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Apr 12 11:45 Fix: Entry point should reference main function for multi-mode deployment 11:32 Fix: Export app from server package for entry point resolution 10:20 refactor: revert to root-level package structure with proper imports and hacky pyproject.toml setup 09:48 refactor: proper package structure - move he_demo package to dedicated folder This refactoring fixes the hacky pyproject.toml setup where the root was acting as the package.
Changes:
- Created he_demo/ subfolder with proper Python package structure
- Moved core files: __init__.py, client.py, models.py, task_graders.py, task_registry.py, grader_manifest.py
- Moved server/ to he_demo/server/ with all files (__init__.py, app.py, he_demo_environment.py)
- Updated pyproject.toml:
* Removed hacky package-dir = {he_demo: ., he_demo.server: server}
* Kept clean: packages = [he_demo, he_demo.server]
* Removed py-modules = [graders]
- Updated all root-level scripts to use proper imports:
* validate_comprehensive.py: server.he_demo_environment → he_demo.server.he_demo_environment
* gym_wrapper.py: server.he_demo_environment → he_demo.server.he_demo_environment
* test_environment.py: server.he_demo_environment → he_demo.server.he_demo_environment
* validate.py: server.he_demo_environment → he_demo.server.he_demo_environment
Benefits:
✓ Proper Python package … 08:59 feat: comprehensive LLM inference evaluation and validation - Created evaluate_inference.py with 300+ lines of evaluation framework
* EvaluationMetrics class for tracking step-by-step performance
* run_random_actions_baseline() for random agent benchmarking
* run_heuristic_agent() for rule-based agent evaluation
* Detailed metrics collection and JSON export
- Created EVALUATION_GUIDE.md with 400+ line comprehensive guide
* Task difficulty matrix with explicit targets
* Grader scoring explained (0.001-0.999 scale)
* Performance baselines and interpretation
* Complete execution instructions
- Created grader_manifest.py for explicit grader discovery
* GRADERS_MANIFEST structure with all 5 tasks
* Discovery endpoints for validator tools
* Validation helpers
- Updated server/app.py
* Increased max_concurrent_envs from 1 to 10
* Added task discovery endpoints
* Added grader manifest endpoints
Benchmark Results:
- Baseline (Random Agent): 1.737 total reward, 0.347 avg/step
- Heuristic Agent: 2.080 total reward, 0.999 gr… 07:39 feat: add explicit task registry and validation endpoints - 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 05:14 feat: remove bias - implement fair, linear reward system (like echo environment) Apr 11 18:52 feat: normalize rewards to 0-1 scale for consistency with grader spec 15:07 Fix: Make task graders explicitly discoverable with proper metadata and endpoints CHANGES:
- Created task_graders.py with 3 explicit graders with detailed scoring logic
* Task 1: Basic RAM Reduction (Difficulty 1, Easy)
* Task 2: Energy Optimization (Difficulty 2, Medium)
* Task 3: Balanced Optimization (Difficulty 3, Hard)
- Each grader returns different scores (0.0-1.0) based on:
* Real system performance metrics (RAM %, Energy kWh, Steps)
* Detailed scoring methodology with explanations
* Real-world application context
* Performance examples showing score variation
- Created graders.json manifest for easy tool discovery
- Created graders_manifest.py with programmatic validation functions
- Added FastAPI endpoints (/graders, /graders/info, /graders/{task_name})
- Updated environment.py with grader methods and properties
- Created comprehensive validation script with performance examples
REAL-WORLD APPLICATION:
- Memory & energy optimization for data centers
- Cloud infrastructure resource management
- Edge computing and IoT device optimization
- P… Apr 9 14:07 Add complete Energy & Memory RAM Optimization RL Environment - Full OpenEnv implementation with typed models
- 5 difficulty levels with agent graders (0.0-1.0 scores)
- Meaningful reward function with partial progress signals
- Baseline inference script with reproducible scoring
- Docker deployment ready
- Comprehensive README with environment details 
