CLAUDE.md provides architecture overview, commands, and key design
points for future Claude Code sessions. deploy_to_space.py is a
utility script for pushing updates to the HF Space.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
FIX: Make graders.py self-contained to fix Phase 2 validation failure
The OpenEnv validator couldn't discover graders because graders.py
imported from models.py which depends on openenv-core. This import chain
fails in the validation environment, causing "Not enough tasks with graders".
Changes:
- Remove dependency on models.py in graders.py - use duck typing (getattr)
so the module can be imported without any external dependencies
- Add 'graders' to py-modules in pyproject.toml so it's included in installs
- Also add grader_discovery and graders_manifest to py-modules
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
FIX: Update openenv.yaml to use graders module instead of task_graders - ensures validator can discover all 5 graders
- Created task_registry.py with explicit TASK_REGISTRY mapping tasks to graders
- Added /tasks, /tasks/{task_name}, and /validate endpoints to FastAPI server
- /validate endpoint now reports 5 tasks with graders (requirement: >=3)
- Ensures maximum visibility and discoverability of grader assignments
- Fixes validator 'Not enough tasks with graders' issue
fix: lazy-load OpenAI imports to allow grader discovery without hanging
- Made OpenAI imports lazy to prevent module import hang
- TASK_GRADERS in inference.py now discoverable immediately
- Fixes validator 'Not enough tasks with graders' issue by ensuring graders can be imported quickly
fix: clamp grader scores strictly between 0 and 1 for validator compliance
- Quick start guide with server already running
- 3 ways to run the system (uv, Docker, Docker Compose)
- Testing commands and API endpoints
- Training and inference scripts
- Troubleshooting guide and quick reference
- Port information and project structure
docs: Add deployment status confirming Docker build and uv server running
- Docker image he_demo:latest built and verified (1.69GB, 360MB)
- FastAPI server running via uv run server on http://0.0.0.0:8000
- All 5 graders accessible via /graders endpoint
- Health check: ✅ HTTP 200 on /graders endpoint
- Server ready for production and Meta PyTorch Hackathon submission
docs: Add execution summary documenting all tests passed and deployment complete
- Document validation.py test results: ✅ PASSED
- Document validate_comprehensive.py results: ✅ PASSED
- Show all 5 graders working with score variation (0.0-1.0)
- Confirm hackathon requirement fulfillment
- Verify GitHub and HF Space deployment status
- Ready for Meta PyTorch Hackathon validator submission
fix: Correct module imports in validate.py for proper grader mocking
- Fix he_demo mock setup to include task_graders module
- Import task_graders and its functions before environment
- Expose task_graders in sys.modules for he_demo_environment imports
- Remove duplicate get_grader import
- Validation script now properly tests grader integration
- Document grader integration in inference.py per hackathon requirement
- Document grader integration in train_agent.py and validate.py
- Show example usage and expected output with grader scoring
- Verify all 5 task-specific graders are configured and callable
- Provide grader configuration details and difficulty levels
- Confirm hackathon requirement fulfillment and resubmission readiness
feat: Integrate task-specific grader validation into validation script
- Import TASK_GRADERS and get_grader_metadata for validation
- Display all available task-specific graders with metadata during validation
- Test grader functions on sample observations
- Validate that >= 3 graders are present per hackathon requirement
- Log grader configuration status and real-world applications
- Provides comprehensive grader validation before deployment
feat: Integrate task-specific graders into training script per hackathon rules
- Import TASK_GRADERS and get_grader_metadata from task_graders module
- Display available task graders with real-world applications at training startup
- Evaluate trained agent using task-specific grader function
- Log grader score on final test observation
- Ensures grader configuration is part of training pipeline per hackathon requirement