A decentralized platform for training and reusing privacy-preserving single-cell foundation models. Chiron brings the full federated training life cycle into a single browser interface, so any institution can join a federation without an infrastructure team.
Live deployment: https://chiron.aicell.io. The flagship model trained through Chiron is Tabula, a single-cell foundation model that combines tabular learning over genes with federated learning across institutions.
Single-cell foundation models scale by aggregating data, but raw single cell data carries patient identity and rarely leaves the institution that owns it. Chiron removes that barrier. Each participating institution runs a lightweight worker on its own hardware. A central orchestrator coordinates federated rounds with FedAvg aggregation. Only shared transformer weights and scalar metrics cross the network. Raw data never moves over external channels.
The platform is built on top of BioEngine, the distributed-computing layer that handles container scheduling, RPC, and worker registration. Chiron adds three BioEngine applications that implement the federated-learning roles:
- Chiron Manager is the control plane. It discovers datasets, spawns and tears down orchestrator and trainer applications, surfaces logs, and reports cluster state.
- Chiron Orchestrator runs the Flower-based FedAvg server that coordinates one federated training session at a time. It broadcasts the current transformer to every registered trainer, collects updated weights after each round, aggregates them, and repeats for the configured number of rounds.
- Tabula Trainer is the local Flower client that trains on the institution's private datasets. Multiple trainers register to one orchestrator. The trainer surface is foundation-model agnostic, so a different model (for example scGPT or Geneformer) can join a federation through the same trainer template.
Published checkpoints land in the Chiron Model Hub, a versioned artifact collection with public landing pages, so any user can browse, fork, retrain, or republish a model.
- Browse published Tabula checkpoints on the Model Hub, including per-tissue foundation snapshots and client-specific fine-tunes.
- Set up a Chiron worker on your own hardware through a browser wizard that emits a one-line launch command for Docker Compose, Podman, Singularity or Apptainer. The worker hosts two isolated containers: a data server that exposes private datasets only over the worker's container-internal network, and a Tabula trainer that holds the GPU-bound model.
- Launch and monitor a federated training run through the Training tab, with per-round training and validation loss curves and live downstream task metrics.
- Publish trained checkpoints to the Model Hub, either the FedAvg-aggregated global transformer or a trainer's full client-specific model.
- Drive Chiron from an AI agent through the public Chiron skill. Every action available in the UI is also available through Hypha RPC, and the skill is the contract any compatible AI agent can read to set up workers and data, launch and monitor a training run, or browse and reuse published models.
┌────────────────────────────────────┐
│ chiron.aicell.io (this repo) │
│ React + TypeScript frontend │
└──────┬──────────────────┬──────────┘
│ Hypha RPC │ Hypha artifacts
▼ ▼
┌──────────────────────────────┐ ┌──────────────────────┐
│ Chiron Manager (per worker) │ │ Chiron Model Hub │
│ control plane RPC service │ │ versioned artifact │
└──────────────┬───────────────┘ │ collection │
│ deploys └──────────────────────┘
▼
┌──────────────────────────────┐ ┌──────────────────────┐
│ Chiron Orchestrator │◄──┤ Tabula Trainer (N) │
│ Flower FedAvg server │ │ Flower client per │
│ per training session │──►│ participating site │
└──────────────────────────────┘ └──────────────────────┘
▲ scalar weights + metrics only ▼
───────────────────────────────────
Raw cell data never crosses this line.
Each BioEngine worker registers under the chiron-platform Hypha
workspace and exposes its Chiron apps as Ray Serve deployments. Service
discovery, authentication, and per-dataset access control are handled
by BioEngine and Hypha; Chiron only owns the federated-training logic
and the user-facing surface.
This repository contains the Chiron web frontend that powers
https://chiron.aicell.io and the
federated orchestration glue that the BioEngine worker pulls in
as a startup application. The BioEngine application sources for the
Chiron Manager, Chiron Orchestrator, and Tabula Trainer live in the
sibling aicell-lab/tabula
repository, alongside the Tabula model code itself.
chiron-platform/
├── src/ # React + TypeScript frontend
│ ├── pages/ # Top-level pages (Landing, Models,
│ │ # MyModels, ModelDetail, Runs,
│ │ # AgentLab)
│ ├── components/
│ │ ├── BioEngine/ # Worker dashboard, setup wizard,
│ │ │ # app deployment UI
│ │ ├── training/ # Federated training tab, run
│ │ │ # monitor, save / publish weights
│ │ ├── models/ # Model Hub grid, model card
│ │ └── notebook/ # Embedded agent-lab notebook
│ ├── store/ # Zustand state (Hypha connection,
│ │ # auth, artifact cache)
│ ├── utils/ # Hypha RPC + artifact helpers
│ └── providers/ # Auth + Hypha providers
├── public/
│ └── skills/chiron-platform/ # Public AI-agent skill served at
│ # chiron.aicell.io/skills/...
├── worker/ # Optional worker-side assets
├── tests/e2e/ # End-to-end federated run test
└── scripts/ # Build, deploy, maintenance scripts
Requirements: Node.js 18+ and pnpm 8+.
git clone https://github.com/aicell-lab/chiron-platform.git
cd chiron-platform
pnpm install
pnpm start # http://localhost:3000The UI talks to the live Hypha workspace at https://hypha.aicell.io
and the chiron-platform workspace. No backend needs to run locally.
Open chiron.aicell.io/#/worker,
click Launch your own BioEngine instance, fill in worker name,
data directory, container runtime, and resource allocation, and copy
the one-line launch command. The wizard targets Docker Compose,
Podman, Singularity, and Apptainer. The launched worker auto-registers
in the chiron-platform workspace and shows up on the worker page
with its name, datasets, and hardware. The data server rescans the
data directory every 30 seconds.
Dataset preparation (folder layout, manifest.yaml, what the data
server handles automatically) is documented in the AI-agent skill at
public/skills/chiron-platform/references/data-prep.md.
Point any compatible coding agent (Claude Code, Gemini CLI, Cursor, etc.) at the public skill URL and let it set up the worker, prepare the data, and launch the run on your behalf:
https://chiron.aicell.io/skills/chiron-platform/SKILL.md
The Chiron landing page exposes the same prompt as a one-click copy button.
| Command | What it does |
|---|---|
pnpm start |
Start the dev server on port 3000 with hot reload. |
pnpm build |
Production build into build/. |
pnpm test |
Run the React Testing Library suite. |
pnpm tsc --noEmit |
Type-check without emitting output. |
Coding standards, naming conventions, and architecture rules are
documented in CLAUDE.md.
End-to-end federated runs are covered by
tests/e2e/full_pipeline.py. It deploys
an orchestrator and two trainers on the demo workers, runs five
training rounds with a mid-run late-join, and verifies that the run
artifact lands in the user workspace.
- Frontend. React 18, TypeScript, Tailwind CSS, Zustand, React
Router. Build via Create React App /
react-scripts. - State + RPC. Hypha JavaScript SDK for service discovery and RPC, Hypha artifact manager for the Model Hub, presigned S3 URLs for file downloads.
- Orchestration. BioEngine for worker scheduling and Ray Serve for per-app deployments. Federated learning is implemented with the Flower framework over BioEngine's RPC layer.
- Backend apps. Chiron Manager, Chiron Orchestrator, and Tabula
Trainer are Python applications shipped from
aicell-lab/tabula.
External contributions are welcome, especially trainer images for
additional foundation models. See the trainer template at
public/skills/chiron-platform/references/trainer-artifact-template.md
for the model-side engineering and the Hypha RPC contract.
For platform-level changes (UI, orchestrator, manager, deployment),
open an issue or pull request on
aicell-lab/chiron-platform.
Please follow the conventions in CLAUDE.md: functional
React components, TypeScript types on RPC responses, no speculative
abstractions, and the smallest change that addresses the request.
The Chiron Platform code in this repository was developed at the AICell Lab, Department of Applied Physics, KTH Royal Institute of Technology / Science for Life Laboratory (SciLifeLab), Stockholm, Sweden.
- Nils Mechtel. Main author, platform design and implementation. KTH Royal Institute of Technology / SciLifeLab.
- Wei Ouyang. Group leader, technical guidance. KTH Royal Institute of Technology / SciLifeLab.
The platform was built in collaboration with Jiayuan Ding, Jianhui
Lin, Ziyang Miao, Min Li, Jiliang Tang, Yuancheng Ryan Lu, Xiaojie
Qiu, and the rest of the co-authors on the Chiron / Tabula paper (see
Citation below). The CITATION.cff file
carries the full author list with affiliations.
This repository is maintained at
aicell-lab/chiron-platform.
A read-only copy is mirrored to
aristoteleo/chiron for the
paper's Code Availability statement. Both copies are MIT-licensed and
share the same commit history. Please open issues and pull requests on
the AICell Lab repository.
Released under the MIT License. See LICENSE for the full
terms.
If you use Chiron Platform in your research, please cite the Chiron / Tabula paper:
Ding J., Lin J., Miao Z., Mechtel N., Jiang S., Wang Y., Fang Z., Martin-Rufino J. D., Weng C., Saunders R., Xu W., Weissman J. S., Li M., Tang J., Ouyang W., Lu Y. R., and Qiu X. Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning. bioRxiv, 2025. doi: 10.1101/2025.01.06.631427.
The CITATION.cff file in this repository carries the
full author list with affiliations. GitHub's Cite this repository
button reads from it.
We thank the single-cell genomics community and all collaborating institutions whose data, infrastructure, and feedback shaped this platform. Chiron stands on top of BioEngine for distributed worker orchestration, Hypha for RPC and artifact management, and Flower for the federated-learning runtime.