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| [[File:Infocepo-picture.png|thumb|right|Discover cloud and AI on infocepo.com]] | | [[File:Infocepo-picture.png|thumb|right|Cloud, AI and Labs on infocepo.com]] |
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| = infocepo.com – Cloud, AI & Labs = | | = infocepo.com – Cloud, AI & Labs = |
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| Welcome to the '''infocepo.com''' portal.
| | Bienvenue sur '''infocepo.com'''. |
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| This wiki is intended for system administrators, cloud engineers, developers, students, and enthusiasts who want to:
| | Ce wiki centralise des ressources sur : |
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| * Understand modern architectures (Kubernetes, OpenStack, bare-metal, HPC…) | | * l’'''infrastructure cloud''' et les architectures distribuées, |
| * Deploy private AI assistants and productivity tools | | * l’'''IA appliquée''' (assistants privés, APIs, RAG, GPU), |
| * Build hands-on labs to learn by doing
| | * les '''labs techniques''' pour apprendre, tester et industrialiser, |
| * Prepare large-scale audits, migrations, and automations | | * les '''scripts et méthodes''' pour l’audit, la migration et l’automatisation. |
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| The goal: turn theory into '''reusable scripts, diagrams, and architectures'''.
| | L’objectif est de transformer des idées et des concepts en '''solutions concrètes, reproductibles et utiles'''. |
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| __TOC__ | | __TOC__ |
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| = Getting started quickly = | | = Accès rapide = |
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| == Recommended paths ==
| | * [https://chat.infocepo.com '''Assistant IA'''] |
| | | * [https://infocepo.com '''Portail principal'''] |
| ; 1. Build a private AI assistant
| | * [[Special:AllPages|'''Toutes les pages''']] |
| * Deploy a typical stack: '''Open WebUI + Ollama + GPU''' (H100 or consumer-grade GPU) | | * [https://github.com/ynotopec '''GitHub'''] |
| * Add a chat model and a summarization model | | * [https://uptime-kuma.ai.lab.infocepo.com/status/ai '''Statut des services''']] |
| * Integrate internal data (RAG, embeddings)
| | * [https://grafana.ai.lab.infocepo.com '''Monitoring''']] |
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| ; 2. Launch a Cloud lab
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| * Create a small cluster (Kubernetes, OpenStack, or bare-metal)
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| * Set up a deployment pipeline (Helm, Ansible, Terraform…)
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| * Add an AI service (transcription, summarization, chatbot…)
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| ; 3. Prepare an audit / migration
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| * Inventory servers with '''ServerDiff.sh'''
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| * Design the target architecture (cloud diagrams) | |
| * Automate the migration with reproducible scripts
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| == Content overview ==
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| * '''AI guides & tools''' : assistants, models, evaluations, GPUs
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| * '''Cloud & infrastructure''' : HA, HPC, web-scale, DevSecOps | |
| * '''Labs & scripts''' : audit, migration, automation
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| * '''Comparison tables''' : Kubernetes vs OpenStack vs AWS vs bare-metal, etc. | |
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| ---- | | ---- |
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| = future = | | = Parcours recommandés = |
| [[File:Automation-full-vs-humans.png|thumb|right|The world after automation]]
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| = AI Assistants & Cloud Tools = | | == Construire un assistant IA privé == |
| | * Déployer une stack type '''Open WebUI + Ollama + GPU''' |
| | * Ajouter des modèles de chat, résumé, OCR ou transcription |
| | * Connecter des documents via '''RAG + embeddings''' |
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| == AI Assistants == | | == Lancer un lab cloud == |
| | | * Créer un environnement Kubernetes, OpenStack ou bare-metal |
| ; '''ChatGPT'''
| | * Déployer avec Helm, Terraform ou Ansible |
| * https://chatgpt.com ChatGPT – Public conversational assistant, suited for exploration, writing, and rapid experimentation. | | * Ajouter des services IA et des outils d’observabilité |
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| ; '''Self-hosted AI assistants'''
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| * https://github.com/open-webui/open-webui Open WebUI + https://www.scaleway.com/en/h100-pcie-try-it-now/ H100 GPU + https://ollama.com Ollama
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| : Typical stack for private assistants, self-hosted LLMs, and OpenAI-compatible APIs.
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| * https://github.com/ynotopec/summarize Private summary – Local, fast, offline summarizer for your own data.
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| == Development, models & tracking ==
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| ; '''Discovering and tracking models'''
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| * https://ollama.com/library LLM Trending – Model library (chat, code, RAG…) for local deployment.
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| * https://huggingface.co/models Models Trending – Model marketplace, filterable by task, size, and license.
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| * https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending Img2txt Trending – Vision-language models (image → text).
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| * https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena Txt2img Evaluation – Image generation model comparisons.
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| ; '''Evaluation & benchmarks'''
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| * https://lmarena.ai/leaderboard ChatBot Evaluation – Chatbot rankings (open-source and proprietary models).
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| * https://huggingface.co/spaces/mteb/leaderboard Embedding Leaderboard – Benchmark of embedding models for RAG and semantic search.
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| * https://ann-benchmarks.com Vectors DB Ranking – Vector database comparison (latency, memory, features).
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| * https://top500.org/lists/green500/ HPC Efficiency – Ranking of the most energy-efficient supercomputers.
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| ; '''Development & fine-tuning tools'''
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| * https://github.com/search?q=stars%3A%3E15000+forks%3A%3E1500+created%3A%3E2022-06-01&type=repositories&s=updated&o=desc Project Trending – Major recent open-source projects, sorted by popularity and activity. | |
| * https://github.com/hiyouga/LLaMA-Factory LLM Fine Tuning – Advanced framework for LLM fine-tuning (instruction tuning, LoRA, etc.). | |
| * https://www.perplexity.ai Perplexity AI – Advanced research and synthesis oriented as a “research copilot”.
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| == AI Hardware & GPUs ==
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| ; '''GPUs & accelerators'''
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| * https://www.nvidia.com/en-us/data-center/h100/ NVIDIA H100 – Datacenter GPU for Kubernetes clusters and intensive AI workloads.
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| * NVIDIA 5080 – Consumer GPU for lower-cost private LLM deployments.
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| * https://www.mouser.fr/ProductDetail/BittWare/RS-GQ-GC1-0109?qs=ST9lo4GX8V2eGrFMeVQmFw%3D%3D GROQ LLM accelerator – Hardware accelerator dedicated to LLM inference.
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| ----
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| = Open models & internal endpoints =
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| ''(Last update: 2026-02-13)''
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| The models below correspond to '''logical endpoints''' (for example via a proxy or gateway), selected for specific use cases.
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| {| class="wikitable"
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| ! Endpoint !! Description / Primary use case
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| | '''ai-chat''' || Based on '''gpt-oss-20b''' – General-purpose chat, good cost / quality balance.
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| | '''ai-translate''' || gpt-oss-20b, temperature = 0 – Deterministic, reproducible translation (FR, EN, other languages).
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| | '''ai-summary''' || qwen3 – Model optimized for summarizing long texts (reports, documents, transcriptions).
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| | '''ai-code''' || gpt-oss-20b – Code reasoning, explanation, and refactoring.
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| | '''ai-code-completion''' || gpt-oss-20b – Fast code completion, designed for IDE auto-completion.
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| | '''ai-parse''' || qwen3 – Structured extraction, log / JSON / table parsing.
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| | '''ai-RAG-FR''' || qwen3 – RAG usage in French (business knowledge, internal FAQs).
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| | '''gpt-oss-20b''' || Agentic tasks.
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| |}
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| Usage idea: each endpoint is associated with one or more labs (chat, summary, parsing, RAG, etc.) in the Cloud Lab section.
| | == Préparer un audit ou une migration == |
| | * Inventorier les environnements |
| | * Concevoir l’architecture cible |
| | * Automatiser les opérations avec des scripts reproductibles |
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| = News & Trends = | | = Sections principales = |
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| * https://www.youtube.com/@lev-selector/videos Top AI News – Curated AI news videos.
| | == IA & APIs == |
| * https://betterprogramming.pub/color-your-captions-streamlining-live-transcriptions-with-diart-and-openais-whisper-6203350234ef Real-time transcription with Diart + Whisper – Example of real-time transcription with speaker detection.
| | Services autour des assistants, modèles, OCR, transcription, synthèse vocale, résumé, embeddings et bases vecteur. |
| * https://github.com/openai-translator/openai-translator OpenAI Translator – Modern extension / client for LLM-assisted translation.
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| * https://opensearch.org/docs/latest/search-plugins/conversational-search Opensearch with LLM – Conversational search based on LLMs and OpenSearch.
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| ----
| | == Cloud & Infrastructure == |
| | Kubernetes, haute disponibilité, architecture web, HPC, DevSecOps et bonnes pratiques d’exploitation. |
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| = Training & Learning = | | == Labs & Automatisation == |
| | Scénarios techniques, scripts réutilisables, audits, migrations et démonstrateurs. |
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| * https://www.youtube.com/watch?v=4Bdc55j80l8 Transformers Explained – Introduction to Transformers, the core architecture of LLMs.
| | == Comparatifs & Références == |
| * Hands-on labs, scripts, and real-world feedback in the [[LAB project|CLOUD LAB]] project below.
| | Tableaux de comparaison, benchmarks, liens utiles et veille technique. |
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| = Cloud Lab & Audit Projects = | | = Services mis en avant = |
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| [[File:Infocepo.drawio.png|400px|Cloud Lab reference diagram]]
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| The '''Cloud Lab''' provides reproducible scenarios: infrastructure audits, cloud migration, automation, high availability.
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| == Audit project – Cloud Audit ==
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| ; '''[[ServerDiff.sh]]'''
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| Bash audit script to:
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| * detect configuration drift,
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| * compare multiple environments,
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| * prepare a migration or remediation plan.
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| == Example of Cloud migration ==
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| [[File:Diagram-migration-ORACLE-KVM-v2.drawio.png|400px|Cloud migration diagram]]
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| Example: migration of virtual environments to a modernized cloud, including audit, architecture design, and automation.
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| {| class="wikitable" | | {| class="wikitable" |
| ! Task !! Description !! Duration (days) | | ! Service !! Rôle |
| |- | | |- |
| | Infrastructure audit || 82 services, automated audit via '''ServerDiff.sh''' || 1.5 | | | [https://api.ai.lab.infocepo.com '''API LLM'''] || Chat, code, RAG, OCR |
| |- | | |- |
| | Cloud architecture diagram || Visual design and documentation || 1.5 | | | [https://stt.ai.lab.infocepo.com/docs '''API STT'''] || Transcription audio |
| |- | | |- |
| | Compliance checks || 2 clouds, 6 hypervisors, 6 TB of RAM || 1.5 | | | [https://tts.ai.lab.infocepo.com/docs '''API TTS'''] || Synthèse vocale |
| |- | | |- |
| | Cloud platform installation || Deployment of main target environments || 1.0 | | | [https://api-summary.ai.lab.infocepo.com/docs '''API Summary'''] || Résumé de textes |
| |- | | |- |
| | Stability verification || Early functional tests || 0.5 | | | [https://text-embeddings.ai.lab.infocepo.com/docs '''Text Embeddings'''] || Embeddings pour RAG |
| |- | | |- |
| | Automation study || Identification and automation of repetitive tasks || 1.5 | | | [https://chromadb.ai.lab.infocepo.com '''ChromaDB'''] || Base de données vecteur |
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| | Template development || 6 templates, 8 environments, 2 clouds / OS || 1.5 | | | [https://datalab.ai.lab.infocepo.com '''DataLab'''] || Environnement de travail et d’expérimentation |
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| | Migration diagram || Illustration of the migration process || 1.0
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| | Migration code writing || 138 lines (see '''MigrationApp.sh''') || 1.5
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| | Process stabilization || Validation that migration is reproducible || 1.5
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| | Cloud benchmarking || Performance comparison vs legacy infrastructure || 1.5
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| | Downtime tuning || Calculation of outage time per migration || 0.5
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| | VM loading || 82 VMs: OS, code, 2 IPs per VM || 0.1
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| ! colspan=2 align="right"| '''Total''' !! 15 person-days
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| |}
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| === Stability checks (minimal HA) ===
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| {| class="wikitable"
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| ! Action !! Expected result
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| | Shutdown of one node || All services must automatically restart on remaining nodes.
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| | Simultaneous shutdown / restart of all nodes || All services must recover correctly after reboot.
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| |} | | |} |
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| = Web Architecture & Best Practices = | | = Projets et usages = |
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| [[File:WebModelDiagram.drawio.png|400px|Reference web architecture]]
| | Ce wiki sert notamment à : |
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| Principles for designing scalable and portable web architectures:
| | * documenter des stacks IA privées, |
| | | * publier des schémas d’architecture, |
| * Favor '''simple, modular, and flexible''' infrastructure. | | * capitaliser des retours d’expérience, |
| * Follow client location (GDNS or equivalent) to bring content closer.
| | * préparer des déploiements reproductibles, |
| * Use network load balancers (LVS, IPVS) for scalability.
| | * industrialiser des workflows cloud et IA. |
| * Systematically compare costs and beware of '''vendor lock-in'''.
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| * TLS:
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| ** HAProxy for fast frontends,
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| ** Envoy for compatibility and advanced use cases (mTLS, HTTP/2/3).
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| * Caching: | |
| ** Varnish, Apache Traffic Server for large content volumes.
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| * Favor open-source stacks and database caches (e.g., Memcached). | |
| * Use message queues, buffers, and quotas to smooth traffic spikes. | |
| * For complete architectures:
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| ** https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Architecture | |
| ** https://github.com/systemdesign42/system-design System Design GitHub
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| = Comparison of major Cloud platforms = | | = Aller plus loin = |
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| {| class="wikitable"
| | * [[ServerDiff.sh|'''Audit avec ServerDiff.sh''']] |
| ! Feature !! Kubernetes !! OpenStack !! AWS !! Bare-metal !! HPC !! CRM !! oVirt
| | * [[MLFlow|'''MLFlow''']] |
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| | * [[CI-CD-GITHUB-K8S|'''CI/CD + GitHub + Kubernetes''']] |
| | '''Deployment tools''' || Helm, YAML, ArgoCD, Juju || Ansible, Terraform, Juju || CloudFormation, Terraform, Juju || Ansible, Shell || xCAT, Clush || Ansible, Shell || Ansible, Python | |
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| | '''Bootstrap method''' || API || API, PXE || API || PXE, IPMI || PXE, IPMI || PXE, IPMI || PXE, API
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| | '''Router control''' || Kube-router || Router/Subnet API || Route Table / Subnet API || Linux, OVS || xCAT || Linux || API
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| | '''Firewall control''' || Istio, NetworkPolicy || Security Groups API || Security Group API || Linux firewall || Linux firewall || Linux firewall || API
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| | '''Network virtualization''' || VLAN, VxLAN, others || VPC || VPC || OVS, Linux || xCAT || Linux || API | |
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| | '''DNS''' || CoreDNS || DNS-Nameserver || Route 53 || GDNS || xCAT || Linux || API
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| | '''Load Balancer''' || Kube-proxy, LVS || LVS || Network Load Balancer || LVS || SLURM || Ldirectord || N/A | |
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| | '''Storage options''' || Local, Cloud, PVC || Swift, Cinder, Nova || S3, EFS, EBS, FSx || Swift, XFS, EXT4, RAID10 || GPFS || SAN || NFS, SAN
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| |}
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| This table serves as a starting point for choosing the right stack based on:
| | Pour plus de détails, parcourir [[Special:AllPages|'''la liste complète des pages''']]. |
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| * Desired level of control (API vs bare-metal),
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| * Context (on-prem, public cloud, HPC, CRM…),
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| * Existing automation tooling.
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| = Useful Cloud & IT links = | | = Contributions = |
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| * https://cloud.google.com/free/docs/aws-azure-gcp-service-comparison Cloud Providers Compared – AWS / Azure / GCP service mapping.
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| * https://global-internet-map-2021.telegeography.com/ Global Internet Topology Map – Global Internet mapping.
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| * https://landscape.cncf.io/?fullscreen=yes CNCF Official Landscape – Overview of cloud-native projects (CNCF).
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| * https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Wiki – Wikimedia infrastructure, real large-scale example.
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| * https://openapm.io OpenAPM – SRE Tools – APM / observability tooling.
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| * https://access.redhat.com/downloads/content/package-browser RedHat Package Browser – Package and version search at Red Hat.
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| * https://www.silkhom.com/barometre-2021-des-tjm-dans-informatique-digital Barometer of IT freelance daily rates.
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| * https://www.glassdoor.fr/salaire/Hays-Salaires-E10166.htm IT Salaries (Glassdoor) – Salary indicators.
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| = Advanced: High Availability, HPC & DevSecOps =
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| == High Availability with Corosync & Pacemaker ==
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| [[File:HA-REF.drawio.png|400px|HA cluster architecture]]
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| Basic principles:
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| * Multi-node or multi-site clusters for redundancy.
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| * Use of IPMI for fencing, provisioning via PXE/NTP/DNS/TFTP.
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| * For a 2-node cluster:
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| – carefully sequence fencing to avoid split-brain,
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| – 3 or more nodes remain recommended for production.
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| === Common resource patterns ===
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| * Multipath storage, LUNs, LVM, NFS.
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| * User resources and application processes.
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| * Virtual IPs, DNS records, network listeners.
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| == HPC ==
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| [[File:HPC.drawio.png|400px|Overview of an HPC cluster]]
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| * Job orchestration (SLURM or equivalent).
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| * High-performance shared storage (GPFS, Lustre…).
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| * Possible integration with AI workloads (large-scale training, GPU inference).
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| == DevSecOps ==
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| [[File:DSO-POC-V3.drawio.png|400px|DevSecOps reference design]]
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| * CI/CD pipelines with built-in security checks (linting, SAST, DAST, SBOM).
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| * Observability (logs, metrics, traces) integrated from design time.
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| * Automated vulnerability scanning, secret management, policy-as-code.
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| ----
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| = About & Contributions =
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| For more examples, scripts, diagrams, and feedback, see:
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| * https://infocepo.com infocepo.com
| | Les suggestions, corrections, améliorations de schémas et nouveaux labs sont les bienvenus. |
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| Suggestions for corrections, diagram improvements, or new labs are welcome.
| | Ce wiki a vocation à rester un '''laboratoire vivant''' autour du cloud, de l’IA et de l’automatisation. |
| This wiki aims to remain a '''living laboratory''' for AI, cloud, and automation.
| |
