- Add cleanup.sh script with dry-run, force, and all options
- Add auto-cleanup service setup for macOS (launchd) and Linux (systemd)
- Add cleanup-status.sh for monitoring Docker resources
- Add Makefile targets: self-cleanup, auto-cleanup
- Features colored output, confirmation prompts, and detailed logging
- Auto-cleanup runs daily to keep system clean
- Status monitoring shows resources and service state
- Fix YAML tags in auth config struct (json -> yaml)
- Update CLI configs to use pre-hashed API keys
- Remove double hashing in WebSocket client
- Fix port mapping (9102 -> 9103) in CLI commands
- Update permission keys to use jobs:read, jobs:create, etc.
- Clean up all debug logging from CLI and server
- All user roles now authenticate correctly:
* Admin: Can queue jobs and see all jobs
* Researcher: Can queue jobs and see own jobs
* Analyst: Can see status (read-only access)
Multi-user authentication is now fully functional.
- Change FETCH_ML_ENV check from 'production' to 'prod'
- Aligns with common environment naming conventions
- Fixes authentication validation for production deployment
- Add detailed development setup instructions
- Include debugging, testing, and performance monitoring guides
- Provide IDE recommendations without enforcing specific configurations
- Make tooling optional and developer-friendly
- Add database management and configuration guidance
Creates welcoming development environment that respects developer preferences
while providing comprehensive guidance for contributing to FetchML platform.
- Add SQLite WAL and SHM file patterns to gitignore
- Include main database files to prevent accidental commits
- Ensures database state doesn't get committed to version control
- Add comprehensive CI/CD workflows for testing and releases
- Include issue and pull request templates
- Add GitHub labeler configuration for automated triage
- Include license check and stale issue management
- Add Windsurf rules for development workflow
- Include database directory structure with gitkeep
Provides complete GitHub automation and development tooling
for streamlined contribution and project management.
- Add end-to-end tests for complete workflow validation
- Include integration tests for API and database interactions
- Add unit tests for all major components and utilities
- Include performance tests for payload handling
- Add CLI API integration tests
- Include Podman container integration tests
- Add WebSocket and queue execution tests
- Include shell script tests for setup validation
Provides comprehensive test coverage ensuring platform reliability
and functionality across all components and interactions.
- Add production setup scripts for automated deployment
- Include monitoring setup and configuration validation
- Add legacy setup scripts for various Linux distributions
- Implement Bitwarden integration for secure credential management
- Add development and production environment setup
- Include comprehensive management tools and utilities
- Add shell script library with common functions
Provides complete automation for setup, deployment, and management
of FetchML platform in development and production environments.
- Add complete API documentation and architecture guides
- Include quick start, installation, and deployment guides
- Add troubleshooting and security documentation
- Include CLI reference and configuration schema docs
- Add production monitoring and operations guides
- Implement MkDocs configuration with search functionality
- Include comprehensive user and developer documentation
Provides complete documentation for users and developers
covering all aspects of the FetchML platform.
- Add Prometheus, Grafana, and Loki monitoring stack
- Include pre-configured dashboards for ML metrics and logs
- Add Podman container support with security policies
- Implement ML runtime environments for multiple frameworks
- Add containerized ML project templates (PyTorch, TensorFlow, etc.)
- Include secure runner with isolation and resource limits
- Add comprehensive log aggregation and alerting
- Add development and production configuration templates
- Include Docker build files for containerized deployment
- Add Nginx configuration with SSL/TLS setup
- Include environment configuration examples
- Add SSL certificate setup and management
- Configure application schemas and validation
- Support for both local and production deployment scenarios
Provides flexible deployment options from development to production
with proper security, monitoring, and configuration management.
- Add modern CLI interface built with Zig for performance
- Include TUI (Terminal User Interface) with bubbletea-like features
- Implement ML experiment commands (run, status, manage)
- Add configuration management and validation
- Include shell completion scripts for bash and zsh
- Add comprehensive CLI testing framework
- Support for multiple ML frameworks and project types
CLI provides fast, efficient interface for ML experiment management
with modern terminal UI and comprehensive feature set.
- Add API server with WebSocket support and REST endpoints
- Implement authentication system with API keys and permissions
- Add task queue system with Redis backend and error handling
- Include storage layer with database migrations and schemas
- Add comprehensive logging, metrics, and telemetry
- Implement security middleware and network utilities
- Add experiment management and container orchestration
- Include configuration management with smart defaults
- Add comprehensive README with architecture overview and quick start guide
- Set up Go module with production-ready dependencies
- Configure build system with Makefile for development and production builds
- Add Docker Compose for local development environment
- Include project configuration files (linting, Python, etc.)
This establishes the foundation for a production-ready ML experiment platform
with task queuing, monitoring, and modern CLI/API interface.
