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Documentation Index

Fetch the complete documentation index at: https://docs.cirron.com/llms.txt

Use this file to discover all available pages before exploring further.

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Quickstart Guide

Get up and running with Cirron CLI in just a few minutes. This guide will walk you through installing the CLI, creating your first project, and deploying it.

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Prerequisites

  • Node.js (v20.19 or higher) - for the CLI
  • Python (3.11 or higher) - for the project

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Installation

Install the Cirron CLI globally: 
npm install -g @cirron/cli
Verify the installation: 
cirron --version
A curl one-line installer and a Homebrew formula are coming. For now, npm is the supported distribution channel.

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Step 1: Create Your First Project

Create a new ML project with an interactive setup: 
cirron init
The CLI will guide you through:
  • Project name
  • Framework selection (PyTorch, TensorFlow, scikit-learn)
  • Model type (Classification, Regression, etc.)
  • Sample data inclusion
  • Jupyter notebook setup
Or create a project with specific options: 
cirron init my-first-model --template pytorch --git
Note: You can use the CLI without authentication for local development. Authentication is only needed to connect your project to the Cirron platform.

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Step 2: Navigate to Your Project

cd my-first-model
You can run the generated project directly with python train.py and then python serve.py, or drive it through the CLI as shown below.

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Step 3: Test Your Project

Run the test suite to ensure everything is working: 
cirron test
This will test:
  • Environment compatibility
  • Requirements validation
  • Unit tests
  • Model loading
  • Data validation

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Step 4: Build Your Project

Build your project for deployment: 
cirron build
This creates a production-ready build of your ML model.

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What You’ve Built

Congratulations! You’ve successfully: ✅ Installed Cirron CLI
✅ Created a new ML project
✅ Tested your project
✅ Built your project
✅ Connected to Cirron platform (optional)
✅ Deployed your project (optional)

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Next Steps

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Explore Your Project

Your project structure includes: For a PyTorch project: 
my-first-model/
├── cirron.yaml           # Project configuration
├── train.py              # Training entry point
├── serve.py              # Serving entry point
├── requirements.txt      # Python dependencies
├── models/               # Trained model files
├── artifacts/            # Build and run artifacts
└── build/                # Build output
The exact files depend on the template you chose.

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Development Workflow

  1. Make changes to your model or code
  2. Test locally with cirron test
  3. Build with cirron build
  4. Deploy with cirron deploy (coming soon)

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Advanced Features

  • Local development: Work entirely offline with full CLI functionality
  • Platform integration: Connect to Cirron for deployment and monitoring
  • Multiple environments: Deploy to staging and production
  • Environment variables: Manage configuration per environment
  • Rollback: Quickly revert to previous deployments
  • Monitoring: View logs and project status

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Troubleshooting

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Installation Issues

# Check Node.js version
node --version

# Reinstall CLI
npm uninstall -g @cirron/cli
npm install -g @cirron/cli

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Project Issues

# Check project configuration
cat cirron.yaml

# Reinitialize project
cirron init --force

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Multiple models in one repo?

If you keep several models in a single repository, add a root cirron.yaml with a workspace: key and the CLI can discover and validate all of them from the repo root. See Monorepo support.

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Need Help?

Ready to build your next ML project? Check out the full CLI documentation for advanced features and workflows.