OneOpen ML Studio

Open-source, local-first ML data platform — from raw data to training-ready datasets and models.

Computer vision is the deepest path today (boxes, polygons, SAM, Label Assist, versions, local train). Text, tabular, audio, video, and LLM project types ship with sample label UIs and packaging adapters. Team collaboration (orgs, workspaces, members, /setup) ships in 1.0; deeper enterprise (full SSO handshake, realtime cursors) is planned — see Roadmap.

OneOpen ML Studio

Docs

oneopensource.org/oneopen-ml-studio

Version

1.0.0

PyPI

oneopen-ml-studio

Source

GitHub

License

Apache-2.0

Local UI

http://127.0.0.1:8765

Quick install

pip install "oneopen-ml-studio[yolo]"
oneopen init
oneopen start

Open http://127.0.0.1:8765 — no cloud account required (Local mode).

Available today

Capability

Description

Projects

CV, text, tabular, audio, video, and LLM workspace types

CV annotate

Boxes, polygons, whole-image class labels; SAM + YOLO Label Assist

Sample annotate

Spans, transcripts, tabular values, LLM instruction/preference fields

Versions

Preprocess, augment, splits (image tasks)

Train / Export

Deepest for CV (YOLO / Torchvision); sklearn / HF prepare / modality packages

Quality & cleaning

Readiness gate + remediating clean actions per modality

Pipelines

Visual DAG editor + YAML/JSON runner

Collaboration

Orgs → workspaces → projects; Team/Enterprise /setup

Plugins

On-demand install of training extras

Target platform

See Product vision — annotation, quality, versioning, training readiness, experiments, model registry, active learning, and multi-user collaboration under one local-first / self-hosted roof.

Documentation contents

Indices