- 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.
