flowyml-notebook 1.2.0

Production-grade reactive notebook for FlowyML — the Jupyter replacement for ML pipelines

🌊 FlowyML Notebook

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

---

🚀 Why FlowyML Notebook?

Feature	Jupyter	Deepnote	Marimo	FlowyML Notebook
Reactive DAG Execution	❌	❌	✅	✅
Pure .py File Storage	❌	❌	✅	✅
Git-Native Collaboration	❌	⚠️ Cloud	❌	✅ GitHub
Pipeline Integration	❌	❌	❌	✅ FlowyML
Reusable Recipes	❌	❌	❌	✅
One-Click Deploy	❌	⚠️ Cloud	❌	✅
SQL First-Class	❌	✅	✅	✅
AI Assistant	❌	✅	❌	✅
Rich Data Explorer	❌	✅	✅	✅
App Mode	❌	❌	✅	✅
Self-Hosted	✅	❌	✅	✅

---

⚡ Quick Start

# Install the core package
pip install flowyml-notebook

# Or install with all ML & AI extensions
pip install "flowyml-notebook[all]"

fml-notebook dev    # 🔥 Hot-reload development mode
fml-notebook start  # 🚀 Production build

The browser opens automatically. You're ready to build.

---

🌟 Features

📊 Rich Data Exploration

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

---

🔄 Reactive DAG Engine

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

---

🧾 Recipes — Reusable Code Templates

Stop rewriting boilerplate. 39 built-in recipes across Core, Assets, Parallel, Observability, Evals, Data, ML, and Visualization categories. Drag into your notebook or click to insert.

Searchable recipe library with FlowyML Step, Pipeline, Conditional Branching, and more

---

💬 Comments & Review

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

---

📄 Reports — One-Click Export

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

---

🌐 Publish as App

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

---

🚀 Production — Pipelines, Deploy & Assets

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

---

🤝 Git & Version Control

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

---

⚙️ Environment & FlowyML Connection

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)

---

🛠️ CLI Reference

Command	Description
fml-notebook dev	🔥 Launch with Vite hot reload
fml-notebook start	🚀 Launch with production build
fml-notebook run <file>	▶️ Execute a notebook headlessly
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

---

📚 Documentation

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
• Data Exploration — Rich DataFrame profiling
• API Reference — CLI & Python API docs

---

🛠️ Development

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.

---

🤝 Community

• 📖 Documentation
• 🐛 Bug Reports
• 💬 Discussions
• 📋 Contributing Guide
• 📜 Changelog
• 🔒 Security Policy
• 📝 Code of Conduct

---

📄 License

Licensed under the Apache License 2.0.

Built with ❤️ by UnicoLab