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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cirron build

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Usage

cirron build [options]

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Options

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Build Types

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ML Model Builds

# Basic ML model build
cirron build

# Build with specific architecture
cirron build --arch cuda

# Build with validation
cirron build --validate

# Build and push Docker image
cirron build --push

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ML Build Process

1. Architecture Detection: Determines target architecture (CPU/CUDA/GPU)
2. Validation: Checks model files, dependencies, and environment
3. Model Compilation: Compiles model for target architecture
4. Artifact Generation: Creates model files and metadata
5. Integrity Testing: Verifies generated artifacts
6. Container Build: Builds Docker image if Dockerfile exists
7. Registry Push: Pushes image to registry if requested

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Traditional Application Builds

# Build for development
cirron build --env development

# Build for production
cirron build --env production --clean

# Build in watch mode
cirron build --watch

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Traditional Build Process

1. Configuration Loading: Reads build settings from cirron.yaml
2. Environment Setup: Sets environment variables
3. Pre-build Commands: Runs configured pre-build steps
4. Main Build: Executes the build command
5. Post-build Commands: Runs configured post-build steps
6. Analysis: Analyzes build output if requested

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Configuration

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ML Project Configuration

{
  "name": "my-ml-model",
  "version": "1.0.0",
  "framework": "pytorch",
  "pythonVersion": "3.9",
  "gpuRequired": true
}

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Traditional Project Configuration

{
  "name": "my-app",
  "version": "1.0.0",
  "build": {
    "command": "npm run build",
    "outputDir": "dist",
    "beforeBuild": [
      "npm install",
      "npm run lint"
    ],
    "afterBuild": [
      "npm run test"
    ]
  },
  "environments": {
    "development": {
      "variables": {
        "NODE_ENV": "development"
      }
    },
    "production": {
      "variables": {
        "NODE_ENV": "production"
      }
    }
  }
}

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Architecture Support

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CPU Architecture

cirron build --arch cpu

• Optimized for CPU inference
• Compatible with all frameworks
• Lightweight deployment

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CUDA Architecture (PyTorch)

cirron build --arch cuda

• GPU-accelerated inference for PyTorch
• Requires CUDA-compatible hardware
• Validates CUDA availability

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GPU Architecture (TensorFlow)

cirron build --arch gpu

• GPU-accelerated inference for TensorFlow
• Requires GPU-compatible hardware
• Validates GPU availability

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Examples

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ML Model Builds

# Basic PyTorch build
cirron build --arch cuda --validate

# TensorFlow build with custom index
cirron build --arch gpu --index config/index.json

# Scikit-learn build
cirron build --arch cpu

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Traditional Application Builds

# Development build
cirron build --env development

# Production build with analysis
cirron build --env production --clean --analyze

# Watch mode for development
cirron build --watch

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Docker Integration

# Build and push to registry
cirron build --push --tag v1.0.0

# Clean build with no cache
cirron build --clean --push

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Build Output

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ML Build Results

Build Results:
  • Architecture: cuda
  • Artifacts: 3 files generated
    - models/model_cuda.pth
    - artifacts/build_info.json
    - build/model_metadata.json

ML model build completed successfully!

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Traditional Build Results

Build completed successfully!

Output directory: dist
Build size: 2.1 MB
Files: 15
Environment: development

Run cirron deploy to deploy this build

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Build Analysis

Build Analysis
Total size: 2.1 MB
File count: 15

Largest files:
  main.js: 1.2 MB
  vendor.js: 800 KB
  styles.css: 100 KB

Recommendations:
  • Consider code splitting to reduce bundle size
  • Enable source maps for better debugging

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Environment Variables

{
  "environments": {
    "production": {
      "variables": {
        "API_URL": "https://api.production.com",
        "DEBUG": "false"
      }
    }
  }
}

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Docker Integration

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Image Naming

# Environment variables for registry configuration
CIRRON_REGISTRY=localhost:5000
CIRRON_ORG=myorg

# Generated image names
cirron build --env development
# Image: localhost:5000/myorg/my-project:latest

cirron build --env production --tag v1.0.0
# Image: localhost:5000/myorg/my-project:production-1.0.0

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Docker Ignore Integration

# .cirronignore patterns are automatically added to .dockerignore
cirron build --push

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Validation

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ML Model Validation

• Required Files: Checks for src/model.py and requirements.txt
• Python Version: Validates Python version compatibility
• GPU Requirements: Checks CUDA/GPU availability for GPU architectures
• Model Creation: Tests model instantiation
• Index Files: Validates index file format and content

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Traditional Build Validation

• Build Configuration: Verifies build settings in cirron.yaml
• Command Availability: Checks if build commands exist
• Dependencies: Validates required dependencies
• Output Directory: Ensures output directory is writable

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Watch Mode

# Start build in watch mode
cirron build --watch

# Press Ctrl+C to stop watching

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Error Handling

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Common Errors

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Missing Project Configuration

Error: No cirron.yaml found

cirron init my-project

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Build Command Failure

Error: Build command failed with exit code 1

# Check build command
cat cirron.yaml

# Try clean build
cirron build --clean

# Check dependencies
npm install

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GPU Validation Failure

Error: CUDA not available for PyTorch

# Use CPU architecture
cirron build --arch cpu

# Install CUDA drivers
# Check GPU availability
nvidia-smi

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Recovery Actions

1. Build Failures
   # Clean build
   cirron build --clean
   
   # Check build configuration
   cat cirron.yaml
   
2. Docker Build Issues
   # Check Docker installation
   docker --version
   
   # Check disk space
   df -h
   
3. Registry Push Issues
   # Check registry credentials
   docker login
   
   # Verify image exists
   docker images
   

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Integration

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CI/CD Pipeline

# GitHub Actions example
- name: Build ML Model
  run: |
    cirron build --arch cuda --validate --push
    cirron build --env production --clean --analyze

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Development Workflow

# Development
cirron build --watch

# Staging
cirron build --env staging --validate

# Production
cirron build --env production --clean --push

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Best Practices

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ML Model Builds

• Use Validation: Always run --validate for production builds
• Architecture Selection: Choose appropriate architecture for deployment
• Index Files: Use index files for complex model configurations
• Containerization: Include Dockerfile for deployment

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Traditional Builds

• Environment-Specific: Use different environments for different stages
• Clean Builds: Use --clean for production builds
• Build Analysis: Regular analysis helps optimize bundle size
• Watch Mode: Use watch mode for development efficiency

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Docker Best Practices

• Registry Configuration: Set up proper registry and organization
• Image Tagging: Use meaningful tags for version management
• Ignore Patterns: Configure .cirronignore for efficient builds
• Push Strategy: Push images after successful builds

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Troubleshooting

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Performance Issues

• Large Models: ML builds may take longer for large models
• Docker Builds: Use .dockerignore to exclude unnecessary files
• Network Issues: Check registry connectivity for push operations

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Build Failures

• Dependencies: Ensure all dependencies are installed
• Permissions: Check file and directory permissions
• Disk Space: Verify sufficient disk space for builds
• Memory: Large builds may require more memory

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Debugging Tips

• Verbose Mode: Set CIRRON_VERBOSE=1 for detailed output
• Dry Run: Use validation to check configuration before building
• Log Analysis: Check build logs for specific error messages
• Environment Check: Verify environment matches requirements