- Move ci-test.sh and setup.sh to scripts/
- Trim docs/src/zig-cli.md to current structure
- Replace hardcoded secrets with placeholders in configs
- Update .gitignore to block .env*, secrets/, keys, build artifacts
- Slim README.md to reflect current CLI/TUI split
- Add cleanup trap to ci-test.sh
- Ensure no secrets are committed
- Add jupyter.zig command with start/stop/status actions
- Update main.zig to include jupyter command in CLI
- CLI now handles all Jupyter setup transparently
- Data scientists only need: ml jupyter start
- Auto-detects container runtime (Podman/Docker)
- Manages container lifecycle automatically
- Provides clear status and error messages
- Add jupyter_launcher.sh script to start Jupyter with ML tools
- Create cli_integration.py helper for CLI operations
- Add sample notebook structure for experiments
- Create workflow documentation for seamless data science integration
- Remove redundant requirements file
- Test and verify Jupyter notebook 7.5.0 works
- ML tools container successfully built with all tools
- All 6 ML tools (MLflow, WandB, Streamlit, Dash, Panel, Bokeh) working
- Remove 14 duplicate README files from test fixtures
- Clean up and restructure docs/src/testing.md with comprehensive testing guide
- Update main README.md to highlight testing and reference docs/ structure
- Remove empty .new files
- Keep only valuable, directory-specific README files (reduced from 34 to 20)
- Update deployment.md to reference new deployments/ directory structure
- Update CLI reference with new multi-user authentication system
- Add roles and permissions examples to configuration
- Fix docker-compose paths in testing documentation
- Remove references to non-existent docker-compose.test.yml
- Update troubleshooting with correct test commands
- Remove misplaced README files from test directories
- Move docker-compose.prod.yml and docker-compose.homelab-secure.yml to deployments/
- Create deployments/README.md with usage instructions
- Update test scripts to use new deployment paths
- Fix performance regression detection to output to tests/bin/
- All test outputs now properly organized in tests/bin/
- Move compiled test binaries to tests/binaries/
- Move profiling output files to tests/binaries/
- Add README.md for test binaries documentation
- Project root now clean of test artifacts
- Fix CLI WebSocket port (9101 vs 9103) in both status and authenticateUser
- Add researcher_user and analyst_user to server config with proper permissions
- Fix API key hashes for all users (complete 64-char SHA256)
- Enable IP whitelist with localhost and private network ranges
- Fix memory leaks in WebSocket handshake (proper key cleanup)
- Fix binary character display in server responses
- All authentication tests now pass: admin, researcher, analyst
Status: Multi-user authentication fully functional
- Add 10ms delay after successful WebSocket handshake
- Use std.posix.nanosleep for proper timing
- This should prevent 'client sent data before handshake complete' error
- CLI WebSocket implementation improved but needs server testing
Status: WebSocket handshake timing improved, ready for testing
- Add Redis configuration to local config
- Fix API key format (api_keys vs apikeys)
- Update CLI to use port 9101
- Disable IP whitelist for testing
- Server now connects to Redis and authenticates
- WebSocket connection reaches server but handshake fails
- CLI needs WebSocket protocol implementation fix
Status: Server running, auth working, WebSocket handshake needs debugging
- Add configuration reference with all options and examples
- Add quick start testing guide for 5-minute setup
- Document all environment and worker configurations
- Add CLI configuration examples for all user roles
- Include permission system documentation
- Add troubleshooting guides and validation commands
- Complete testing framework with multiple documentation levels
Documentation now includes:
- Quick start testing guide (5-minute setup)
- Comprehensive configuration reference
- Testing protocol and guide
- Troubleshooting and validation
- Permission system reference
- Environment variable documentation
- 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
