cirron init

Initialize a new Cirron project with pre-configured templates for different ML frameworks. The init command creates a complete project structure with all necessary files, configuration, and dependencies.

Usage

cirron init [project-name] [options]

Arguments

Argument	Description	Required
`project-name`	Name of the project to create	No (interactive mode)

Options

Option	Description	Default
`--template, -t`	Template to use (see templates below)	`pytorch`
`--force, -f`	Overwrite existing directory	`false`
`--git`	Initialize git repository	`false`
`--no-install`	Skip dependency installation	`false`

Templates

PyTorch Templates

# Basic PyTorch model
cirron init my-model --template pytorch

# PyTorch training pipeline
cirron init my-model --template pytorch-train

Creates a project with:

PyTorch and torchvision
Common ML dependencies (numpy, pandas, matplotlib)
Model definition and training scripts
Docker configuration
Testing setup

TensorFlow Templates

# Basic TensorFlow model
cirron init my-model --template tensorflow

# TensorFlow training pipeline
cirron init my-model --template tensorflow-train

Creates a project with:

TensorFlow and Keras
GPU support configuration
Data pipeline utilities
Model training scripts
Docker with GPU support

Scikit-Learn Templates

# Basic scikit-learn model
cirron init my-model --template sklearn

# Full ML pipeline
cirron init my-model --template sklearn-pipeline

Creates a project with:

scikit-learn, pandas, numpy
Data preprocessing utilities
Model evaluation tools
Lightweight Docker setup

Custom Template

cirron init my-model --template custom

Creates a blank Python project for any ML framework.

Interactive Mode

When you run cirron init without a project name, you’ll be prompted for:

Project Name

Validation: Project name can only contain letters, numbers, hyphens, and underscores
Default: my-ml-project

Framework Selection

Choose from available templates:

PyTorch - PyTorch model with training and inference
TensorFlow - TensorFlow/Keras model with training and inference
Scikit-Learn - Scikit-learn model with preprocessing and inference
PyTorch Training - PyTorch training pipeline with data loading
TensorFlow Training - TensorFlow training pipeline with data loading
Scikit-Learn Pipeline - Full ML pipeline with preprocessing and training
Custom Framework - Blank Python project for any ML framework

Model Type

Choose from available model types:

Classification - Classification models
Regression - Regression models
Computer Vision - Computer vision models
Natural Language Processing - NLP models
Time Series - Time series models
Custom - Custom model types

Additional Options

Include Sample Data - Include sample data for testing (default: true)
Include Jupyter Notebook - Include a Jupyter notebook for experimentation (default: true)

Examples

Basic Usage

# Interactive project creation
cirron init

# Create a PyTorch project
cirron init sentiment-analysis

# Create a TensorFlow training pipeline
cirron init image-classifier --template tensorflow-train

# Create a scikit-learn pipeline
cirron init recommendation-engine --template sklearn-pipeline

Advanced Usage

# Force overwrite existing directory
cirron init my-model --force

# Initialize with git repository
cirron init my-model --git

# Skip dependency installation
cirron init my-model --no-install

# Combine options
cirron init my-model --template pytorch-train --git

Project Structure

After running cirron init, you’ll get a project structure like this:

my-model/
├── cirron.yaml              # Project configuration
├── src/
│   ├── __init__.py
│   ├── model.py             # Model definition
│   ├── train.py             # Training script
│   └── predict.py           # Prediction script
├── tests/
│   ├── __init__.py
│   └── test_model.py        # Unit tests
├── data/                    # Data directory
│   └── sample/              # Sample data (if included)
├── models/                  # Saved models
├── notebooks/               # Jupyter notebooks (if included)
├── Dockerfile               # Container configuration
├── requirements.txt         # Python dependencies
├── README.md               # Project documentation
└── .gitignore              # Git ignore file

Configuration File

The cirron.yaml file is automatically created with comprehensive configuration:

{
  "name": "my-model",
  "version": "1.0.0",
  "template": "pytorch",
  "framework": "pytorch",
  "modelType": "classification",
  "pythonVersion": "3.9",
  "gpuRequired": false,
  "environments": {
    "development": {
      "name": "development",
      "url": "http://localhost:8000"
    },
    "staging": {
      "name": "staging"
    },
    "production": {
      "name": "production"
    }
  },
  "build": {
    "outputDir": "dist",
    "command": "docker build -t ${PROJECT_NAME} .",
    "include": ["src/**", "requirements.txt", "Dockerfile"],
    "exclude": ["*.pyc", "__pycache__", ".pytest_cache", "data/raw/**"]
  },
  "deploy": {
    "provider": "custom",
    "settings": {
      "containerRegistry": "harbor",
      "imageTag": "${VERSION}"
    }
  },
  "artifacts": {
    "modelPath": "models/",
    "checkpointPath": "checkpoints/",
    "logsPath": "logs/"
  },
  "test": {
    "dataPaths": {
      "sample": "data/sample/sample_data.pt",
      "validation": "data/sample/sample_data.pt",
      "inference": "data/sample/sample_data.pt"
    },
    "fallbackToDummy": true,
    "variables": {
      "featureCount": 10,
      "targetColumn": "labels",
      "dataFormat": "tensor",
      "framework": "pytorch"
    }
  },
  "metadata": {
    "modelClassName": "ClassificationModel",
    "architecture": "Neural Network",
    "lastUpdated": "2024-01-15T10:30:00.000Z",
    "inputShape": "(1, 3, 224, 224)",
    "detectedPatterns": []
  }
}

Model Type Configuration

Default Model Class Names

Each template and model type combination gets an appropriate default model class name:

PyTorch Classification: ClassificationModel
PyTorch Regression: RegressionModel
PyTorch Computer Vision: CNNModel
PyTorch NLP: TransformerModel
PyTorch Time Series: LSTMModel
TensorFlow Classification: ClassificationModel
Scikit-Learn Classification: ClassificationPipeline
Custom: Model

Default Architectures

PyTorch: Neural Network
TensorFlow: Keras Model
Scikit-Learn: Scikit-learn Pipeline
Custom: Custom Model

Default Input Shapes

PyTorch: (1, 3, 224, 224)
TensorFlow: (224, 224, 3)
Scikit-Learn: Varies by dataset
Custom: Not specified

Git Integration

Automatic Git Initialization

When using the --git flag, the command automatically:

Initializes Git Repository: Runs git init
Adds All Files: Runs git add .
Creates Initial Commit: Runs git commit -m "Initial commit"

Git Information

If a git repository exists, the command captures:

Commit Hash: Current commit hash
Repository Info: Git repository metadata

Dependency Installation

Automatic Installation

By default, the command automatically installs dependencies. Use --no-install to skip. The installation:

Installs Dependencies: Runs pip install -r requirements.txt
Error Handling: Continues if installation fails
Logging: Shows installation progress and warnings

Post-Install Commands

Each template includes post-install commands:

pip install -r requirements.txt

Project Registration

Cirron API Integration

If you’re authenticated with Cirron, your project will be automatically registered:

API Registration: Creates project in Cirron API
Project Metadata: Sends project configuration
Error Handling: Continues if registration fails

Authentication Check

If not authenticated, you’ll see a tip:

Tip: Run cirron auth login to connect to Cirron

Next Steps

After initializing your project:

Navigate to the project:
cd my-model
Install dependencies (if not done automatically):
pip install -r requirements.txt
Run tests:
cirron test
Build project:
cirron build
Deploy:
cirron deploy

Error Handling

Directory Already Exists

# Error: Directory 'my-model' already exists
cirron init my-model

# Interactive confirmation for overwrite
cirron init my-model
# WARNING: There is already a model with this name (my-model) and this action will overwrite existing files. This cannot be undone. Continue anyway? (y/N)

Invalid Project Name

# Error: Project name can only contain letters, numbers, hyphens, and underscores
cirron init my@model

# Valid project names
cirron init my-model
cirron init my_model
cirron init mymodel123

Invalid Template

# Error: Unknown template: invalid
cirron init my-model --template invalid

# Interactive template selection
cirron init my-model
# Choose a framework: (Use arrow keys)
# ❯ PyTorch - PyTorch model with training and inference
#   TensorFlow - TensorFlow/Keras model with training and inference
#   Scikit-Learn - Scikit-learn model with preprocessing and inference

Git Initialization Fails

# Warning: Failed to initialize git repository
cirron init my-model --git

# Solution: Ensure git is installed and configured
git --version
git config --global user.name "Your Name"
git config --global user.email "your.email@example.com"

Dependency Installation Fails

# Warning: Failed to run: pip install -r requirements.txt
# Solution: Install manually
cd my-model
pip install -r requirements.txt

Best Practices

Project Naming

Use Descriptive Names: Choose names that describe the project purpose
Follow Conventions: Use lowercase with hyphens or underscores
Avoid Special Characters: Stick to letters, numbers, hyphens, and underscores

Template Selection

Choose Appropriate Framework: Select framework based on your needs
Consider Training Needs: Use training templates for full ML pipelines
Start Simple: Use basic templates for simple models

Development Workflow

Use Git: Initialize git repository for version control
Install Dependencies: Install dependencies immediately after creation
Test Early: Run tests to verify project setup
Document Changes: Update README with project-specific information

Troubleshooting

Common Issues

Permission Errors

# Error: EACCES: permission denied
# Solution: Check directory permissions
ls -la
chmod 755 my-model

Network Issues

# Error: Failed to register project with Cirron
# Solution: Check network connection and authentication
cirron auth status
ping app.cirron.com

Python Environment Issues

# Error: pip install failed
# Solution: Check Python environment
python --version
pip --version

Debugging Tips

Check Logs: Look for detailed error messages
Verify Dependencies: Ensure required tools are installed
Test Commands: Run commands manually to identify issues
Check Permissions: Verify file and directory permissions

cirron auth - Manage authentication
cirron test - Test your project
cirron build - Build your project
cirron deploy - Deploy your project

​cirron init

​Usage

​Arguments

​Options

​Templates

​PyTorch Templates

​TensorFlow Templates

​Scikit-Learn Templates

​Custom Template

​Interactive Mode

​Project Name

​Framework Selection

​Model Type

​Additional Options

​Examples

​Basic Usage

​Advanced Usage

​Project Structure

​Configuration File

​Model Type Configuration

​Default Model Class Names

​Default Architectures

​Default Input Shapes

​Git Integration

​Automatic Git Initialization

​Git Information

​Dependency Installation

​Automatic Installation

​Post-Install Commands

​Project Registration

​Cirron API Integration

​Authentication Check

​Next Steps

​Error Handling

​Directory Already Exists

​Invalid Project Name

​Invalid Template

​Git Initialization Fails

​Dependency Installation Fails

​Best Practices

​Project Naming

​Template Selection

​Development Workflow

​Troubleshooting

​Common Issues

​Permission Errors

​Network Issues

​Python Environment Issues

​Debugging Tips

​Related Commands

cirron init

Usage

Arguments

Options

Templates

PyTorch Templates

TensorFlow Templates

Scikit-Learn Templates

Custom Template

Interactive Mode

Project Name

Framework Selection

Model Type

Additional Options

Examples

Basic Usage

Advanced Usage

Project Structure

Configuration File

Model Type Configuration

Default Model Class Names

Default Architectures

Default Input Shapes

Git Integration

Automatic Git Initialization

Git Information

Dependency Installation

Automatic Installation

Post-Install Commands

Project Registration

Cirron API Integration

Authentication Check

Next Steps

Error Handling

Directory Already Exists

Invalid Project Name

Invalid Template

Git Initialization Fails

Dependency Installation Fails

Best Practices

Project Naming

Template Selection

Development Workflow

Troubleshooting

Common Issues

Permission Errors

Network Issues

Python Environment Issues

Debugging Tips

Related Commands