The Reactive Notebook That Ships to Production
FlowyML Notebook is a reactive, DAG-powered notebook environment that replaces Jupyter for production ML workflows. Write pure Python cells, get automatic dependency tracking, and ship directly to pipelines, dashboards, and apps — without changing a single line of code.
Reactive notebook editor with code cells, variable explorer, and full toolbar
| Feature | Jupyter | Deepnote | Marimo | FlowyML Notebook |
|---|---|---|---|---|
| Reactive DAG Execution | ❌ | ❌ | ✅ | ✅ |
Pure .py File Storage |
❌ | ❌ | ✅ | ✅ |
| Git-Native Collaboration | ❌ | ❌ | ✅ GitHub | |
| Pipeline Integration | ❌ | ❌ | ❌ | ✅ FlowyML |
| Reusable Recipes | ❌ | ❌ | ❌ | ✅ |
| One-Click Deploy | ❌ | ❌ | ✅ | |
| SQL First-Class | ❌ | ✅ | ✅ | ✅ |
| AI Assistant | ❌ | ✅ | ❌ | ✅ |
| Rich Data Explorer | ❌ | ✅ | ✅ | ✅ |
| App Mode | ❌ | ❌ | ✅ | ✅ |
| Self-Hosted | ✅ | ❌ | ✅ | ✅ |
| SmartPrep Advisor | ❌ | ❌ | ❌ | ✅ |
| Algorithm Matchmaker | ❌ | ❌ | ❌ | ✅ |
| Interactive Dashboards | ❌ | ❌ | ❌ | ✅ |
| Analysis Patterns | ❌ | ❌ | ❌ | ✅ |
| Keras Ecosystem | ❌ | ❌ | ❌ | ✅ UnicoLab |
# Install the core package
pip install flowyml-notebook
# Or install with all ML & AI extensions
pip install "flowyml-notebook[all]"
# Or install with Keras ecosystem (KDP + KerasFactory + MLPotion)
pip install "flowyml-notebook[keras]"fml-notebook dev # 🔥 Hot-reload development mode
fml-notebook start # 🚀 Production buildThe browser opens automatically. You're ready to build.
Every DataFrame gets automatic profiling — statistics, distributions, correlations, quality checks, and ML-ready insights. No extra code needed.
Automatic DataFrame profiling with column statistics, type detection, and memory impact
Interactive charts for every column — histograms, bar charts, and distribution analysis
Pearson correlation matrix with color-coded heatmap for quick feature analysis
Automated ML insights: outlier detection, scaling recommendations, and target variable suggestions
Cells are nodes in a dependency graph. Change a variable, and only dependent cells re-execute — automatically. Visualize the full pipeline with the built-in DAG view.
Visual dependency graph showing data flow: imports → data_generation → analysis → exploration → summary
Native integration with the UnicoLab ML ecosystem — KDP, KerasFactory, and MLPotion. All packages are optional and auto-detected.
| Package | What It Does | Integration Point |
|---|---|---|
| KDP | Keras preprocessing layers with distribution-aware encoding | SmartPrep Advisor |
| KerasFactory | 38+ reusable Keras layers and production-ready model architectures | Algorithm Matchmaker |
| MLPotion | Managed training pipelines with type-safe configuration | Algorithm Matchmaker |
When all three are installed, the Algorithm Matchmaker surfaces a flagship end-to-end pipeline: KDP → KerasFactory → MLPotion — preprocessing, model building, and training in a single deployable Keras model.
# Install the full ecosystem
pip install "flowyml-notebook[keras]"4 new builtin recipes are also included: KDP Smart Preprocessing, KerasFactory Quick Model, MLPotion Training Pipeline, and the UnicoLab End-to-End Pipeline.
Stop rewriting boilerplate. 43 built-in recipes across Core, Assets, Parallel, Observability, Evals, Data, ML, Visualization, and Ecosystem categories. Drag into your notebook or click to insert.
Searchable recipe library with FlowyML Step, Pipeline, Conditional Branching, and more
Collaborate directly in the notebook with inline comments and a review panel. Add notebook-level or cell-level annotations for team discussions.
Comments panel with threaded discussions, resolve/reply actions, and scatter plot output
Generate beautiful HTML or PDF reports from your notebook. Optionally include source code cells alongside outputs. Preview in browser, then download.
Report generation with HTML/PDF format selection, code inclusion toggle, and instant preview
Turn any notebook into an interactive web application with one click. Choose layout (Linear, Grid, Tabs, Sidebar, Dashboard), theme, and cell visibility.
Publish dialog with layout options, dark/light/auto theme, source code toggle, and per-cell visibility
Ship notebooks directly to production. Promote to pipeline, deploy as API/Docker/Batch, track kernel assets (DataFrames, models), and connect to FlowyML infrastructure.
Pipeline promotion with quick actions and @step decorators |
Deploy as API, Docker Container, or Batch Pipeline with infrastructure stacks |
Kernel assets: tracked DataFrames with size, shape, and type metadata
Full GitHub integration as the collaboration backend. Link a repository, branch, commit, and push — all from the notebook sidebar. No proprietary cloud needed.
Connect GitHub repository for team collaboration and versioning |
Save and browse notebook snapshots with cell-level diffs |
Run standalone (Local Mode) or connect to a FlowyML server (Remote Mode) for experiment tracking, pipeline export, and deployment. Full runtime details at a glance.
Environment panel: Local/Remote connection, runtime info (Python 3.12, IPython, Reactive DAG engine)
| Command | Description |
|---|---|
fml-notebook dev |
🔥 Launch with Vite hot reload |
fml-notebook start |
🚀 Launch with production build |
fml-notebook run <file> |
|
fml-notebook export <file> |
📦 Export as pipeline/HTML/PDF/Docker |
fml-notebook app <file> |
🌐 Deploy as interactive web app |
fml-notebook list --server <URL> |
📚 List notebooks on a server |
Visit FlowyML Notebook Docs for the complete guide:
- Getting Started — Install, launch, configure
- Features — Complete feature inventory
- Architecture — Reactive DAG engine internals
- Recipes — Reusable cell templates
- Collaboration — GitHub-based team workflows
- Integration — FlowyML instance connections
- Ecosystem — UnicoLab Keras ecosystem (KDP, KerasFactory, MLPotion)
- Data Exploration — Rich DataFrame profiling
- API Reference — CLI & Python API docs
git clone https://github.com/UnicoLab/flowyml-notebook.git
cd flowyml-notebook
make setup
make dev| Target | Description |
|---|---|
make setup |
🔧 Install Python package + frontend deps |
make dev |
🔥 Launch dev mode with hot reload |
make test |
🧪 Run all tests |
make lint |
🔍 Run Ruff linter |
make format |
✨ Auto-format code |
make docs |
📖 Build MkDocs documentation |
make docs-serve |
👁️ Preview docs locally |
make pre-commit |
🔒 Run pre-commit checks |
make release-dry-run |
🏷️ Dry-run semantic release |
make clean |
🧹 Remove build artifacts |
See CONTRIBUTING.md for the full contributor guide.
- 📖 Documentation
- 🐛 Bug Reports
- 💬 Discussions
- 📋 Contributing Guide
- 📜 Changelog
- 🔒 Security Policy
- 📝 Code of Conduct
Licensed under the Apache License 2.0.
Built with ❤️ by UnicoLab