cirron build

Build ML models and Docker containers for your projects with intelligent project detection, validation, and registry configuration. Supports both ML model compilation and traditional application builds.

Usage

cirron build [options]

Options

Option	Description	Default
`--env, -e`	Environment (PRODUCTION, DEVELOPMENT, CUSTOM)	`DEVELOPMENT`
`--arch, -a`	Target architecture (`cpu`, `cuda`, `gpu`)	Auto-detected
`--index`	Path to index/manifest file	`null`
`--validate`	Run validation checks before build	`false`
`--strict`	Enable strict error handling	`false`
`--clean`	Clean output directory before build	`false`
`--watch, -w`	Run build in watch mode	`false`
`--analyze`	Analyze build output after completion	`false`
`--push`	Push Docker image to registry after build	`false`
`--tag, -t`	Tag for the Docker image	Auto-generated
`-f, --force`	Force build despite warnings	`false`
`-i, --interactive`	Step-by-step build confirmations	`false`

Build Types

ML model builds

For projects with ML frameworks (PyTorch, TensorFlow, Scikit-learn). The command detects the target architecture, validates files and dependencies, compiles the model for the target architecture, generates artifacts and metadata, then builds a Docker image (if a Dockerfile exists) and pushes it when --push is set.

cirron build                              # basic ML model build
cirron build --arch cuda --validate       # PyTorch CUDA build with validation
cirron build --arch gpu --index config/index.json   # TensorFlow GPU build
cirron build --arch cpu                   # Scikit-learn
cirron build --push --tag v1.0.0          # build and push

Architecture	Use case	Notes
`cpu`	All frameworks	Lightweight, CPU inference
`cuda`	PyTorch	Validates CUDA availability
`gpu`	TensorFlow	Validates GPU availability

Traditional application builds

For non-ML projects with build configuration. Reads settings from cirron.yaml, sets environment variables, runs beforeBuild commands, the main build command, then afterBuild commands. Use --analyze to inspect output.

cirron build --env DEVELOPMENT
cirron build --env PRODUCTION --clean --analyze
cirron build --watch                      # rebuild on change

Configuration

ML project:

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

Traditional project:

{
  "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"  } }
  }
}

Validation

With --validate:

ML projects: required files (src/model.py, requirements.txt), Python version compatibility, CUDA/GPU availability for GPU architectures, model instantiation, index file format.
Traditional projects: build configuration in cirron.yaml, command availability, dependencies, output directory writable.

Docker and Registry

Images are named registry/organization/project:tag. Configure with CIRRON_REGISTRY and CIRRON_ORG:

CIRRON_REGISTRY=localhost:5000
CIRRON_ORG=myorg

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

.cirronignore patterns are automatically merged into .dockerignore during build.

Environment Variables

Variable	Description	Example
`NODE_ENV`	Node environment	`production`
`CIRRON_ENV`	Cirron environment	`production`

Per-environment overrides live in cirron.yaml:

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

Build Output

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!

Build completed successfully!

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

Run cirron deploy to deploy this build

With --analyze:

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

Watch Mode

cirron build --watch     # Ctrl+C to stop

Troubleshooting

Error	Resolution
`No cirron.yaml found`	`cirron init my-project`
`Build command failed with exit code 1`	`cat cirron.yaml` to verify command; try `cirron build --clean`; reinstall deps
`CUDA not available for PyTorch`	Use `--arch cpu`, install CUDA, or verify with `nvidia-smi`
Docker build issues	`docker --version`, check disk space (`df -h`)
Registry push failures	`docker login`, verify image exists with `docker images`
Large/slow builds	Use `.dockerignore`, verify memory/disk capacity
Debugging	`CIRRON_VERBOSE=1`, run with `--validate` first

Best Practices and CI/CD

Run --validate for production builds.
Choose architecture to match the target deployment hardware.
Use index files for complex model configurations.
Use --clean and meaningful --tag values for production.
Use .cirronignore to keep build context lean.

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

# Local dev cycle
cirron build --watch
cirron build --env CUSTOM --validate
cirron build --env PRODUCTION --clean --push

Cirron CLI

Commands

Utils

Templates

build

cirron build

Usage

Options

Build Types

ML model builds

Traditional application builds

Configuration

Validation

Docker and Registry

Environment Variables

Build Output

Watch Mode

Troubleshooting

Best Practices and CI/CD

​cirron build

​Usage

​Options

​Build Types

​ML model builds

​Traditional application builds

​Configuration

​Validation

​Docker and Registry

​Environment Variables

​Build Output

​Watch Mode

​Troubleshooting

​Best Practices and CI/CD

cirron build

Usage

Options

Build Types

ML model builds

Traditional application builds

Configuration

Validation

Docker and Registry

Environment Variables

Build Output

Watch Mode

Troubleshooting

Best Practices and CI/CD