marimo on Amazon SageMaker

Run marimo, the reactive Python notebook, on Amazon SageMaker Studio and Studio Lab.

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⚡ One-Command Setup

For SageMaker Studio Lab (free, no AWS account required):

curl -fsSL https://raw.githubusercontent.com/scttfrdmn/aws-sagemaker-marimo/main/bootstrap.sh | bash

Then start marimo:

~/start-marimo.sh

📖 Full Bootstrap Guide | ⚙️ Manual Setup

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⚠️ Known Limitation: WebSocket on SageMaker Studio Lab

marimo's home page loads but notebooks show blank cells or "connecting" status.

This is a known limitation of SageMaker Studio Lab's gateway/ALB infrastructure. HTTP proxying through jupyter-server-proxy works correctly — you can browse marimo's file list — but WebSocket connections (which marimo requires for cell execution) are dropped by the SageMaker gateway on /proxy/PORT/ paths. This affects all WebSocket-dependent proxied applications, not just marimo.

What works:

• ✅ marimo home page / file browser via /proxy/2718/
• ✅ HTTP API requests through the proxy
• ✅ jupyter-server-proxy 4.4.0 (conda default) — no downgrade needed

What doesn't work (without the shim):

• ❌ Interactive notebook editing (requires WebSocket)
• ❌ Cell execution and reactive updates (requires WebSocket)

Workaround included: This repo uses ws-sse-proxy to translate WebSocket to SSE, making marimo fully functional on Studio Lab. See WEBSOCKET-STATUS.md for details, or just run:

bash start-marimo-shim.sh
# Then access at /proxy/2719/

Tracking: marimo-jupyter-extension #8 and marimo #8060

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🚀 Quick Start (5 Minutes)

Want to try marimo right now?

👉 Start with the Quick Start Guide - Get marimo running in 5 minutes on SageMaker Studio Lab (free!) or Studio.

📚 What's Included

This repository provides:

1. 📖 Complete Blog Post (~2000 words)

   • Deep dive into marimo's features
   • Why use marimo on SageMaker
   • Architecture overview
   • Deployment strategies
   • Best practices and troubleshooting

2. ⚡ Quick Start Guide

   • 5-minute setup for Studio Lab (free)
   • Easy installation for Studio
   • Sample notebooks
   • Troubleshooting tips

3. 🔧 Infrastructure as Code

   • terraform/ - Complete Terraform deployment
   • cdk/ - AWS CDK (Python) deployment
   • Lifecycle configurations
   • Sample notebooks

4. 🎓 Demo Notebook

   • Complete ML workflow
   • Interactive data exploration
   • Model training with reactive parameters
   • SageMaker integration examples

🎯 Choose Your Path

Path 1: Just Try It (Fastest)

Perfect for: Learning, experimenting, quick demos

1. Get free SageMaker Studio Lab account
2. Follow QUICKSTART.md
3. Try the sample notebook
4. Total time: ~10 minutes

Path 2: Manual Setup on Studio/Studio Lab

Perfect for: Individual users, existing Studio environment

1. Open SageMaker Studio or Studio Lab
2. Run pip install marimo jupyter-server-proxy
3. Start with marimo edit --host 0.0.0.0 --port 2718 --no-token --headless
4. See QUICKSTART.md for details
5. Note: On Studio Lab, WebSocket connections are blocked by the gateway — see known limitation

Path 3: Production Deployment

Perfect for: Teams, production workloads, persistent setup

1. Read the blog post for architecture understanding
2. Choose Terraform or CDK
3. Deploy with one command
4. Get automated, persistent marimo installation

💡 Why marimo?

Traditional Jupyter notebooks have well-known issues:

• ❌ Hidden state from out-of-order execution
• ❌ JSON format causes Git conflicts
• ❌ ~75% of notebooks on GitHub don't run
• ❌ Hard to reproduce research

marimo solves these problems:

• ✅ Reactive execution - cells auto-update when dependencies change
• ✅ Stored as pure Python - Git-friendly, executable as scripts
• ✅ No hidden state - deterministic, reproducible
• ✅ Interactive UI widgets - no callbacks needed
• ✅ Three tools in one - notebook, script, and web app

🏗️ Architecture

┌─────────────────────────────────────┐
│   SageMaker Studio / Studio Lab     │
│  ┌───────────────────────────────┐  │
│  │  JupyterLab Environment       │  │
│  │  ┌─────────────────────────┐  │  │
│  │  │ jupyter-server-proxy    │  │  │
│  │  │         ↓                │  │  │
│  │  │ marimo server (:2718)   │  │  │
│  │  └─────────────────────────┘  │  │
│  └───────────────────────────────┘  │
└─────────────────────────────────────┘

📦 Repository Structure

.
├── README.md                    # This file
├── QUICKSTART.md               # 5-minute setup guide
├── BOOTSTRAP.md                # One-command bootstrap guide
├── STUDIO-LAB-SETUP.md         # Automated Studio Lab setup
├── BADGES.md                   # Badge options for READMEs
├── WEBSOCKET-STATUS.md         # WebSocket proxy status & research
├── CONTRIBUTING.md             # Contribution guidelines
├── CHANGELOG.md                # Version history (Keep a Changelog)
├── LICENSE                     # MIT License
├── VERSION                     # Semantic version (0.1.0)
├── blog-post.md                # Full blog post (~2000 words)
├── sagemaker_ml_demo.py        # Complete demo notebook
├── bootstrap.sh                # One-command setup script
├── start-marimo-shim.sh        # Start marimo with WebSocket shim
├── studio-lab-setup.sh         # Setup script with conda env
├── terraform/                  # Terraform IaC (coming soon)
├── cdk/                        # AWS CDK IaC (coming soon)
└── notebooks/                  # Sample notebooks (coming soon)

🎓 Sample Notebooks

Quick Demo

import marimo as mo

# Interactive slider
slider = mo.ui.slider(0, 100, value=50)

# Automatically updates when slider changes!
result = slider.value ** 2
mo.md(f"Value: {slider.value}, Squared: {result}")

SageMaker Integration

import marimo as mo
import boto3

sagemaker = boto3.client('sagemaker')

# List training jobs
jobs = sagemaker.list_training_jobs(MaxResults=10)

# Interactive table
mo.ui.table(jobs['TrainingJobSummaries'])

See sagemaker_ml_demo.py for a complete, production-ready example.

🚢 Deployment Options

Option 1: Terraform

cd terraform
terraform init
terraform apply

Creates:

• SageMaker Studio Domain
• VPC and security groups
• IAM roles
• Lifecycle configuration for marimo
• S3 bucket for artifacts

Option 2: AWS CDK

cd cdk
pip install -r requirements.txt
cdk deploy

Same infrastructure as Terraform, using Python CDK constructs.

Option 3: Manual (Quickest)

See QUICKSTART.md - just pip install marimo and go!

💰 Cost Comparison

Option	Cost	Best For
Studio Lab	$0 (100% free)	Learning, small projects
Studio (manual)	~$0.05-2/hour	Individual use, testing
Studio (IaC)	~$1-5/hour	Teams, production

marimo's lightweight architecture means minimal overhead costs.

🔧 Maintenance

Updating marimo

Studio Lab / Manual:

pip install --upgrade marimo

With Lifecycle Config: Update the version in install-marimo.sh and redeploy lifecycle configuration.

Cleanup

Terraform:

terraform destroy

CDK:

cdk destroy

Manual: Just stop using it - no infrastructure to clean up!

🤝 Use Cases

marimo on SageMaker is perfect for:

• 🔬 Reproducible Research - Pure Python format, no hidden state
• 👥 Team Collaboration - Git-friendly, version-controlled notebooks
• 📊 Interactive Dashboards - Reactive UI updates, deploy as web apps
• 🚀 MLOps Pipelines - Run notebooks as scripts in CI/CD
• 🎓 Teaching & Demos - Predictable execution, professional output
• 🔍 Data Exploration - Interactive filtering and visualization

🆚 marimo vs Jupyter

When to use marimo:

• ✅ Building dashboards or interactive apps
• ✅ Need reproducible, version-controlled research
• ✅ Want reactive, automatic updates
• ✅ Creating reusable modules or pipelines
• ✅ Teaching or presenting (no hidden state issues)

When to use Jupyter:

• ✅ Quick ad-hoc exploration
• ✅ Team heavily invested in Jupyter ecosystem
• ✅ Need specific Jupyter extensions

Best practice: Use both! Convert between formats as needed with marimo convert.

❓ Troubleshooting

Common issues and solutions are in QUICKSTART.md.

Quick fixes:

• Can't access UI: Check proxy URL path
• Port in use: Use different port (--port 8889)
• Proxy not working: Run jupyter serverextension enable --py jupyter_server_proxy

🎯 Next Steps

1. ✅ Try the Quick Start (5 minutes)
2. ✅ Read the blog post for deep dive
3. ✅ Run the demo notebook
4. ✅ Convert your Jupyter notebooks: marimo convert notebook.ipynb
5. ✅ Deploy with infrastructure-as-code for production use

🌟 Features Showcase

Reactive Execution

# Change slider, everything updates automatically
slider = mo.ui.slider(0, 100)
filtered_data = data[data['value'] > slider.value]
plot = create_plot(filtered_data)  # Auto-updates!

Git-Friendly

# Clean diffs, no JSON
git diff notebook.py

# Run as script
python notebook.py

# Deploy as app
marimo run notebook.py

Interactive UI

# No callbacks needed!
dropdown = mo.ui.dropdown(['A', 'B', 'C'])
table = mo.ui.table(dataframe)
plot = mo.ui.plotly(figure)

📄 License

This repository: MIT License

marimo: Apache 2.0 License

🙏 Acknowledgments

• marimo team - for building an amazing reactive notebook platform
• AWS SageMaker team - for creating a flexible ML platform
• Community - for feedback and contributions

📬 Support

• Issues: Open an issue in this repository
• marimo Discord: https://marimo.io/discord
• AWS Support: https://aws.amazon.com/support/

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📚 Documentation

• Quick Start Guide - Get running in 5 minutes
• Bootstrap Guide - One-command automated setup
• Studio Lab Setup - Persistent conda environment
• WebSocket Status - WebSocket limitation details
• Blog Post - Complete guide (~2000 words)
• Badge Options - Add badges to your own projects
• Contributing - How to contribute
• Changelog - Version history

📝 Project Info

• Version: 0.1.0 (Semantic Versioning)
• License: MIT
• Copyright: © 2026 Scott Friedman
• Changelog: Keep a Changelog format

🤝 Contributing

Contributions are welcome! Please read CONTRIBUTING.md for guidelines.

To contribute:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

See CHANGELOG.md for version history.

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Ready to get started?

👉 One command: curl -fsSL https://raw.githubusercontent.com/scttfrdmn/aws-sagemaker-marimo/main/bootstrap.sh | bash

👉 Or manual: QUICKSTART.md

👉 Deep dive: Full blog post

Happy reactive coding! 🚀