Auto-commit 2026-03-15 02:40

tasks/FRE-12.yaml

@@ -43,6 +43,12 @@ completion_notes: |
 
   Testing requires: docker-compose up -d redis
 
+  **Code Review Improvements (2026-03-15):**
+  - Fixed hardcoded subscriptionStatus="free" - now fetched from database via getUserSubscription()
+  - Fixed hardcoded demo user data in job completion/failure notifications
+  - Notifications now use actual user_id, email, and job data from database
+  - Added getUserEmailFromUserId() helper for fetching user emails
+
 review_notes: |
   Code review completed 2026-03-14 by Code Reviewer:
   - Found solid implementation with proper separation of concerns

tasks/FRE-17.yaml

@@ -3,7 +3,8 @@ date: 2026-03-08
 day_of_week: Sunday
 task_id: FRE-17
 title: Add Memory-Efficient Model Loading
-status: todo
+status: done
+completed_date: 2026-03-15
 company_id: FrenoCorp
 objective: Implement gradient checkpointing and mixed precision for lower VRAM usage
 context: |
@@ -28,6 +29,33 @@ acceptance_criteria:
 notes:
   - Use torch.cuda.amp for mixed precision
   - Set gradient_checkpointing=True in model config
+  - COMPLETED: Added memory-efficient model loading with auto-detection
+
+completion_notes: |
+  Completed 2026-03-15. Deliverables:
+  
+  **New Parameters:**
+  - `memory_efficient` (bool, default=True): Enable all memory-saving features
+  - `use_gradient_checkpointing` (bool, default=False): Trade compute for memory
+  - Enhanced `dtype` support with auto-selection based on available GPU memory
+  
+  **New Methods:**
+  - `_check_gpu_memory()`: Returns (total_gb, available_gb)
+  - `_select_optimal_dtype(available_gb)`: Auto-selects fp32/bf16/fp16
+  - `get_memory_stats()`: Returns dict with current GPU memory usage
+  - `estimate_model_memory()`: Returns estimated memory for different precisions
+  
+  **Features:**
+  - Auto-detects GPU memory and selects optimal dtype (bf16 for Ampere+, fp16 otherwise)
+  - Graceful degradation: fp32 → bf16 → fp16 based on available memory
+  - Enhanced OOM error messages with actionable suggestions
+  - Memory stats reported on load/unload
+  - Gradient checkpointing support for training scenarios
+  
+  **Memory Estimates:**
+  - FP32: ~6.8GB (1.7B params × 4 bytes + overhead)
+  - FP16/BF16: ~3.9GB (50% reduction)
+  - Minimum recommended: 4GB VRAM
 
 links:
   tts_model: /home/mike/code/AudiobookPipeline/src/generation/tts_model.py