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[[File:Infocepo-picture.png|thumb|right|Discover cloud computing on infocepo.com]]
[[File:Infocepo-picture.png|thumb|right|Discover cloud and AI on infocepo.com]]


= Discover Cloud Computing on infocepo.com =
= infocepo.com – Cloud, AI & Labs =


Welcome! This portal is designed for IT professionals, engineers, students, and enthusiasts who want to master cloud infrastructure, explore AI tools, and accelerate their IT skills through hands-on labs and open-source solutions.
Welcome to the '''infocepo.com''' portal.
 
This wiki is intended for system administrators, cloud engineers, developers, students, and enthusiasts who want to:
 
* Understand modern architectures (Kubernetes, OpenStack, bare-metal, HPC…)
* Deploy private AI assistants and productivity tools
* Build hands-on labs to learn by doing
* Prepare large-scale audits, migrations, and automations
 
The goal: turn theory into '''reusable scripts, diagrams, and architectures'''.


__TOC__
__TOC__


----
= Getting started quickly =
== Recommended paths ==


== Quick Start ==
; 1. Build a private AI assistant
* '''Master cloud infrastructure:''' Practical guides and labs
* Deploy a typical stack: '''Open WebUI + Ollama + GPU''' (H100 or consumer-grade GPU)
* '''Explore artificial intelligence:''' Trends and hands-on tools
* Add a chat model and a summarization model
* '''Compare cloud providers:''' Kubernetes, AWS, OpenStack, and more
* Integrate internal data (RAG, embeddings)
* '''Develop expertise:''' Training, open-source, and real-world projects
 
; 2. Launch a Cloud lab
* Create a small cluster (Kubernetes, OpenStack, or bare-metal)
* Set up a deployment pipeline (Helm, Ansible, Terraform…)
* Add an AI service (transcription, summarization, chatbot…)
 
; 3. Prepare an audit / migration
* Inventory servers with '''ServerDiff.sh'''
* Design the target architecture (cloud diagrams)
* Automate the migration with reproducible scripts
 
== Content overview ==
 
* '''AI guides & tools''' : assistants, models, evaluations, GPUs
* '''Cloud & infrastructure''' : HA, HPC, web-scale, DevSecOps
* '''Labs & scripts''' : audit, migration, automation
* '''Comparison tables''' : Kubernetes vs OpenStack vs AWS vs bare-metal, etc.


----
----


= AI & Cloud Tools =
= future =
[[File:Automation-full-vs-humans.png|thumb|right|The world after automation]]


; '''AI Assistants'''
= AI Assistants & Cloud Tools =
* [https://chat.openai.com ChatGPT] – Public conversational AI with strong learning capabilities
* [https://github.com/open-webui/open-webui Open WebUI] + [https://www.scaleway.com/en/h100-pcie-try-it-now/ GPU H100] + [https://ollama.com Ollama] – Private assistants and self-hosted LLM APIs
* [https://github.com/ynotopec/summarize Private summary] – Fast, offline summarizer for your data


; '''Development & Model Tracking'''
== AI Assistants ==
* [https://ollama.com/library LLM Trending] – Latest open-source LLMs
 
* [https://github.com/search?q=stars%3A%3E15000+forks%3A%3E1500+created%3A%3E2022-06-01&type=repositories&s=updated&o=desc Project Trending] – Top trending codebases since 2022
; '''ChatGPT'''
* [https://lmarena.ai/leaderboard ChatBot Evaluation] – Compare chatbot performance
* https://chatgpt.com ChatGPT – Public conversational assistant, suited for exploration, writing, and rapid experimentation.
* [https://www.perplexity.ai Perplexity AI] – Cutting-edge research and question answering
 
* [https://huggingface.co/models Models Trending] – Model marketplace
; '''Self-hosted AI assistants'''
* [https://github.com/hiyouga/LLaMA-Factory LLM Fine Tuning] – Advanced training framework
* 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 
* [https://huggingface.co/spaces/mteb/leaderboard Embedding Leaderboard] – Ranking for vector search models
: Typical stack for private assistants, self-hosted LLMs, and OpenAI-compatible APIs.
* [https://ann-benchmarks.com Vectors DB Ranking] – Database speed and feature comparison
* https://github.com/ynotopec/summarize Private summary – Local, fast, offline summarizer for your own data.
* [https://www.nvidia.com/en-us/data-center/h100/ NVIDIA H100] – HPC/AI GPUs for Kubernetes clusters
 
* NVIDIA 5080 – Consumer GPU for private deployments
== Development, models & tracking ==
* [https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending Img2txt Trending] – Vision-language models
 
* [https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena Txt2img Evaluation] – Compare generative image models
; '''Discovering and tracking models'''
* [https://top500.org/lists/green500/ HPC Efficiency] – Top green supercomputers
* https://ollama.com/library LLM Trending – Model library (chat, code, RAG…) for local deployment.
* https://huggingface.co/models Models Trending – Model marketplace, filterable by task, size, and license.
* https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending Img2txt Trending – Vision-language models (image → text).
* https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena Txt2img Evaluation – Image generation model comparisons.
 
; '''Evaluation & benchmarks'''
* https://lmarena.ai/leaderboard ChatBot Evaluation – Chatbot rankings (open-source and proprietary models).
* https://huggingface.co/spaces/mteb/leaderboard Embedding Leaderboard – Benchmark of embedding models for RAG and semantic search.
* https://ann-benchmarks.com Vectors DB Ranking – Vector database comparison (latency, memory, features).
* https://top500.org/lists/green500/ HPC Efficiency – Ranking of the most energy-efficient supercomputers.
 
; '''Development & fine-tuning tools'''
* 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”.
 
== AI Hardware & GPUs ==
 
; '''GPUs & accelerators'''
* https://www.nvidia.com/en-us/data-center/h100/ NVIDIA H100 – Datacenter GPU for Kubernetes clusters and intensive AI workloads.
* NVIDIA 5080 – Consumer GPU for lower-cost private LLM deployments.
* https://www.mouser.fr/ProductDetail/BittWare/RS-GQ-GC1-0109?qs=ST9lo4GX8V2eGrFMeVQmFw%3D%3D GROQ LLM accelerator – Hardware accelerator dedicated to LLM inference.


----
----


== Notable Open LLMs ==
= Open models & internal endpoints =
''(Last updated: 26/07/2025)''
 
''(Last update: 2026-02-13)''
 
The models below correspond to '''logical endpoints''' (for example via a proxy or gateway), selected for specific use cases.


{| class="wikitable"
{| class="wikitable"
! Model !! Description / Notable Features
! Endpoint !! Description / Primary use case
|-
|-
| '''ai-chat''' || gemma3n, cost efficient
| '''ai-chat''' || Based on '''gpt-oss-20b''' – General-purpose chat, good cost / quality balance.
|-
|-
| '''ai-translate''' || gemma3n, temperature=0 (deterministic translation)
| '''ai-translate''' || gpt-oss-20b, temperature = 0 – Deterministic, reproducible translation (FR, EN, other languages).
|-
|-
| '''ai-summary''' || qwen2.5, optimized for summarization
| '''ai-summary''' || qwen3 – Model optimized for summarizing long texts (reports, documents, transcriptions).
|-
|-
| '''ai-code''' || mistral-small, advanced code reasoning
| '''ai-code''' || gpt-oss-20b – Code reasoning, explanation, and refactoring.
|-
|-
| '''ai-code-completion''' || granite-2b, fast code suggestions
| '''ai-code-completion''' || gpt-oss-20b – Fast code completion, designed for IDE auto-completion.
|-
|-
| '''ai-parse''' || gemma2-simpo, parsing & extraction
| '''ai-parse''' || qwen3 – Structured extraction, log / JSON / table parsing.
|-
|-
| '''ai-RAG-FR''' || qwen2.5, French RAG applications
| '''ai-RAG-FR''' || qwen3 – RAG usage in French (business knowledge, internal FAQs).
|-
|-
| '''mannix/gemma2-9b-simpo''' || OllamaFunctions integration
| '''gpt-oss-20b''' || Agentic tasks.
|}
|}
Usage idea: each endpoint is associated with one or more labs (chat, summary, parsing, RAG, etc.) in the Cloud Lab section.


----
----


= Industry News & Trends =
= News & Trends =


* [https://www.youtube.com/@lev-selector/videos Top AI News] – Video digest
* https://www.youtube.com/@lev-selector/videos Top AI News – Curated AI news videos.
* [https://betterprogramming.pub/color-your-captions-streamlining-live-transcriptions-with-diart-and-openais-whisper-6203350234ef Real-time transcription with Diart + Whisper] – Speaker tracking
* 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.
* [https://github.com/openai-translator/openai-translator OpenAI Translator] – Modern open-source translation
* https://github.com/openai-translator/openai-translator OpenAI Translator – Modern extension / client for LLM-assisted translation.
* [https://www.mouser.fr/ProductDetail/BittWare/RS-GQ-GC1-0109?qs=ST9lo4GX8V2eGrFMeVQmFw%3D%3D GROQ LLM accelerator] – Fast, low-cost inference hardware
* https://opensearch.org/docs/latest/search-plugins/conversational-search Opensearch with LLM – Conversational search based on LLMs and OpenSearch.
* [https://opensearch.org/docs/latest/search-plugins/conversational-search Opensearch with LLM] – Enhanced search experiences


----
----
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= Training & Learning =
= Training & Learning =


* [https://www.youtube.com/watch?v=4Bdc55j80l8 Transformers Explained] – Intro to Transformers algorithm
* https://www.youtube.com/watch?v=4Bdc55j80l8 Transformers Explained – Introduction to Transformers, the core architecture of LLMs.
* Hands-on labs and scripts in the [[LAB project|CLOUD LAB]] below
* Hands-on labs, scripts, and real-world feedback in the [[LAB project|CLOUD LAB]] project below.


----
----
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= Cloud Lab & Audit Projects =
= Cloud Lab & Audit Projects =


[[File:Infocepo.drawio.png|400px|Cloud Lab Reference Diagram]]
[[File:Infocepo.drawio.png|400px|Cloud Lab reference diagram]]
 
The '''Cloud Lab''' provides reproducible scenarios: infrastructure audits, cloud migration, automation, high availability.
 
== Audit project – Cloud Audit ==
 
; '''[[ServerDiff.sh]]'''
Bash audit script to:
 
* detect configuration drift,
* compare multiple environments,
* prepare a migration or remediation plan.


; '''Lab Project''' 
== Example of Cloud migration ==
Experiment with high-availability, cloud migration, and audit automation.


=== Cloud Audit ===
[[File:Diagram-migration-ORACLE-KVM-v2.drawio.png|400px|Cloud migration diagram]]
* '''[[ServerDiff.sh]]''' – Bash script for auditing servers, tracking config drift, and checking environment consistency


=== Cloud Migration Example ===
Example: migration of virtual environments to a modernized cloud, including audit, architecture design, and automation.
[[File:Diagram-migration-ORACLE-KVM-v2.drawio.png|400px|Cloud Migration Diagram]]


{| class="wikitable"
{| class="wikitable"
! Task !! Description !! Duration (days)
! Task !! Description !! Duration (days)
|-
|-
| Audit infrastructure || 82 services, automated via ServerDiff.sh || 1.5
| Infrastructure audit || 82 services, automated audit via '''ServerDiff.sh''' || 1.5
|-
|-
| Diagram cloud architecture || Visual design || 1.5
| Cloud architecture diagram || Visual design and documentation || 1.5
|-
|-
| Compliance check || 2 clouds, 6 hypervisors, 6TB RAM || 1.5
| Compliance checks || 2 clouds, 6 hypervisors, 6 TB of RAM || 1.5
|-
|-
| Install cloud platforms || Deploy core cloud environments || 1.0
| Cloud platform installation || Deployment of main target environments || 1.0
|-
|-
| Stability check || Early operations || 0.5
| Stability verification || Early functional tests || 0.5
|-
|-
| Automation study || Automate deployment/tasks || 1.5
| Automation study || Identification and automation of repetitive tasks || 1.5
|-
|-
| Develop templates || 6 templates, 8 envs, 2 clouds/OS || 1.5
| Template development || 6 templates, 8 environments, 2 clouds / OS || 1.5
|-
|-
| Migration diagram || Process illustration || 1.0
| Migration diagram || Illustration of the migration process || 1.0
|-
|-
| Write migration code || 138 lines (see MigrationApp.sh) || 1.5
| Migration code writing || 138 lines (see '''MigrationApp.sh''') || 1.5
|-
|-
| Process stabilization || Ensure repeatable migration || 1.5
| Process stabilization || Validation that migration is reproducible || 1.5
|-
|-
| Cloud benchmarking || Performance test vs legacy || 1.5
| Cloud benchmarking || Performance comparison vs legacy infrastructure || 1.5
|-
|-
| Downtime calibration || Per-migration time calculation || 0.5
| Downtime tuning || Calculation of outage time per migration || 0.5
|-
|-
| VM loading || 82 VMs: OS, code, 2 IPs each || 0.1
| VM loading || 82 VMs: OS, code, 2 IPs per VM || 0.1
|-
|-
! colspan=2 align="right"| '''Total''' !! 15 man-days
! colspan=2 align="right"| '''Total''' !! 15 person-days
|}
|}


==== Stability check ====
=== Stability checks (minimal HA) ===


{| class="wikitable"
{| class="wikitable"
! Action !! Expected Result
! Action !! Expected result
|-
|-
| Power off one node || All resources started
| Shutdown of one node || All services must automatically restart on remaining nodes.
|-
|-
| Power off/on all nodes simultaneously || All resources started
| Simultaneous shutdown / restart of all nodes || All services must recover correctly after reboot.
|}
|}


----
----


= Web Infrastructure & Best Practices =
= Web Architecture & Best Practices =


[[File:WebModelDiagram.drawio.png|400px|Web Architecture Reference]]
[[File:WebModelDiagram.drawio.png|400px|Reference web architecture]]


* Favor minimal, flexible infrastructure
Principles for designing scalable and portable web architectures:
* Track customer location via GDNS or similar
 
* Use network load balancers (LVS, IPVS) for scaling
* Favor '''simple, modular, and flexible''' infrastructure.
* Compare prices and beware of vendor lock-in
* Follow client location (GDNS or equivalent) to bring content closer.
* For TLS: use HAProxy for fast frontend, Envoy for compatibility
* Use network load balancers (LVS, IPVS) for scalability.
* Caching: Varnish, Apache Traffic Server for large content
* Systematically compare costs and beware of '''vendor lock-in'''.
* Prefer open-source stacks and database caches (e.g. Memcached)
* TLS:
* Use message queues and buffers for workload smoothing
** HAProxy for fast frontends,
* For more examples: [https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Architecture], [https://github.com/systemdesign42/system-design System Design GitHub]
** Envoy for compatibility and advanced use cases (mTLS, HTTP/2/3).
* Caching:
** Varnish, Apache Traffic Server for large content volumes.
* Favor open-source stacks and database caches (e.g., Memcached).
* Use message queues, buffers, and quotas to smooth traffic spikes.
* For complete architectures:
** https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Architecture
** https://github.com/systemdesign42/system-design System Design GitHub


----
----


= Major Cloud Platforms: Feature Comparison =
= Comparison of major Cloud platforms =


{| class="wikitable"
{| class="wikitable"
! Function !! Kubernetes !! OpenStack !! AWS !! Bare-metal !! HPC !! CRM !! oVirt
! Feature !! Kubernetes !! OpenStack !! AWS !! Bare-metal !! HPC !! CRM !! oVirt
|-
|-
| '''Deployment Tools''' || Helm, YAML, ArgoCD, Juju || Ansible, Terraform, Juju || CloudFormation, Terraform, Juju || Ansible, Shell || xCAT, Clush || Ansible, Shell || Ansible, Python
| '''Deployment tools''' || Helm, YAML, ArgoCD, Juju || Ansible, Terraform, Juju || CloudFormation, Terraform, Juju || Ansible, Shell || xCAT, Clush || Ansible, Shell || Ansible, Python
|-
|-
| '''Bootstrap Method''' || API || API, PXE || API || PXE, IPMI || PXE, IPMI || PXE, IPMI || PXE, API
| '''Bootstrap method''' || API || API, PXE || API || PXE, IPMI || PXE, IPMI || PXE, IPMI || PXE, API
|-
|-
| '''Router Control''' || Kube-router || Router/Subnet API || Route Table/Subnet API || Linux, OVS || xCAT || Linux || API
| '''Router control''' || Kube-router || Router/Subnet API || Route Table / Subnet API || Linux, OVS || xCAT || Linux || API
|-
|-
| '''Firewall Control''' || Istio, NetworkPolicy || Security Groups API || Security Group API || Linux Firewall || Linux Firewall || Linux Firewall || API
| '''Firewall control''' || Istio, NetworkPolicy || Security Groups API || Security Group API || Linux firewall || Linux firewall || Linux firewall || API
|-
|-
| '''Network Virtualization''' || VLAN, VxLAN, others || VPC || VPC || OVS, Linux || xCAT || Linux || API
| '''Network virtualization''' || VLAN, VxLAN, others || VPC || VPC || OVS, Linux || xCAT || Linux || API
|-
|-
| '''DNS''' || CoreDNS || DNS-Nameserver || Route 53 || GDNS || xCAT || Linux || API
| '''DNS''' || CoreDNS || DNS-Nameserver || Route 53 || GDNS || xCAT || Linux || API
Line 174: Line 242:
| '''Load Balancer''' || Kube-proxy, LVS || LVS || Network Load Balancer || LVS || SLURM || Ldirectord || N/A
| '''Load Balancer''' || Kube-proxy, LVS || LVS || Network Load Balancer || LVS || SLURM || Ldirectord || N/A
|-
|-
| '''Storage Options''' || Local, Cloud, PVC || Swift, Cinder, Nova || S3, EFS, EBS, FSx || Swift, XFS, EXT4, RAID10 || GPFS || SAN || NFS, SAN
| '''Storage options''' || Local, Cloud, PVC || Swift, Cinder, Nova || S3, EFS, EBS, FSx || Swift, XFS, EXT4, RAID10 || GPFS || SAN || NFS, SAN
|}
|}
This table serves as a starting point for choosing the right stack based on:
* Desired level of control (API vs bare-metal),
* Context (on-prem, public cloud, HPC, CRM…),
* Existing automation tooling.


----
----


= Useful Cloud & IT Links =
= Useful Cloud & IT links =


* [https://cloud.google.com/free/docs/aws-azure-gcp-service-comparison Cloud Providers Compared]
* https://cloud.google.com/free/docs/aws-azure-gcp-service-comparison Cloud Providers Compared – AWS / Azure / GCP service mapping.
* [https://global-internet-map-2021.telegeography.com/ Global Internet Topology Map]
* https://global-internet-map-2021.telegeography.com/ Global Internet Topology Map – Global Internet mapping.
* [https://landscape.cncf.io/?fullscreen=yes CNCF Official Landscape]
* https://landscape.cncf.io/?fullscreen=yes CNCF Official Landscape – Overview of cloud-native projects (CNCF).
* [https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Wiki]
* https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Wiki – Wikimedia infrastructure, real large-scale example.
* [https://openapm.io OpenAPM – SRE Tools]
* https://openapm.io OpenAPM – SRE Tools – APM / observability tooling.
* [https://access.redhat.com/downloads/content/package-browser RedHat Package Browser]
* https://access.redhat.com/downloads/content/package-browser RedHat Package Browser – Package and version search at Red Hat.
* [https://www.silkhom.com/barometre-2021-des-tjm-dans-informatique-digital Freelance IT Rates]
* https://www.silkhom.com/barometre-2021-des-tjm-dans-informatique-digital Barometer of IT freelance daily rates.
* [https://www.glassdoor.fr/salaire/Hays-Salaires-E10166.htm IT Salaries (Glassdoor)]
* https://www.glassdoor.fr/salaire/Hays-Salaires-E10166.htm IT Salaries (Glassdoor) – Salary indicators.


----
----


= Advanced: High-Availability, HPC & DevSecOps =
= Advanced: High Availability, HPC & DevSecOps =


== High Availability with Corosync & Pacemaker ==
== High Availability with Corosync & Pacemaker ==
[[File:HA-REF.drawio.png|400px|HA Cluster Architecture]]


* Multi-node or dual-room clusters for redundancy
[[File:HA-REF.drawio.png|400px|HA cluster architecture]]
* Use IPMI for fencing, provision via PXE/NTP/DNS/TFTP
* For 2-node clusters: stagger fencing for stability; 3+ nodes recommended


=== Common Resources Pattern ===
Basic principles:
* Multipath storage, LUN, LVM, NFS
 
* User and process resources
* Multi-node or multi-site clusters for redundancy.
* IP, DNS, Listener management
* Use of IPMI for fencing, provisioning via PXE/NTP/DNS/TFTP.
* For a 2-node cluster:
  – carefully sequence fencing to avoid split-brain,
  – 3 or more nodes remain recommended for production.
 
=== Common resource patterns ===
 
* Multipath storage, LUNs, LVM, NFS.
* User resources and application processes.
* Virtual IPs, DNS records, network listeners.


== HPC ==
== HPC ==
[[File:HPC.drawio.png|400px|HPC Cluster Overview]]
 
[[File:HPC.drawio.png|400px|Overview of an HPC cluster]]
 
* Job orchestration (SLURM or equivalent).
* High-performance shared storage (GPFS, Lustre…).
* Possible integration with AI workloads (large-scale training, GPU inference).


== DevSecOps ==
== DevSecOps ==
[[File:DSO-POC-V3.drawio.png|400px|DevSecOps Reference Design]]
 
[[File:DSO-POC-V3.drawio.png|400px|DevSecOps reference design]]
 
* CI/CD pipelines with built-in security checks (linting, SAST, DAST, SBOM).
* Observability (logs, metrics, traces) integrated from design time.
* Automated vulnerability scanning, secret management, policy-as-code.


----
----


'''For more examples, guides, and scripts, visit [https://infocepo.com infocepo.com]. Contributions and suggestions welcome!'''
= About & Contributions =
 
For more examples, scripts, diagrams, and feedback, see:
 
* https://infocepo.com infocepo.com
 
Suggestions for corrections, diagram improvements, or new labs are welcome.
This wiki aims to remain a '''living laboratory''' for AI, cloud, and automation.

Latest revision as of 01:24, 13 February 2026

Discover cloud and AI on infocepo.com

infocepo.com – Cloud, AI & Labs

Welcome to the infocepo.com portal.

This wiki is intended for system administrators, cloud engineers, developers, students, and enthusiasts who want to:

  • Understand modern architectures (Kubernetes, OpenStack, bare-metal, HPC…)
  • Deploy private AI assistants and productivity tools
  • Build hands-on labs to learn by doing
  • Prepare large-scale audits, migrations, and automations

The goal: turn theory into reusable scripts, diagrams, and architectures.

Getting started quickly

Recommended paths

1. Build a private AI assistant
  • Deploy a typical stack: Open WebUI + Ollama + GPU (H100 or consumer-grade GPU)
  • Add a chat model and a summarization model
  • Integrate internal data (RAG, embeddings)
2. Launch a Cloud lab
  • Create a small cluster (Kubernetes, OpenStack, or bare-metal)
  • Set up a deployment pipeline (Helm, Ansible, Terraform…)
  • Add an AI service (transcription, summarization, chatbot…)
3. Prepare an audit / migration
  • Inventory servers with ServerDiff.sh
  • Design the target architecture (cloud diagrams)
  • Automate the migration with reproducible scripts

Content overview

  • AI guides & tools : assistants, models, evaluations, GPUs
  • Cloud & infrastructure : HA, HPC, web-scale, DevSecOps
  • Labs & scripts : audit, migration, automation
  • Comparison tables : Kubernetes vs OpenStack vs AWS vs bare-metal, etc.

future

The world after automation

AI Assistants & Cloud Tools

AI Assistants

ChatGPT
  • https://chatgpt.com ChatGPT – Public conversational assistant, suited for exploration, writing, and rapid experimentation.
Self-hosted AI assistants
Typical stack for private assistants, self-hosted LLMs, and OpenAI-compatible APIs.

Development, models & tracking

Discovering and tracking models
Evaluation & benchmarks
Development & fine-tuning tools

AI Hardware & GPUs

GPUs & accelerators

Open models & internal endpoints

(Last update: 2026-02-13)

The models below correspond to logical endpoints (for example via a proxy or gateway), selected for specific use cases.

Endpoint Description / Primary use case
ai-chat Based on gpt-oss-20b – General-purpose chat, good cost / quality balance.
ai-translate gpt-oss-20b, temperature = 0 – Deterministic, reproducible translation (FR, EN, other languages).
ai-summary qwen3 – Model optimized for summarizing long texts (reports, documents, transcriptions).
ai-code gpt-oss-20b – Code reasoning, explanation, and refactoring.
ai-code-completion gpt-oss-20b – Fast code completion, designed for IDE auto-completion.
ai-parse qwen3 – Structured extraction, log / JSON / table parsing.
ai-RAG-FR qwen3 – RAG usage in French (business knowledge, internal FAQs).
gpt-oss-20b Agentic tasks.

Usage idea: each endpoint is associated with one or more labs (chat, summary, parsing, RAG, etc.) in the Cloud Lab section.

News & Trends

Training & Learning

Cloud Lab & Audit Projects

Cloud Lab reference diagram

The Cloud Lab provides reproducible scenarios: infrastructure audits, cloud migration, automation, high availability.

Audit project – Cloud Audit

ServerDiff.sh

Bash audit script to:

  • detect configuration drift,
  • compare multiple environments,
  • prepare a migration or remediation plan.

Example of Cloud migration

Cloud migration diagram

Example: migration of virtual environments to a modernized cloud, including audit, architecture design, and automation.

Task Description Duration (days)
Infrastructure audit 82 services, automated audit via ServerDiff.sh 1.5
Cloud architecture diagram Visual design and documentation 1.5
Compliance checks 2 clouds, 6 hypervisors, 6 TB of RAM 1.5
Cloud platform installation Deployment of main target environments 1.0
Stability verification Early functional tests 0.5
Automation study Identification and automation of repetitive tasks 1.5
Template development 6 templates, 8 environments, 2 clouds / OS 1.5
Migration diagram Illustration of the migration process 1.0
Migration code writing 138 lines (see MigrationApp.sh) 1.5
Process stabilization Validation that migration is reproducible 1.5
Cloud benchmarking Performance comparison vs legacy infrastructure 1.5
Downtime tuning Calculation of outage time per migration 0.5
VM loading 82 VMs: OS, code, 2 IPs per VM 0.1
Total 15 person-days

Stability checks (minimal HA)

Action Expected result
Shutdown of one node All services must automatically restart on remaining nodes.
Simultaneous shutdown / restart of all nodes All services must recover correctly after reboot.

Web Architecture & Best Practices

Reference web architecture

Principles for designing scalable and portable web architectures:

  • Favor simple, modular, and flexible infrastructure.
  • Follow client location (GDNS or equivalent) to bring content closer.
  • Use network load balancers (LVS, IPVS) for scalability.
  • Systematically compare costs and beware of vendor lock-in.
  • TLS:
    • HAProxy for fast frontends,
    • Envoy for compatibility and advanced use cases (mTLS, HTTP/2/3).
  • Caching:
    • Varnish, Apache Traffic Server for large content volumes.
  • Favor open-source stacks and database caches (e.g., Memcached).
  • Use message queues, buffers, and quotas to smooth traffic spikes.
  • For complete architectures:

Comparison of major Cloud platforms

Feature Kubernetes OpenStack AWS Bare-metal HPC CRM oVirt
Deployment tools Helm, YAML, ArgoCD, Juju Ansible, Terraform, Juju CloudFormation, Terraform, Juju Ansible, Shell xCAT, Clush Ansible, Shell Ansible, Python
Bootstrap method API API, PXE API PXE, IPMI PXE, IPMI PXE, IPMI PXE, API
Router control Kube-router Router/Subnet API Route Table / Subnet API Linux, OVS xCAT Linux API
Firewall control Istio, NetworkPolicy Security Groups API Security Group API Linux firewall Linux firewall Linux firewall API
Network virtualization VLAN, VxLAN, others 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
Storage options Local, Cloud, PVC Swift, Cinder, Nova S3, EFS, EBS, FSx Swift, XFS, EXT4, RAID10 GPFS SAN NFS, SAN

This table serves as a starting point for choosing the right stack based on:

  • Desired level of control (API vs bare-metal),
  • Context (on-prem, public cloud, HPC, CRM…),
  • Existing automation tooling.

Useful Cloud & IT links

Advanced: High Availability, HPC & DevSecOps

High Availability with Corosync & Pacemaker

HA cluster architecture

Basic principles:

  • Multi-node or multi-site clusters for redundancy.
  • Use of IPMI for fencing, provisioning via PXE/NTP/DNS/TFTP.
  • For a 2-node cluster:
 – carefully sequence fencing to avoid split-brain,
 – 3 or more nodes remain recommended for production.

Common resource patterns

  • Multipath storage, LUNs, LVM, NFS.
  • User resources and application processes.
  • Virtual IPs, DNS records, network listeners.

HPC

Overview of an HPC cluster

  • Job orchestration (SLURM or equivalent).
  • High-performance shared storage (GPFS, Lustre…).
  • Possible integration with AI workloads (large-scale training, GPU inference).

DevSecOps

DevSecOps reference design

  • CI/CD pipelines with built-in security checks (linting, SAST, DAST, SBOM).
  • Observability (logs, metrics, traces) integrated from design time.
  • Automated vulnerability scanning, secret management, policy-as-code.

About & Contributions

For more examples, scripts, diagrams, and feedback, see:

Suggestions for corrections, diagram improvements, or new labs are welcome. This wiki aims to remain a living laboratory for AI, cloud, and automation.

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