GraphCanon updated 3w · GitHub synced 3w
Decision brief
alice is an AI-assisted tool for handling YOLO object detection datasets using Docker with auto-detection of hardware resources.
Good fit when
- When you need a toolkit that seamlessly integrates with Docker for dataset management in conjunction with YOLO models.
- For use cases requiring automatic hardware resource detection and optimized installation of dependencies like PyTorch, CUDA or CPU variants.
Avoid when
- Do not use if your project does not require integration with the YOLO model for object detection tasks.
- Avoid if you prefer tools that handle NVIDIA drivers and CUDA toolkit installations automatically without requiring manual pre-installation by users.
Observed Jul 14, 2026 · Source: enrich:decision_facts
Verify the decision
Adoption
Package downloads where a registry match exists. GitHub stars (370) are secondary evidence.
- npm downloads (30d)
- 106·npm downloads API·3w
Maintenance and security
Full trust report- Maintenance
- Slowing (96d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
npm install aliceSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A JavaScript-based toolkit for managing datasets with functionalities including analysis, learning, ingestion, curation, and export tailored for object detection tasks using YOLO models. Supports Docker containerization with auto-detection of hardware resources.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 1, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 1, 2026
- Languages
- javascript
Source: github.language · Aug 1, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 1, 2026)
python3 builder.py --no-venvSource link
Tags
README
Docker
python3 builder.py --no-venv
The builder generates docker-compose.yml automatically, depending on your configured hardware (GPU or CPU).
Edit the volume paths to match your Frigate setup, then:
docker compose up --build -d
See Docker Setup below for details.
Requirements
Python 3.8+ required. The builder creates a .venv and installs the base dependency (Pillow) automatically. All other dependencies are managed by ALICE and can be installed from the welcome page or Settings → System with one click:
| Package | Purpose |
|---|---|
| Pillow | Image processing, pHash, format conversion (auto-installed by builder) |
| NumPy | Numerical operations for dedup |
| inotify | Filesystem watching on Linux (falls back to polling) |
| opencv-python-headless | Video frame extraction |
| ONNX | ONNX model format for export |
| onnxslim | ONNX model optimization |
| onnxruntime | ONNX Runtime — GPU or CPU variant auto-selected |
| PyTorch | Deep learning framework — CUDA or CPU variant auto-selected |
| ultralytics | YOLO model training & inference |
ALICE installs the correct PyTorch variant (CUDA or CPU) based on detected hardware. No manual torch installation needed.
Note: ALICE does not install NVIDIA drivers or CUDA. If you want GPU-accelerated training, install the NVIDIA drivers and CUDA toolkit on your system before running ALICE.
Docker Setup
The builder generates docker-compose.yml with GPU support auto-detected from your host:
python3 builder.py --no-venv
This creates docker-compose.yml with the NVIDIA GPU block included if a GPU is detected, or CPU-only otherwise.
Edit the volume paths to match your setup:
volumes:
- ./alice.conf:/app/alice.conf
- /path/to/datasets:/app/datasets
- /path/to/models:/app/models
- /path/to/frigate/clips:/app/clips:ro
- /path/to/frigate/exports:/app/exports:ro
- /path/to/frigate/frigate.db:/app/frigate.db:ro
Important: The
frigate.dbvolume must point to the actual file, not a directory. If the file doesn't exist on the host at the time of container creation, Docker will create a directory instead and ALICE won't be able to open the database.
Then start:
docker compose up --build -d
On first run, open ALICE in your browser and install dependencies from the welcome page or Settings → System. Dependencies are persisted in a Docker volume across container restarts.
Note: GPU support in Docker requires NVIDIA Container Toolkit on the host. If running the builder on a machine without GPU, the generated compose file will be CPU-only.
License
PolyForm Noncommercial 1.0.0 — free for personal, non-commercial use. For commercial licensing, contact alice@it-link.net.
For agents
This page has a .md twin and JSON over the API.