentes ===
| |
| * multipath, LUNs, LVM, NFS,
| |
| * processus applicatifs,
| |
| * IP virtuelles, DNS, listeners réseau.
| |
| | |
| == HPC ==
| |
| [[File:HPC.drawio.png|400px|Overview of an HPC cluster]]
| |
| | |
| * orchestration de jobs (SLURM ou équivalent),
| |
| * stockage partagé haute performance,
| |
| * intégration possible avec des workloads IA.
| |
| | |
| == DevSecOps ==
| |
| [[File:DSO-POC-V3.drawio.png|400px|DevSecOps reference design]]
| |
| | |
| * CI/CD avec contrôles de sécurité intégrés,
| |
| * observabilité dès la conception,
| |
| * scans de vulnérabilité,
| |
| * gestion des secrets,
| |
| * policy-as-code.
| |
| | |
| ----
| |
| | |
| = News & trends =
| |
| | |
| * [https://www.youtube.com/@lev-selector/videos Top AI News]
| |
| * [https://betterprogramming.pub/color-your-captions-streamlining-live-transcriptions-with-diart-and-openais-whisper-6203350234ef Real-time transcription with Diart + Whisper]
| |
| * [https://github.com/openai-translator/openai-translator OpenAI Translator]
| |
| * [https://opensearch.org/docs/latest/search-plugins/conversational-search Opensearch with LLM]
| |
| | |
| ----
| |
| | |
| = Formation & apprentissage =
| |
| | |
| * [https://www.youtube.com/watch?v=4Bdc55j80l8 Transformers Explained]
| |
| * Labs, scripts et retours d’expérience concrets dans le projet Cloud Lab
| |
| | |
| ----
| |
| | |
| = Liens cloud & IT utiles =
| |
| | |
| * [https://cloud.google.com/free/docs/aws-azure-gcp-service-comparison Cloud Providers Compared]
| |
| * [https://global-internet-map-2021.telegeography.com/ Global Internet Topology Map]
| |
| * [https://landscape.cncf.io/?fullscreen=yes CNCF Official Landscape]
| |
| * [https://openapm.io OpenAPM]
| |
| * [https://access.redhat.com/downloads/content/package-browser Red Hat Package Browser]
| |
| * [https://www.silkhom.com/barometre-2021-des-tjm-dans-informatique-digital Baromètre TJM IT]
| |
| * [https://www.glassdoor.fr/salaire/Hays-Salaires-E10166.htm Indicateurs salariaux IT]
| |
| | |
| ----
| |
| | |
| = Outils collaboratifs =
| |
| | |
| == Dépôts de code ==
| |
| * [https://github.com/ynotopec '''GitHub ynotopec''']
| |
| | |
| == Base de connaissance ==
| |
| * [https://infocepo.com '''ce wiki''']
| |
| | |
| == Messagerie ==
| |
| * contact interne / support selon les projets
| |
| | |
| == SSO ==
| |
| * [https://auth-lab.ailab.infocepo.com:wait-2026-12/auth Keycloak]
| |
| | |
| ----
| |
| | |
| = À propos & contributions =
| |
| | |
| Suggestions de corrections, améliorations de schémas, retours d’expérience ou nouveaux labs bienvenus.
| |
| | |
| Ce wiki a vocation à rester un '''laboratoire vivant''' pour l’IA, le cloud et l’automatisation.
| |
