OneOpen ML Studio
Open-source, local-first ML data platform
From raw data → annotation → validated versions → training-ready datasets → models.
Product definition
An open-source, local-first and self-hosted collaborative machine-learning data platform for preparing, annotating, validating, versioning, training and managing ML datasets and models.
Core workflow (target platform):
Raw Data → Profiling → Annotation → Review → Quality Validation
→ Transformation → Dataset Version → Training Readiness
→ Model Training → Experiment Comparison → Model Registry → Active Learning
Differentiation:
Multi-modal annotation
+ Training-ready dataset generation
+ Reproducible versioning
+ Multi-user collaboration
+ Shared GPU training
+ Framework-neutral adapters
+ Local-first deployment
Computer vision, text, tabular, audio, video, and LLM dataset workflows ship in this package. Visual pipelines, Team/Enterprise setup, and org→workspace→project hierarchy are included. See the docs roadmap for planned SSO handshake and shared GPU scheduling.
Install
pip install oneopen-ml-studio
With extras:
pip install "oneopen-ml-studio[yolo]" # Ultralytics Label Assist + training
pip install "oneopen-ml-studio[libreyolo]" # LibreYOLO backend
pip install "oneopen-ml-studio[sam]" # SAM deps (torch); install segment-anything separately
pip install "oneopen-ml-studio[all]"
From source:
git clone https://github.com/1-OpenSource/OneOpen-ML-Studio.git
cd OneOpen-ML-Studio
pip install -e ".[yolo]"
Quick start (local mode)
oneopen init
oneopen start # alias: oneopen serve → http://127.0.0.1:8765
Or: python -m oneopen_ml_studio start
No cloud account. No mandatory registration. Data stays under ~/.oneopen (or ONEOPEN_DATA_DIR).
What works today
| Capability | Status |
|---|---|
| Local CV: annotate, versions, YOLO / Torchvision train & export | Available |
| Image classification & segmentation catalogs | Available |
| Dataset profiling, quality checks, readiness score | Available |
| Experiment tracking + training adapters | Available |
| Text / tabular / audio / video / LLM workspaces | Available |
| Plugin manager (auto-install extras) | Available |
| Active learning selection | Available |
| Plugin registry + pipeline CRUD/runner | Available |
| Docker Compose (Postgres/Redis/MinIO) | Available |
| Multi-user orgs, review queues, Celery workers | Available (SSO handshake forthcoming) |
| Kubernetes enterprise starter | Available |
See docs: Quality and Experiments tabs in the project UI, and /api/platform.
Platform modes (vision)
| Mode | Users | Stack (target) |
|---|---|---|
| Local | Individuals, students, offline | FastAPI + UI, SQLite, local FS, optional GPU |
| Team server | Labs & startups | PostgreSQL, Redis, Celery, MinIO/S3, auth, workers |
| Enterprise | Regulated / multi-team | K8s, SSO (OIDC/SAML/LDAP), audit, HA, quotas |
Documentation
https://oneopensource.org/oneopen-ml-studio/
Includes product vision, architecture, roadmap, package notes, and the full user guide.
pip install "oneopen-ml-studio[docs]"
cd docs && make html # Windows: .\make.bat html
CLI
oneopen start Start local web UI (default :8765)
oneopen serve Same as start
oneopen init Initialize ~/.oneopen
oneopen version Print version
License
Apache License 2.0 — see LICENSE and NOTICE.
@software{oneopensource_oneopen_ml_studio_2026,
author = {oneopensource},
title = {OneOpen ML Studio},
year = {2026},
url = {https://github.com/1-OpenSource/OneOpen-ML-Studio},
note = {Open-source local-first ML data platform}
}
oneopensource — creator and maintainer.
Metadata
Release files for oneopen-ml-studio 1.0.0
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| File | Size | Uploaded | Provenance | |
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| oneopen_ml_studio-1.0.0.tar.gz | 237.4 kB | Without provenance | Details |
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| oneopen_ml_studio-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 505.7 kB
Release files / oneopen_ml_studio-1.0.0.tar.gz
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