731 lines
28 KiB
Python
731 lines
28 KiB
Python
"""
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Training Operations API - BUSINESS logic
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Handles training job execution, metrics, and WebSocket live feed
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"""
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from fastapi import APIRouter, Depends, HTTPException, status, BackgroundTasks, Request, Path, WebSocket, WebSocketDisconnect
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from typing import List, Optional, Dict, Any
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import structlog
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import asyncio
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import json
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import datetime
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from shared.auth.access_control import require_user_role, admin_role_required, analytics_tier_required
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from shared.routing import RouteBuilder
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from shared.monitoring.decorators import track_execution_time
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from shared.monitoring.metrics import get_metrics_collector
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from shared.database.base import create_database_manager
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from datetime import datetime, timezone
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import uuid
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from app.services.training_service import EnhancedTrainingService
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from app.schemas.training import (
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TrainingJobRequest,
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SingleProductTrainingRequest,
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TrainingJobResponse
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)
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from app.services.messaging import (
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publish_job_progress,
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publish_data_validation_started,
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publish_data_validation_completed,
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publish_job_step_completed,
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publish_job_completed,
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publish_job_failed,
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publish_job_started,
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training_publisher
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)
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from app.core.config import settings
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logger = structlog.get_logger()
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route_builder = RouteBuilder('training')
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router = APIRouter(tags=["training-operations"])
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def get_enhanced_training_service():
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"""Dependency injection for EnhancedTrainingService"""
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database_manager = create_database_manager(settings.DATABASE_URL, "training-service")
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return EnhancedTrainingService(database_manager)
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@router.post(
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route_builder.build_base_route("jobs"), response_model=TrainingJobResponse)
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@track_execution_time("enhanced_training_job_duration_seconds", "training-service")
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async def start_training_job(
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request: TrainingJobRequest,
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tenant_id: str = Path(..., description="Tenant ID"),
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background_tasks: BackgroundTasks = BackgroundTasks(),
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request_obj: Request = None,
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enhanced_training_service: EnhancedTrainingService = Depends(get_enhanced_training_service)
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):
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"""
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Start a new training job for all tenant products using repository pattern.
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Enhanced immediate response pattern:
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1. Validate request with enhanced validation
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2. Create job record using repository pattern
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3. Return 200 with enhanced job details
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4. Execute enhanced training in background with repository tracking
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Enhanced features:
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- Repository pattern for data access
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- Enhanced error handling and logging
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- Metrics tracking and monitoring
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- Transactional operations
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"""
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metrics = get_metrics_collector(request_obj)
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try:
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# Generate enhanced job ID
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job_id = f"enhanced_training_{tenant_id}_{uuid.uuid4().hex[:8]}"
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logger.info("Creating enhanced training job using repository pattern",
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job_id=job_id,
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tenant_id=tenant_id)
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# Record job creation metrics
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if metrics:
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metrics.increment_counter("enhanced_training_jobs_created_total")
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# Add enhanced background task
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background_tasks.add_task(
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execute_training_job_background,
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tenant_id=tenant_id,
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job_id=job_id,
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bakery_location=(40.4168, -3.7038),
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requested_start=request.start_date,
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requested_end=request.end_date
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)
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# Return enhanced immediate success response
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response_data = {
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"job_id": job_id,
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"tenant_id": tenant_id,
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"status": "pending",
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"message": "Enhanced training job started successfully using repository pattern",
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"created_at": datetime.now(timezone.utc),
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"estimated_duration_minutes": 18,
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"training_results": {
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"total_products": 0,
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"successful_trainings": 0,
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"failed_trainings": 0,
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"products": [],
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"overall_training_time_seconds": 0.0
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},
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"data_summary": None,
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"completed_at": None,
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"error_details": None,
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"processing_metadata": {
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"background_task": True,
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"async_execution": True,
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"enhanced_features": True,
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"repository_pattern": True,
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"dependency_injection": True
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}
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}
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logger.info("Enhanced training job queued successfully",
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job_id=job_id,
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features=["repository-pattern", "dependency-injection", "enhanced-tracking"])
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return TrainingJobResponse(**response_data)
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except HTTPException:
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raise
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except ValueError as e:
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if metrics:
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metrics.increment_counter("enhanced_training_validation_errors_total")
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logger.error("Enhanced training job validation error",
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error=str(e),
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tenant_id=tenant_id)
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=str(e)
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)
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except Exception as e:
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if metrics:
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metrics.increment_counter("enhanced_training_job_errors_total")
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logger.error("Failed to queue enhanced training job",
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error=str(e),
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tenant_id=tenant_id)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="Failed to start enhanced training job"
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)
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async def execute_training_job_background(
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tenant_id: str,
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job_id: str,
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bakery_location: tuple,
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requested_start: Optional[datetime] = None,
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requested_end: Optional[datetime] = None
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):
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"""
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Enhanced background task that executes the training job using repository pattern.
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Enhanced features:
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- Repository pattern for all data operations
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- Enhanced error handling with structured logging
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- Transactional operations for data consistency
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- Comprehensive metrics tracking
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- Database connection pooling
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- Enhanced progress reporting
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"""
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logger.info("Enhanced background training job started",
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job_id=job_id,
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tenant_id=tenant_id,
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features=["repository-pattern", "enhanced-tracking"])
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# Get enhanced training service with dependency injection
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database_manager = create_database_manager(settings.DATABASE_URL, "training-service")
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enhanced_training_service = EnhancedTrainingService(database_manager)
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try:
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# Create initial training log entry first
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await enhanced_training_service._update_job_status_repository(
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job_id=job_id,
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status="pending",
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progress=0,
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current_step="Starting enhanced training job",
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tenant_id=tenant_id
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)
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# Publish job started event
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await publish_job_started(job_id, tenant_id, {
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"enhanced_features": True,
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"repository_pattern": True,
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"job_type": "enhanced_training"
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})
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training_config = {
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"job_id": job_id,
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"tenant_id": tenant_id,
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"bakery_location": {
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"latitude": bakery_location[0],
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"longitude": bakery_location[1]
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},
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"requested_start": requested_start.isoformat() if requested_start else None,
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"requested_end": requested_end.isoformat() if requested_end else None,
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"estimated_duration_minutes": 18,
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"background_execution": True,
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"enhanced_features": True,
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"repository_pattern": True,
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"api_version": "enhanced_v1"
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}
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# Update job status using repository pattern
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await enhanced_training_service._update_job_status_repository(
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job_id=job_id,
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status="running",
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progress=0,
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current_step="Initializing enhanced training pipeline",
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tenant_id=tenant_id
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)
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# Execute the enhanced training pipeline with repository pattern
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result = await enhanced_training_service.start_training_job(
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tenant_id=tenant_id,
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job_id=job_id,
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bakery_location=bakery_location,
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requested_start=requested_start,
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requested_end=requested_end
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)
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# Update final status using repository pattern
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await enhanced_training_service._update_job_status_repository(
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job_id=job_id,
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status="completed",
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progress=100,
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current_step="Enhanced training completed successfully",
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results=result,
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tenant_id=tenant_id
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)
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# Publish enhanced completion event
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await publish_job_completed(
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job_id=job_id,
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tenant_id=tenant_id,
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results={
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**result,
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"enhanced_features": True,
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"repository_integration": True
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}
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)
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logger.info("Enhanced background training job completed successfully",
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job_id=job_id,
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models_created=result.get('products_trained', 0),
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features=["repository-pattern", "enhanced-tracking"])
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except Exception as training_error:
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logger.error("Enhanced training pipeline failed",
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job_id=job_id,
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error=str(training_error))
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try:
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await enhanced_training_service._update_job_status_repository(
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job_id=job_id,
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status="failed",
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progress=0,
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current_step="Enhanced training failed",
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error_message=str(training_error),
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tenant_id=tenant_id
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)
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except Exception as status_error:
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logger.error("Failed to update job status after training error",
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job_id=job_id,
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status_error=str(status_error))
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# Publish enhanced failure event
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await publish_job_failed(
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job_id=job_id,
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tenant_id=tenant_id,
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error=str(training_error),
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metadata={
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"enhanced_features": True,
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"repository_pattern": True,
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"error_type": type(training_error).__name__
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}
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)
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except Exception as background_error:
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logger.error("Critical error in enhanced background training job",
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job_id=job_id,
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error=str(background_error))
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finally:
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logger.info("Enhanced background training job cleanup completed",
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job_id=job_id)
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@router.post(
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route_builder.build_resource_detail_route("products", "inventory_product_id"), response_model=TrainingJobResponse)
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@track_execution_time("enhanced_single_product_training_duration_seconds", "training-service")
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async def start_single_product_training(
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request: SingleProductTrainingRequest,
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tenant_id: str = Path(..., description="Tenant ID"),
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inventory_product_id: str = Path(..., description="Inventory product UUID"),
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request_obj: Request = None,
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enhanced_training_service: EnhancedTrainingService = Depends(get_enhanced_training_service)
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):
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"""
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Start enhanced training for a single product using repository pattern.
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Enhanced features:
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- Repository pattern for data access
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- Enhanced error handling and validation
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- Metrics tracking
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- Transactional operations
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"""
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metrics = get_metrics_collector(request_obj)
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try:
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logger.info("Starting enhanced single product training",
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inventory_product_id=inventory_product_id,
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tenant_id=tenant_id)
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# Record metrics
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if metrics:
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metrics.increment_counter("enhanced_single_product_training_total")
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# Generate enhanced job ID
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job_id = f"enhanced_single_{tenant_id}_{inventory_product_id}_{uuid.uuid4().hex[:8]}"
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# Delegate to enhanced training service
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result = await enhanced_training_service.start_single_product_training(
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tenant_id=tenant_id,
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inventory_product_id=inventory_product_id,
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job_id=job_id,
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bakery_location=request.bakery_location or (40.4168, -3.7038)
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)
|
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if metrics:
|
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metrics.increment_counter("enhanced_single_product_training_success_total")
|
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logger.info("Enhanced single product training completed",
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inventory_product_id=inventory_product_id,
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job_id=job_id)
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return TrainingJobResponse(**result)
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except ValueError as e:
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if metrics:
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metrics.increment_counter("enhanced_single_product_validation_errors_total")
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logger.error("Enhanced single product training validation error",
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error=str(e),
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inventory_product_id=inventory_product_id)
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raise HTTPException(
|
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=str(e)
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)
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except Exception as e:
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if metrics:
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metrics.increment_counter("enhanced_single_product_training_errors_total")
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logger.error("Enhanced single product training failed",
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error=str(e),
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inventory_product_id=inventory_product_id)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
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detail="Enhanced single product training failed"
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)
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|
|
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# ============================================
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# WebSocket Live Feed
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# ============================================
|
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class ConnectionManager:
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"""Manage WebSocket connections for training progress"""
|
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def __init__(self):
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self.active_connections: Dict[str, Dict[str, WebSocket]] = {}
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# Structure: {job_id: {connection_id: websocket}}
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async def connect(self, websocket: WebSocket, job_id: str, connection_id: str):
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"""Accept WebSocket connection and register it"""
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await websocket.accept()
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if job_id not in self.active_connections:
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self.active_connections[job_id] = {}
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self.active_connections[job_id][connection_id] = websocket
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logger.info(f"WebSocket connected for job {job_id}, connection {connection_id}")
|
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def disconnect(self, job_id: str, connection_id: str):
|
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"""Remove WebSocket connection"""
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if job_id in self.active_connections:
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self.active_connections[job_id].pop(connection_id, None)
|
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if not self.active_connections[job_id]:
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del self.active_connections[job_id]
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logger.info(f"WebSocket disconnected for job {job_id}, connection {connection_id}")
|
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async def send_to_job(self, job_id: str, message: dict):
|
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"""Send message to all connections for a specific job with better error handling"""
|
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if job_id not in self.active_connections:
|
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logger.debug(f"No active connections for job {job_id}")
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return
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|
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# Send to all connections for this job
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disconnected_connections = []
|
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|
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for connection_id, websocket in self.active_connections[job_id].items():
|
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try:
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await websocket.send_json(message)
|
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logger.debug(f"Sent {message.get('type', 'unknown')} to connection {connection_id}")
|
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except Exception as e:
|
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logger.warning(f"Failed to send message to connection {connection_id}: {e}")
|
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disconnected_connections.append(connection_id)
|
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|
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# Clean up disconnected connections
|
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for connection_id in disconnected_connections:
|
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self.disconnect(job_id, connection_id)
|
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|
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# Log successful sends
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active_count = len(self.active_connections.get(job_id, {}))
|
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if active_count > 0:
|
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logger.info(f"Sent {message.get('type', 'unknown')} message to {active_count} connection(s) for job {job_id}")
|
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|
|
|
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# Global connection manager
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connection_manager = ConnectionManager()
|
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|
|
|
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@router.websocket(route_builder.build_nested_resource_route('jobs', 'job_id', 'live'))
|
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async def training_progress_websocket(
|
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websocket: WebSocket,
|
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tenant_id: str,
|
|
job_id: str
|
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):
|
|
"""
|
|
WebSocket endpoint for real-time training progress updates
|
|
"""
|
|
# Validate token from query parameters
|
|
token = websocket.query_params.get("token")
|
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if not token:
|
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logger.warning(f"WebSocket connection rejected - missing token for job {job_id}")
|
|
await websocket.close(code=1008, reason="Authentication token required")
|
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return
|
|
|
|
# Validate the token
|
|
from shared.auth.jwt_handler import JWTHandler
|
|
|
|
jwt_handler = JWTHandler(settings.JWT_SECRET_KEY, settings.JWT_ALGORITHM)
|
|
|
|
try:
|
|
payload = jwt_handler.verify_token(token)
|
|
if not payload:
|
|
logger.warning(f"WebSocket connection rejected - invalid token for job {job_id}")
|
|
await websocket.close(code=1008, reason="Invalid authentication token")
|
|
return
|
|
|
|
# Verify user has access to this tenant
|
|
user_id = payload.get('user_id')
|
|
if not user_id:
|
|
logger.warning(f"WebSocket connection rejected - no user_id in token for job {job_id}")
|
|
await websocket.close(code=1008, reason="Invalid token payload")
|
|
return
|
|
|
|
logger.info(f"WebSocket authenticated for user {payload.get('email', 'unknown')} on job {job_id}")
|
|
|
|
except Exception as e:
|
|
logger.warning(f"WebSocket token validation failed for job {job_id}: {e}")
|
|
await websocket.close(code=1008, reason="Token validation failed")
|
|
return
|
|
|
|
connection_id = f"{tenant_id}_{user_id}_{id(websocket)}"
|
|
|
|
await connection_manager.connect(websocket, job_id, connection_id)
|
|
logger.info(f"WebSocket connection established for job {job_id}, user {user_id}")
|
|
|
|
consumer_task = None
|
|
training_completed = False
|
|
|
|
try:
|
|
# Start RabbitMQ consumer
|
|
consumer_task = asyncio.create_task(
|
|
setup_rabbitmq_consumer_for_job(job_id, tenant_id)
|
|
)
|
|
|
|
last_activity = asyncio.get_event_loop().time()
|
|
|
|
while not training_completed:
|
|
try:
|
|
try:
|
|
data = await asyncio.wait_for(websocket.receive(), timeout=60.0)
|
|
last_activity = asyncio.get_event_loop().time()
|
|
|
|
# Handle different message types
|
|
if data["type"] == "websocket.receive":
|
|
if "text" in data:
|
|
message_text = data["text"]
|
|
if message_text == "ping":
|
|
await websocket.send_text("pong")
|
|
logger.debug(f"Text ping received from job {job_id}")
|
|
elif message_text == "get_status":
|
|
current_status = await get_current_job_status(job_id, tenant_id)
|
|
if current_status:
|
|
await websocket.send_json({
|
|
"type": "current_status",
|
|
"job_id": job_id,
|
|
"data": current_status
|
|
})
|
|
elif message_text == "close":
|
|
logger.info(f"Client requested connection close for job {job_id}")
|
|
break
|
|
|
|
elif "bytes" in data:
|
|
await websocket.send_text("pong")
|
|
logger.debug(f"Binary ping received for job {job_id}, responding with text pong")
|
|
|
|
elif data["type"] == "websocket.disconnect":
|
|
logger.info(f"WebSocket disconnect message received for job {job_id}")
|
|
break
|
|
|
|
except asyncio.TimeoutError:
|
|
current_time = asyncio.get_event_loop().time()
|
|
|
|
if current_time - last_activity > 90:
|
|
logger.warning(f"No frontend activity for 90s on job {job_id}, sending training service heartbeat")
|
|
|
|
try:
|
|
await websocket.send_json({
|
|
"type": "heartbeat",
|
|
"job_id": job_id,
|
|
"timestamp": str(datetime.now()),
|
|
"message": "Training service heartbeat - frontend inactive",
|
|
"inactivity_seconds": int(current_time - last_activity)
|
|
})
|
|
last_activity = current_time
|
|
except Exception as e:
|
|
logger.error(f"Failed to send heartbeat for job {job_id}: {e}")
|
|
break
|
|
else:
|
|
logger.debug(f"Normal 60s timeout for job {job_id}, continuing (last activity: {int(current_time - last_activity)}s ago)")
|
|
continue
|
|
|
|
except WebSocketDisconnect:
|
|
logger.info(f"WebSocket client disconnected for job {job_id}")
|
|
break
|
|
except Exception as e:
|
|
logger.error(f"WebSocket error for job {job_id}: {e}")
|
|
if "Cannot call" in str(e) and "disconnect message" in str(e):
|
|
logger.error(f"FastAPI WebSocket disconnect error - connection already closed")
|
|
break
|
|
await asyncio.sleep(1)
|
|
|
|
logger.info(f"WebSocket loop ended for job {job_id}, training_completed: {training_completed}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Critical WebSocket error for job {job_id}: {e}")
|
|
|
|
finally:
|
|
logger.info(f"Cleaning up WebSocket connection for job {job_id}")
|
|
connection_manager.disconnect(job_id, connection_id)
|
|
|
|
if consumer_task and not consumer_task.done():
|
|
if training_completed:
|
|
logger.info(f"Training completed, cancelling consumer for job {job_id}")
|
|
consumer_task.cancel()
|
|
else:
|
|
logger.warning(f"WebSocket disconnected but training not completed for job {job_id}")
|
|
|
|
try:
|
|
await consumer_task
|
|
except asyncio.CancelledError:
|
|
logger.info(f"Consumer task cancelled for job {job_id}")
|
|
except Exception as e:
|
|
logger.error(f"Consumer task error for job {job_id}: {e}")
|
|
|
|
|
|
async def setup_rabbitmq_consumer_for_job(job_id: str, tenant_id: str):
|
|
"""Set up RabbitMQ consumer to listen for training events for a specific job"""
|
|
|
|
logger.info(f"Setting up RabbitMQ consumer for job {job_id}")
|
|
|
|
try:
|
|
# Create a unique queue for this WebSocket connection
|
|
queue_name = f"websocket_training_{job_id}_{tenant_id}"
|
|
|
|
async def handle_training_message(message):
|
|
"""Handle incoming RabbitMQ messages and forward to WebSocket"""
|
|
try:
|
|
# Parse the message
|
|
body = message.body.decode()
|
|
data = json.loads(body)
|
|
|
|
logger.debug(f"Received message for job {job_id}: {data.get('event_type', 'unknown')}")
|
|
|
|
# Extract event data
|
|
event_type = data.get("event_type", "unknown")
|
|
event_data = data.get("data", {})
|
|
|
|
# Only process messages for this specific job
|
|
message_job_id = event_data.get("job_id") if event_data else None
|
|
if message_job_id != job_id:
|
|
logger.debug(f"Ignoring message for different job: {message_job_id}")
|
|
await message.ack()
|
|
return
|
|
|
|
# Transform RabbitMQ message to WebSocket message format
|
|
websocket_message = {
|
|
"type": map_event_type_to_websocket_type(event_type),
|
|
"job_id": job_id,
|
|
"timestamp": data.get("timestamp"),
|
|
"data": event_data
|
|
}
|
|
|
|
logger.info(f"Forwarding {event_type} message to WebSocket clients for job {job_id}")
|
|
|
|
# Send to all WebSocket connections for this job
|
|
await connection_manager.send_to_job(job_id, websocket_message)
|
|
|
|
# Check if this is a completion message
|
|
if event_type in ["training.completed", "training.failed"]:
|
|
logger.info(f"Training completion detected for job {job_id}: {event_type}")
|
|
|
|
# Acknowledge the message
|
|
await message.ack()
|
|
|
|
logger.debug(f"Successfully processed {event_type} for job {job_id}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error handling training message for job {job_id}: {e}")
|
|
import traceback
|
|
logger.error(f"Traceback: {traceback.format_exc()}")
|
|
await message.nack(requeue=False)
|
|
|
|
# Check if training_publisher is connected
|
|
if not training_publisher.connected:
|
|
logger.warning(f"Training publisher not connected for job {job_id}, attempting to connect...")
|
|
success = await training_publisher.connect()
|
|
if not success:
|
|
logger.error(f"Failed to connect training_publisher for job {job_id}")
|
|
return
|
|
|
|
# Subscribe to training events
|
|
logger.info(f"Subscribing to training events for job {job_id}")
|
|
success = await training_publisher.consume_events(
|
|
exchange_name="training.events",
|
|
queue_name=queue_name,
|
|
routing_key="training.*",
|
|
callback=handle_training_message
|
|
)
|
|
|
|
if success:
|
|
logger.info(f"Successfully set up RabbitMQ consumer for job {job_id} (queue: {queue_name})")
|
|
|
|
# Keep the consumer running indefinitely until cancelled
|
|
try:
|
|
while True:
|
|
await asyncio.sleep(10)
|
|
logger.debug(f"Consumer heartbeat for job {job_id}")
|
|
|
|
except asyncio.CancelledError:
|
|
logger.info(f"Consumer cancelled for job {job_id}")
|
|
raise
|
|
except Exception as e:
|
|
logger.error(f"Consumer error for job {job_id}: {e}")
|
|
raise
|
|
else:
|
|
logger.error(f"Failed to set up RabbitMQ consumer for job {job_id}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Exception in setup_rabbitmq_consumer_for_job for job {job_id}: {e}")
|
|
import traceback
|
|
logger.error(f"Traceback: {traceback.format_exc()}")
|
|
|
|
|
|
def map_event_type_to_websocket_type(rabbitmq_event_type: str) -> str:
|
|
"""Map RabbitMQ event types to WebSocket message types"""
|
|
mapping = {
|
|
"training.started": "started",
|
|
"training.progress": "progress",
|
|
"training.completed": "completed",
|
|
"training.failed": "failed",
|
|
"training.cancelled": "cancelled",
|
|
"training.step.completed": "step_completed",
|
|
"training.product.started": "product_started",
|
|
"training.product.completed": "product_completed",
|
|
"training.product.failed": "product_failed",
|
|
"training.model.trained": "model_trained",
|
|
"training.data.validation.started": "validation_started",
|
|
"training.data.validation.completed": "validation_completed"
|
|
}
|
|
|
|
return mapping.get(rabbitmq_event_type, "unknown")
|
|
|
|
|
|
async def get_current_job_status(job_id: str, tenant_id: str) -> Dict[str, Any]:
|
|
"""Get current job status from database"""
|
|
try:
|
|
return {
|
|
"job_id": job_id,
|
|
"status": "running",
|
|
"progress": 0,
|
|
"current_step": "Starting...",
|
|
"started_at": "2025-07-30T19:00:00Z"
|
|
}
|
|
except Exception as e:
|
|
logger.error(f"Failed to get current job status: {e}")
|
|
return None
|
|
|
|
|
|
@router.get("/health")
|
|
async def health_check():
|
|
"""Health check endpoint for the training operations"""
|
|
return {
|
|
"status": "healthy",
|
|
"service": "training-operations",
|
|
"version": "2.0.0",
|
|
"features": [
|
|
"repository-pattern",
|
|
"dependency-injection",
|
|
"enhanced-error-handling",
|
|
"metrics-tracking",
|
|
"transactional-operations",
|
|
"websocket-support"
|
|
],
|
|
"timestamp": datetime.now().isoformat()
|
|
}
|