Principles

Local-first

Simple local install:

pip install oneopen-ml-studio
oneopen start

Local mode requires:

  • No external cloud account

  • No mandatory registration

  • No external data upload

  • No extra infrastructure beyond Python

  • SQLite + local filesystem

Self-hosted by default

Deploy on a workstation, internal server, VM, Docker Compose, Kubernetes, or private cloud.

Framework-neutral

Avoid hard dependency on a single ML stack. Pluggable adapters target Ultralytics, PyTorch, TensorFlow/Keras, scikit-learn, XGBoost, LightGBM, Hugging Face, spaCy, Detectron2, MMDetection, Whisper, and custom scripts.

Modality-independent architecture

Internal design supports images first, then video, text, documents, audio, tabular, time series, 3D/point cloud, LLM instruction/preference data, and RAG evaluation sets.

Reproducible

Every model should be traceable to dataset version, transform pipeline, schema, training config, source checksums, framework versions, weights, seed, and hardware.

Extensible

New capabilities arrive through plugins (oneopen.datasource, oneopen.trainer, oneopen.exporter, …) rather than hard-coded forks.