laivision 0.1.5

LAI: a platform to train and deploy AI vision models — datasets, annotation, training, and evaluation.

LAI

laivision.dk — project site, workflow overview, and tutorials.

Self-hosted computer vision studio: datasets, SAM-assisted annotation, training (YOLO / MMYOLO / RT-DETR), evaluation, and export.

Install a small CLI from PyPI, pull pre-built images from Docker Hub, and run everything with lai. No git clone required.

Tested on: Linux (Ubuntu) and Windows 10/11 (Docker Desktop + WSL2).

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Requirements

Requirement	Notes
OS	Linux or Windows 10/11 (see platform notes below)
Docker Engine + Compose v2.24+	docker compose version must work
Python 3.10–3.12	For the lai CLI only — not for running the app itself
RAM	8 GB minimum · 16 GB+ recommended (32 GB with GPU tier)
Disk	~5 GB (CPU stack) · ~20–30 GB (GPU images + models)
Browser	For lai install-gui and the studio UI
NVIDIA GPU (optional)	Training, auto-annotate, SAM — GPU tier + Container Toolkit (Linux) or WSL2 GPU passthrough (Windows)

You do not need Node.js, a git checkout, or local image builds for the quick start.

Linux

• Docker Engine + Compose plugin
• Install CLI with pipx or a venv (recommended on Debian/Ubuntu — do not use system pip; PEP 668)

Windows

• Docker Desktop with WSL2 backend
• lai install-gui works in any browser; terminal lai install needs Git Bash or WSL
• GPU tier: enable WSL2 integration in Docker Desktop and install NVIDIA drivers for WSL

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Quick start

flowchart LR
  A["① pip install laivision"] --> B["② lai install-gui"]
  B --> C["③ lai up"]
  C --> D["④ lai download-models"]
  D --> E["Open localhost:8089"]

① Install the CLI

pip install laivision
# recommended:  pipx install laivision

Installs the lai command and embeds Docker Compose files inside the package. Your settings live in ~/.config/lai/.env (not in site-packages), so upgrades do not overwrite them.

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② First-time setup

lai install-gui

Opens a browser wizard on http://127.0.0.1:… where you choose:

Setting	Default	Purpose
Data directory	~/lai-data	Databases, datasets, projects, model cache
Web port	8089	UI in your browser
GPU tier	off	Enables worker-gpu + sam_service (NVIDIA required)
SAM 3 folder	~/lai-data/sam3-models	Optional checkpoint path (SAM 2 works without it)

Terminal alternative: lai install or lai install --yes for non-interactive defaults.

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③ Start the stack

lai up

• Pulls images from Docker Hub (luluray/lai-*) if they are not local yet
• Starts database, API, workers, and web UI
• First run may take several minutes while images download

Then run step ④ (lai download-models) before training or auto-annotate. You can open http://localhost:8089 (or the port you chose) while weights download.

lai down          # stop containers
lai doctor        # version, Docker checks, bundle path
lai upgrade       # after pip install -U laivision

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④ Download foundation models (required — run after lai up)

lai download-models

Downloads the weights LAI needs for training, auto-annotate, and related workflows into your data directory ($LAI_DATA_DIR/models and ai_models/). The studio is not fully usable until this finishes — without these files, training and auto-annotate will fail or hang waiting for models.

Run it once after the stack is up (containers must be running). You can narrow what is fetched:

lai download-models --yolo yolov8n.pt      # single Ultralytics weight
lai download-models --mmyolo minimal         # MMYOLO pretrained checkpoints
lai download-models --depth minimal          # depth estimation ONNX

Use lai download-models --help for the full matrix. Re-run anytime to add more weights.

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Optional extras

SAM 3 — SAM 2 is included. For SAM 3, place a checkpoint (e.g. from Hugging Face) at the path from the wizard, then:

lai restart sam_service

GPU check (if GPU tier is enabled):

docker compose exec worker-gpu nvidia-smi

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Where things live

Path	Contents
~/.config/lai/.env	Ports, data dir, Docker image tags
~/lai-data/ (default)	Postgres/Redis/Mongo data, projects, models
PyPI package lai/bundle/	Read-only compose files (do not edit)

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Advanced setup

Git checkout, building images locally, running tests, maintainer releases → see README_advanced.md.

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License

AGPL-3.0 — bundled ML runtimes (YOLO, MMYOLO, SAM) have additional upstream licenses. Details in README_advanced.md#license.