Phase 2: Deterministic Manifests
- Add manifest.Validator with required field checking
- Support Validate() and ValidateStrict() modes
- Integrate validation into worker executor before execution
- Block execution if manifest missing commit_id or deps_manifest_sha256
Phase 5: Pinned Dependencies
- Add hermetic.dockerfile template with pinned system deps
- Frozen package versions: libblas3, libcudnn8, etc.
- Support for deps_manifest.json and requirements.txt with hashes
- Image tagging strategy: deps-<first-8-of-sha256>
Phase 8: Tests as Specifications
- Add queue_spec_test.go with executable scheduler specs
- Document priority ordering (higher first)
- Document FIFO tiebreaker for same priority
- Test cases for negative/zero priorities
Phase 10: Local Dev Parity
- Create root-level docker-compose.dev.yml
- Simplified from deployments/ for quick local dev
- Redis + API server + Worker with hot reload volumes
- Debug ports: 9101 (API), 6379 (Redis)
- 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
- 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
