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대시보드 api 노출 수정

This commit is contained in:
vast 2025-09-30 14:18:59 +00:00
parent 9b7befdaba
commit 7b64e74f2e
11 changed files with 1309 additions and 784 deletions
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8
app/monitoring/dashboard.py
View File

@ -1282,7 +1282,13 @@ HTML_TEMPLATE = """
return;
}
Object.entries(endpointStats).forEach(([endpoint, stats]) => {
// 불필요한 엔드포인트 필터링 (헬스/문서/자체 통계)
const excludedExact = new Set(['/api/v1/health', '/docs', '/openapi.json', '/redoc']);
const excludedPrefixes = ['/api/v1/stats'];
Object.entries(endpointStats)
.filter(([endpoint]) => !excludedExact.has(endpoint) && !excludedPrefixes.some(p => endpoint.startsWith(p)))
.forEach(([endpoint, stats]) => {
const item = document.createElement('div');
item.className = 'endpoint-item';

309
logs/main.log
View File

@ -940,3 +940,312 @@
2025-09-30 13:54:43,092 - main - INFO - ✅ 배치 관리자 중지 완료
2025-09-30 13:54:43,092 - main - INFO - 👋 인페인팅 서버 종료 완료
2025-09-30 13:54:43,092 - app.utils.discord_notifier - WARNING - Discord 웹훅 URL이 설정되지 않아 알림을 보낼 수 없습니다.
2025-09-30 14:02:56,861 - main - INFO - 🚀 인페인팅 서버 시작 중...
2025-09-30 14:02:56,861 - main - INFO - ✅ 공유 객체를 app.state에 저장 완료
2025-09-30 14:02:56,861 - main - INFO - 🔄 상태 저장 백그라운드 작업 생성 중...
2025-09-30 14:02:56,861 - main - INFO - ✅ 상태 저장 백그라운드 작업 생성 완료
2025-09-30 14:02:56,861 - main - INFO - 🚀 세션 풀 초기화 (CUDA 자동 감지)
2025-09-30 14:02:56,861 - app.core.session_pool - INFO - Initializing dynamic session pools...
2025-09-30 14:02:56,861 - app.core.session_pool - INFO - Pre-loading 2 sessions for simple_lama
2025-09-30 14:02:56,861 - main - INFO - 🔄 상태 저장 백그라운드 작업 시작됨
2025-09-30 14:02:56,862 - app.core.session_pool - INFO - Creating new session simple_lama_0 for simple_lama...
2025-09-30 14:02:58,479 - app.core.session_pool - INFO - Creating new session simple_lama_1 for simple_lama...
2025-09-30 14:02:58,479 - app.models.simple_lama - INFO - Loading Simple LAMA model...
2025-09-30 14:02:59,441 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
2025-09-30 14:02:59,442 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
2025-09-30 14:02:59,442 - app.models.simple_lama - INFO - Loading Simple LAMA model...
2025-09-30 14:03:00,171 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
2025-09-30 14:03:00,171 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
2025-09-30 14:03:00,171 - app.core.session_pool - INFO - Successfully created session simple_lama_0
2025-09-30 14:03:00,171 - app.core.session_pool - INFO - ➕ Session Created (simple_lama). Status -> simple_lama: 0, migan: 0, rembg: 0 | VRAM: 0.0/0.0 GB (7.3%)
2025-09-30 14:03:00,172 - app.core.session_pool - INFO - Successfully created session simple_lama_1
2025-09-30 14:03:00,172 - app.core.session_pool - INFO - ➕ Session Created (simple_lama). Status -> simple_lama: 0, migan: 0, rembg: 0 | VRAM: 0.0/0.0 GB (7.3%)
2025-09-30 14:03:00,172 - app.core.session_pool - INFO - Pre-loading 1 sessions for migan
2025-09-30 14:03:00,172 - app.core.session_pool - INFO - Creating new session migan_0 for migan...
2025-09-30 14:03:00,210 - app.models.migan - INFO - Loading MIGAN ONNX model...
2025-09-30 14:03:00,210 - app.models.migan - INFO - MIGAN ONNX 런타임 세션 생성 시도...
2025-09-30 14:03:00,211 - app.models.migan - INFO - MIGAN ONNX providers 설정: ['CUDAExecutionProvider', 'CPUExecutionProvider']
2025-09-30 14:03:00,480 - app.models.migan - INFO - MIGAN ONNX 세션 생성 완료. Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
2025-09-30 14:03:00,480 - app.models.migan - INFO - MIGAN ONNX model loaded successfully
2025-09-30 14:03:00,481 - app.core.session_pool - INFO - Successfully created session migan_0
2025-09-30 14:03:00,481 - app.core.session_pool - INFO - ➕ Session Created (migan). Status -> simple_lama: 2, migan: 0, rembg: 0 | VRAM: 0.0/0.0 GB (7.7%)
2025-09-30 14:03:00,481 - app.core.session_pool - INFO - Pre-loading 1 sessions for rembg
2025-09-30 14:03:00,481 - app.core.session_pool - INFO - Creating new session rembg_0 for rembg...
2025-09-30 14:03:00,482 - app.models.bria_rmbg_onnx - INFO - BriaRMBGOnnxProcessor 초기화 완료
2025-09-30 14:03:00,482 - app.models.bria_rmbg_onnx - INFO - Bria RMBG ONNX 세션 생성 중... path=app/models/onnx/BriaRMBG1.4_model_fp16.onnx
2025-09-30 14:03:00,883 - app.models.bria_rmbg_onnx - INFO - Bria RMBG ONNX 세션 생성 완료, Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'] | Input: input, Output: output
2025-09-30 14:03:00,884 - app.core.session_pool - INFO - Successfully created session rembg_0
2025-09-30 14:03:00,885 - app.core.session_pool - INFO - ➕ Session Created (rembg). Status -> simple_lama: 2, migan: 1, rembg: 0 | VRAM: 0.0/0.0 GB (8.9%)
2025-09-30 14:03:00,885 - app.core.session_pool - INFO - Session pools initialized successfully
2025-09-30 14:03:00,885 - main - INFO - ✅ 세션 풀 초기화 완료
2025-09-30 14:03:00,886 - app.core.worker_manager - INFO - Starting worker manager...
2025-09-30 14:03:00,886 - app.core.worker_manager - INFO - Worker manager started with 2 workers
2025-09-30 14:03:00,886 - main - INFO - ✅ 워커 매니저 시작 완료
2025-09-30 14:03:00,886 - app.core.batch_manager - INFO - Starting BatchManager...
2025-09-30 14:03:00,886 - app.core.batch_manager - INFO - BatchManager started successfully.
2025-09-30 14:03:00,886 - main - INFO - ✅ 배치 관리자 시작 완료
2025-09-30 14:03:00,887 - main - INFO - 🎉 인페인팅 서버 시작 완료!
2025-09-30 14:03:00,887 - app.utils.discord_notifier - WARNING - Discord 웹훅 URL이 설정되지 않아 알림을 보낼 수 없습니다.
2025-09-30 14:03:00,888 - app.core.session_pool - INFO - Idle session reaper started. Timeout: 1800s, Check Interval: 60s
2025-09-30 14:08:24,240 - main - INFO - 🛑 인페인팅 서버 종료 중...
2025-09-30 14:08:24,241 - app.core.worker_manager - INFO - Stopping worker manager...
2025-09-30 14:08:24,241 - app.core.worker_manager - INFO - Worker manager stopped
2025-09-30 14:08:24,241 - main - INFO - ✅ 워커 매니저 중지 완료
2025-09-30 14:08:24,242 - app.core.batch_manager - INFO - Stopping BatchManager...
2025-09-30 14:08:24,242 - app.core.batch_manager - INFO - BatchManager stopped.
2025-09-30 14:08:24,242 - main - INFO - ✅ 배치 관리자 중지 완료
2025-09-30 14:08:24,243 - main - INFO - 👋 인페인팅 서버 종료 완료
2025-09-30 14:08:24,243 - app.utils.discord_notifier - WARNING - Discord 웹훅 URL이 설정되지 않아 알림을 보낼 수 없습니다.
2025-09-30 14:10:55,233 - main - INFO - 🚀 인페인팅 서버 시작 중...
2025-09-30 14:10:55,234 - main - INFO - ✅ 공유 객체를 app.state에 저장 완료
2025-09-30 14:10:55,234 - main - INFO - 🔄 상태 저장 백그라운드 작업 생성 중...
2025-09-30 14:10:55,234 - main - INFO - ✅ 상태 저장 백그라운드 작업 생성 완료
2025-09-30 14:10:55,234 - main - INFO - 🚀 세션 풀 초기화 (CUDA 자동 감지)
2025-09-30 14:10:55,234 - app.core.session_pool - INFO - Initializing dynamic session pools...
2025-09-30 14:10:55,234 - app.core.session_pool - INFO - Pre-loading 2 sessions for simple_lama
2025-09-30 14:10:55,234 - main - INFO - 🔄 상태 저장 백그라운드 작업 시작됨
2025-09-30 14:10:55,235 - app.core.session_pool - INFO - Creating new session simple_lama_0 for simple_lama...
2025-09-30 14:10:56,821 - app.core.session_pool - INFO - Creating new session simple_lama_1 for simple_lama...
2025-09-30 14:10:56,822 - app.models.simple_lama - INFO - Loading Simple LAMA model...
2025-09-30 14:10:57,770 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
2025-09-30 14:10:57,770 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
2025-09-30 14:10:57,770 - app.models.simple_lama - INFO - Loading Simple LAMA model...
2025-09-30 14:10:58,493 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
2025-09-30 14:10:58,493 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
2025-09-30 14:10:58,493 - app.core.session_pool - INFO - Successfully created session simple_lama_0
2025-09-30 14:10:58,494 - app.core.session_pool - INFO - ➕ Session Created (simple_lama). Status -> simple_lama: 0, migan: 0, rembg: 0 | VRAM: 0.0/0.0 GB (7.3%)
2025-09-30 14:10:58,494 - app.core.session_pool - INFO - Successfully created session simple_lama_1
2025-09-30 14:10:58,494 - app.core.session_pool - INFO - ➕ Session Created (simple_lama). Status -> simple_lama: 0, migan: 0, rembg: 0 | VRAM: 0.0/0.0 GB (7.3%)
2025-09-30 14:10:58,494 - app.core.session_pool - INFO - Pre-loading 1 sessions for migan
2025-09-30 14:10:58,494 - app.core.session_pool - INFO - Creating new session migan_0 for migan...
2025-09-30 14:10:58,534 - app.models.migan - INFO - Loading MIGAN ONNX model...
2025-09-30 14:10:58,534 - app.models.migan - INFO - MIGAN ONNX 런타임 세션 생성 시도...
2025-09-30 14:10:58,535 - app.models.migan - INFO - MIGAN ONNX providers 설정: ['CUDAExecutionProvider', 'CPUExecutionProvider']
2025-09-30 14:10:58,804 - app.models.migan - INFO - MIGAN ONNX 세션 생성 완료. Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
2025-09-30 14:10:58,804 - app.models.migan - INFO - MIGAN ONNX model loaded successfully
2025-09-30 14:10:58,804 - app.core.session_pool - INFO - Successfully created session migan_0
2025-09-30 14:10:58,805 - app.core.session_pool - INFO - ➕ Session Created (migan). Status -> simple_lama: 2, migan: 0, rembg: 0 | VRAM: 0.0/0.0 GB (7.7%)
2025-09-30 14:10:58,805 - app.core.session_pool - INFO - Pre-loading 1 sessions for rembg
2025-09-30 14:10:58,805 - app.core.session_pool - INFO - Creating new session rembg_0 for rembg...
2025-09-30 14:10:58,806 - app.models.bria_rmbg_onnx - INFO - BriaRMBGOnnxProcessor 초기화 완료
2025-09-30 14:10:58,806 - app.models.bria_rmbg_onnx - INFO - Bria RMBG ONNX 세션 생성 중... path=app/models/onnx/BriaRMBG1.4_model_fp16.onnx
2025-09-30 14:10:59,204 - app.models.bria_rmbg_onnx - INFO - Bria RMBG ONNX 세션 생성 완료, Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'] | Input: input, Output: output
2025-09-30 14:10:59,205 - app.core.session_pool - INFO - Successfully created session rembg_0
2025-09-30 14:10:59,206 - app.core.session_pool - INFO - ➕ Session Created (rembg). Status -> simple_lama: 2, migan: 1, rembg: 0 | VRAM: 0.0/0.0 GB (8.9%)
2025-09-30 14:10:59,206 - app.core.session_pool - INFO - Session pools initialized successfully
2025-09-30 14:10:59,206 - main - INFO - ✅ 세션 풀 초기화 완료
2025-09-30 14:10:59,206 - app.core.worker_manager - INFO - Starting worker manager...
2025-09-30 14:10:59,207 - app.core.worker_manager - INFO - Worker manager started with 2 workers
2025-09-30 14:10:59,207 - main - INFO - ✅ 워커 매니저 시작 완료
2025-09-30 14:10:59,207 - app.core.batch_manager - INFO - Starting BatchManager...
2025-09-30 14:10:59,207 - app.core.batch_manager - INFO - BatchManager started successfully.
2025-09-30 14:10:59,207 - main - INFO - ✅ 배치 관리자 시작 완료
2025-09-30 14:10:59,207 - main - INFO - 🎉 인페인팅 서버 시작 완료!
2025-09-30 14:10:59,208 - app.utils.discord_notifier - WARNING - Discord 웹훅 URL이 설정되지 않아 알림을 보낼 수 없습니다.
2025-09-30 14:10:59,209 - app.core.session_pool - INFO - Idle session reaper started. Timeout: 1800s, Check Interval: 60s
2025-09-30 14:11:40,105 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:11:48,281 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:11:48,282 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 8.175s
2025-09-30 14:11:48,282 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 8.177s
2025-09-30 14:11:48,529 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:11:48,608 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:11:48,608 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.078s
2025-09-30 14:11:48,608 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.079s
2025-09-30 14:11:48,861 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:11:48,931 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:11:48,932 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.070s
2025-09-30 14:11:48,932 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.071s
2025-09-30 14:11:49,126 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:11:49,199 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:11:49,199 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.072s
2025-09-30 14:11:49,200 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.073s
2025-09-30 14:11:49,388 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:11:49,459 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:11:49,459 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.070s
2025-09-30 14:11:49,460 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.071s
2025-09-30 14:12:04,282 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:12:04,361 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:12:04,362 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.079s
2025-09-30 14:12:04,362 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.080s
2025-09-30 14:12:04,602 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:12:04,676 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:12:04,676 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.072s
2025-09-30 14:12:04,676 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.074s
2025-09-30 14:12:04,878 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:12:04,947 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:12:04,948 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.068s
2025-09-30 14:12:04,948 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.070s
2025-09-30 14:12:05,136 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:12:05,213 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:12:05,213 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.076s
2025-09-30 14:12:05,213 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.077s
2025-09-30 14:12:05,407 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:12:05,478 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=113.244
2025-09-30 14:12:05,478 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.071s
2025-09-30 14:12:05,479 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.072s
2025-09-30 14:15:08,451 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:15:08,452 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:15:08,530 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:15:10,121 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 1.669s
2025-09-30 14:15:10,122 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 1.669s (avg: 1.669s/image)
2025-09-30 14:15:10,122 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:15:14,728 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:15:14,729 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:15:14,777 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:15:21,682 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 6.952s
2025-09-30 14:15:21,682 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 6.952s (avg: 6.952s/image)
2025-09-30 14:15:21,683 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:15:26,608 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:15:26,609 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:15:26,641 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:15:26,764 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.156s
2025-09-30 14:15:26,765 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.156s (avg: 0.156s/image)
2025-09-30 14:15:26,765 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:15:32,559 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:15:32,560 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:15:32,595 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:15:32,720 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.160s
2025-09-30 14:15:32,721 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.160s (avg: 0.160s/image)
2025-09-30 14:15:32,721 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:15:37,543 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:15:37,544 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:15:37,580 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:15:37,701 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.157s
2025-09-30 14:15:37,702 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.157s (avg: 0.157s/image)
2025-09-30 14:15:37,702 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:15:49,263 - app.api.endpoints - INFO - 플러그인 호환 엔드포인트 '/api/v1/run_plugin_gen_image' 호출됨 (모델: rembg)
2025-09-30 14:15:49,298 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:15:49,389 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=84.301
2025-09-30 14:15:49,389 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.090s
2025-09-30 14:15:49,391 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.093s
2025-09-30 14:15:54,546 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:15:54,547 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:15:54,582 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:15:54,708 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.161s
2025-09-30 14:15:54,709 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.161s (avg: 0.161s/image)
2025-09-30 14:15:54,709 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:15:58,795 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:15:58,796 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:15:58,830 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:15:58,956 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.160s
2025-09-30 14:15:58,957 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.160s (avg: 0.160s/image)
2025-09-30 14:15:58,957 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:16:04,838 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:16:04,839 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:16:04,876 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:16:05,009 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.170s
2025-09-30 14:16:05,010 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.170s (avg: 0.170s/image)
2025-09-30 14:16:05,010 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:16:10,709 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:16:10,710 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:16:10,751 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:16:10,875 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.165s
2025-09-30 14:16:10,875 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.165s (avg: 0.165s/image)
2025-09-30 14:16:10,875 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:16:16,406 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:16:16,407 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:16:16,448 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:16:16,575 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.168s
2025-09-30 14:16:16,575 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.168s (avg: 0.168s/image)
2025-09-30 14:16:16,575 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:16:48,043 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:16:48,045 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:16:48,122 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:16:48,282 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.237s
2025-09-30 14:16:48,282 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.237s (avg: 0.237s/image)
2025-09-30 14:16:48,282 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:16:52,866 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:16:52,867 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:16:52,909 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:16:53,032 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.165s
2025-09-30 14:16:53,032 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.165s (avg: 0.165s/image)
2025-09-30 14:16:53,033 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:16:57,197 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:16:57,198 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:16:57,240 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:16:57,370 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.172s
2025-09-30 14:16:57,370 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.172s (avg: 0.172s/image)
2025-09-30 14:16:57,370 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:17:03,801 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:17:03,802 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:17:03,845 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:17:03,971 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.169s
2025-09-30 14:17:03,972 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.169s (avg: 0.169s/image)
2025-09-30 14:17:03,972 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:17:08,175 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:17:08,176 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:17:08,225 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:17:08,355 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.179s
2025-09-30 14:17:08,356 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.179s (avg: 0.179s/image)
2025-09-30 14:17:08,356 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:17:12,633 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:17:12,634 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:17:12,677 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:17:12,807 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.173s
2025-09-30 14:17:12,807 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.173s (avg: 0.173s/image)
2025-09-30 14:17:12,808 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:17:25,031 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:17:25,032 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:17:25,069 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:17:25,198 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.166s
2025-09-30 14:17:25,198 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.166s (avg: 0.166s/image)
2025-09-30 14:17:25,199 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:17:40,552 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:17:40,552 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:17:40,601 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:17:40,742 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.189s
2025-09-30 14:17:40,742 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.189s (avg: 0.189s/image)
2025-09-30 14:17:40,742 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:17:45,218 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:17:45,218 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:17:45,269 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:17:45,411 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.192s
2025-09-30 14:17:45,412 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.192s (avg: 0.192s/image)
2025-09-30 14:17:45,412 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:17:50,561 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:17:50,562 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:17:50,607 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:17:50,747 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.184s
2025-09-30 14:17:50,747 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.184s (avg: 0.184s/image)
2025-09-30 14:17:50,747 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:18:09,148 - app.api.endpoints - INFO - 플러그인 호환 엔드포인트 '/api/v1/run_plugin_gen_image' 호출됨 (모델: rembg)
2025-09-30 14:18:09,179 - app.models.bria_rmbg_onnx - INFO - 배경제거 시작(Bria ONNX): image.shape=(800, 800, 3), model_name=briaaiRMBG-1.4
2025-09-30 14:18:09,278 - app.models.bria_rmbg_onnx - INFO - Bria ONNX mask stats: min=0, max=255, mean=98.451
2025-09-30 14:18:09,278 - app.models.bria_rmbg_onnx - INFO - 'bria-rmbg' processed in 0.098s
2025-09-30 14:18:09,278 - app.core.worker_manager - INFO - 'rembg (briaaiRMBG-1.4)' processed in 0.100s
2025-09-30 14:18:18,298 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:18:18,299 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:18:18,340 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:18:18,463 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.164s
2025-09-30 14:18:18,463 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.164s (avg: 0.164s/image)
2025-09-30 14:18:18,464 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:18:37,359 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:18:37,360 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:18:37,413 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:18:37,548 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.187s
2025-09-30 14:18:37,548 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.187s (avg: 0.187s/image)
2025-09-30 14:18:37,548 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:18:40,383 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:18:40,384 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:18:40,430 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:18:40,565 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.181s
2025-09-30 14:18:40,566 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.181s (avg: 0.181s/image)
2025-09-30 14:18:40,566 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:18:47,280 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:18:47,281 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:18:47,322 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:18:47,456 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.175s
2025-09-30 14:18:47,456 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.175s (avg: 0.175s/image)
2025-09-30 14:18:47,456 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:18:52,347 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:18:52,348 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:18:52,386 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:18:52,518 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.170s
2025-09-30 14:18:52,519 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.170s (avg: 0.170s/image)
2025-09-30 14:18:52,519 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.
2025-09-30 14:18:57,255 - app.core.batch_manager - INFO - Creating a new batch with 1 jobs.
2025-09-30 14:18:57,256 - app.core.worker_manager - INFO - 🧠[simple_lama] Batch Inference Start (Size: 1). VRAM: 0.0 GB
2025-09-30 14:18:57,295 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 1개 이미지 인페인팅 수행
2025-09-30 14:18:57,415 - app.core.worker_manager - INFO - ✅[simple_lama] Batch Inference End (Size: 1). VRAM: 0.0 GB | Duration: 0.159s
2025-09-30 14:18:57,415 - app.core.worker_manager - INFO - 'simple-lama' batch of 1 processed in 0.159s (avg: 0.159s/image)
2025-09-30 14:18:57,416 - app.core.batch_manager - INFO - Successfully processed batch of 1 jobs.

1378
logs/main_server.log
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File diff suppressed because it is too large Load Diff

2
logs/main_server.pid
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@ -1 +1 @@
8030
16093

262
logs/monitoring.log
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@ -1,175 +1,97 @@
WARNING:root:jtop library not found. Jetson monitoring will be limited. Please run 'sudo pip install jetson-stats'
INFO: Started server process [8148]
INFO: Started server process [16259]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8888 (Press CTRL+C to quit)
INFO: 127.0.0.1:33342 - "GET /api/simple HTTP/1.1" 200 OK
INFO: 122.35.47.45:51860 - "GET / HTTP/1.1" 200 OK
INFO: 122.35.47.45:51865 - "WebSocket /ws" [accepted]
INFO: 127.0.0.1:55412 - "GET /api/simple HTTP/1.1" 200 OK
INFO: 122.35.47.45:53200 - "GET / HTTP/1.1" 200 OK
INFO: 122.35.47.45:53202 - "WebSocket /ws" [accepted]
INFO: connection open
INFO: 122.35.47.45:51860 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:51861 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51867 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51866 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51868 - "GET /favicon.ico HTTP/1.1" 404 Not Found
INFO: 122.35.47.45:51873 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51873 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51873 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51877 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51876 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:51875 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51878 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51895 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51895 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51895 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51918 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:51916 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51919 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51917 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51938 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51938 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51938 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51973 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:51974 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51975 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51972 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51986 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:51986 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:51986 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52008 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52005 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52007 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52006 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52025 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52025 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52025 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52046 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52047 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52049 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52048 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52054 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52054 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52054 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52061 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52060 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52058 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52059 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52069 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52069 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52069 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52082 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52084 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52081 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52083 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52087 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52087 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52087 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52112 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52114 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52115 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52113 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52113 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52113 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52113 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52131 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52130 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52129 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52132 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52144 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52144 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52144 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52154 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52151 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52153 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52152 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52164 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52164 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52164 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52170 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52169 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52171 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52172 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52177 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52183 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52182 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52184 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52185 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52195 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52194 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52194 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52214 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52213 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52216 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52215 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52225 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52225 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52234 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52235 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52236 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52237 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52238 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52245 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52245 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52245 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52257 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52259 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52256 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52258 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52261 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52261 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52261 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52268 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52265 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52266 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52267 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52272 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52272 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52272 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52276 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52278 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52275 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52277 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52290 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52290 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52290 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52307 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52309 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52308 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52306 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52316 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52316 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52316 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52369 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52366 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52367 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52368 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52368 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52368 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52368 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52387 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52389 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52388 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52386 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52392 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52392 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52392 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52393 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52395 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52394 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52396 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52449 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:52448 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:52450 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:52447 - "GET /api/performance-stats HTTP/1.1" 200 OK
ERROR:app.monitoring.dashboard:모델 성능 통계 조회 중 예외 발생: HTTPConnectionPool(host='0.0.0.0', port=8008): Max retries exceeded with url: /api/v1/stats (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f1f99e52110>: Failed to establish a new connection: [Errno 111] Connection refused'))
ERROR:app.monitoring.dashboard:모델 성능 통계 조회 중 예외 발생: HTTPConnectionPool(host='0.0.0.0', port=8008): Max retries exceeded with url: /api/v1/stats (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f1f99e500d0>: Failed to establish a new connection: [Errno 111] Connection refused'))
ERROR:app.monitoring.dashboard:모델 성능 통계 조회 중 예외 발생: HTTPConnectionPool(host='0.0.0.0', port=8008): Max retries exceeded with url: /api/v1/stats (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f1f99db36d0>: Failed to establish a new connection: [Errno 111] Connection refused'))
ERROR:app.monitoring.dashboard:모델 성능 통계 조회 중 예외 발생: HTTPConnectionPool(host='0.0.0.0', port=8008): Max retries exceeded with url: /api/v1/stats (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f1f99db0ca0>: Failed to establish a new connection: [Errno 111] Connection refused'))
INFO: Shutting down
INFO: connection closed
INFO: Waiting for background tasks to complete. (CTRL+C to force quit)
ERROR:app.monitoring.dashboard:모델 성능 통계 조회 중 예외 발생: HTTPConnectionPool(host='0.0.0.0', port=8008): Max retries exceeded with url: /api/v1/stats (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f1f99e51060>: Failed to establish a new connection: [Errno 111] Connection refused'))
ERROR:app.monitoring.dashboard:모델 성능 통계 조회 중 예외 발생: HTTPConnectionPool(host='0.0.0.0', port=8008): Max retries exceeded with url: /api/v1/stats (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f1f99e525f0>: Failed to establish a new connection: [Errno 111] Connection refused'))
INFO: Waiting for application shutdown.
INFO: Application shutdown complete.
INFO: Finished server process [8148]
INFO: 122.35.47.45:53200 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53201 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53203 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53204 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53222 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53222 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53222 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53242 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53241 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53243 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53240 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53249 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53249 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53249 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53256 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53257 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53255 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53258 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53275 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53275 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53275 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53293 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53294 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53291 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53292 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53299 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53299 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53299 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53312 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53311 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53310 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53313 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53365 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53364 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53376 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53377 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53424 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53423 - "GET /api/performance-stats HTTP/1.1" 200 OK
ERROR:app.monitoring.dashboard:모델 성능 통계 조회 중 예외 발생: HTTPConnectionPool(host='0.0.0.0', port=8008): Read timed out. (read timeout=2)
ERROR:app.monitoring.dashboard:모델 성능 통계 조회 중 예외 발생: HTTPConnectionPool(host='0.0.0.0', port=8008): Read timed out. (read timeout=2)
INFO: 122.35.47.45:53473 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53470 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53471 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53472 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53534 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53535 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53533 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53536 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53550 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53548 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53549 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53551 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53561 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53561 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53561 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53570 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53568 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53569 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53571 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53589 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53589 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53589 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53634 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53631 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53633 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53632 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53646 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53646 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53646 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53677 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53679 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53678 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53676 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53684 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53684 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53684 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53711 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53714 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53712 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53713 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53725 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53725 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53725 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53753 - "GET /api/logs?lines=50 HTTP/1.1" 200 OK
INFO: 122.35.47.45:53756 - "GET /api/performance-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53755 - "GET /api/model-usage-stats HTTP/1.1" 200 OK
INFO: 122.35.47.45:53754 - "GET /api/system-alerts HTTP/1.1" 200 OK
INFO: 122.35.47.45:53772 - "GET /api/system-alerts HTTP/1.1" 200 OK

2
logs/monitoring.pid
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@ -1 +1 @@
8148
16259

38
main.py
View File

@ -142,6 +142,17 @@ class APIStatsCollector:
"current_concurrent": self.endpoint_concurrent[endpoint]
}
# 기본 엔드포인트 프리시드 (요청이 없어도 0으로 노출)
for endpoint in DEFAULT_ENDPOINTS:
if endpoint not in endpoint_stats:
endpoint_stats[endpoint] = {
"count": 0,
"avg_time": 0.0,
"min_time": 0.0,
"max_time": 0.0,
"current_concurrent": self.endpoint_concurrent[endpoint]
}
return {
"total_requests": self.total_requests,
"successful_requests": self.successful_requests,
@ -162,6 +173,25 @@ class APIStatsCollector:
# 전역 통계 수집기
api_stats = APIStatsCollector()
# 대시보드/헬스 전용 경로는 API 통계에서 제외
# - 주기적 폴링으로 인해 실제 비즈니스 엔드포인트 통계를 왜곡시키지 않기 위함
EXCLUDED_ENDPOINTS = {
"/api/v1/health",
"/docs",
"/openapi.json",
"/redoc",
}
EXCLUDED_PREFIXES = [
"/api/v1/stats", # /api/v1/stats 및 /api/v1/stats/* 전체 제외
]
# 대시보드에 기본적으로 표시할 핵심 엔드포인트(요청이 없더라도 0으로 노출)
DEFAULT_ENDPOINTS = [
"POST /api/v1/inpaint",
"POST /api/v1/remove_bg",
"POST /api/v1/run_plugin_gen_image",
]
async def save_status_periodically():
"""주기적으로 워커와 세션 상태를 파일에 저장합니다."""
logger.info("🔄 상태 저장 백그라운드 작업 시작됨")
@ -288,7 +318,13 @@ app = FastAPI(
async def collect_api_stats(request: Request, call_next):
"""API 호출 통계를 수집하는 미들웨어"""
start_time = time.time()
endpoint = f"{request.method} {request.url.path}"
path = request.url.path
# 통계 제외 대상이면 단순 패스스루 (카운팅/지표 반영 안 함)
if path in EXCLUDED_ENDPOINTS or any(path.startswith(p) for p in EXCLUDED_PREFIXES):
return await call_next(request)
endpoint = f"{request.method} {path}"
# 요청 시작
api_stats.start_request(endpoint)

88
status.json
View File

@ -6,14 +6,14 @@
"workers_by_status": {
"idle": [
{
"id": "worker_67ac071e",
"id": "worker_ee896b41",
"status": "idle",
"task_count": 0,
"error_count": 0,
"last_task_at": null
},
{
"id": "worker_99f585de",
"id": "worker_5e530626",
"status": "idle",
"task_count": 0,
"error_count": 0,
@ -50,54 +50,62 @@
}
},
"api_stats": {
"total_requests": 500,
"successful_requests": 500,
"failed_requests": 0,
"success_rate": 100.0,
"total_requests": 66,
"successful_requests": 65,
"failed_requests": 1,
"success_rate": 98.48484848484848,
"endpoint_usage": {
"GET /api/v1/health": 27,
"GET /docs": 2,
"GET /openapi.json": 2,
"GET /api/v1/stats": 469
"POST /api/v1/remove_bg": 10,
"GET /health": 1,
"GET /api/v1/model": 28,
"POST /api/v1/inpaint": 25,
"POST /api/v1/run_plugin_gen_image": 2
},
"endpoint_stats": {
"GET /api/v1/health": {
"count": 27,
"avg_time": 0.0008100227073386863,
"min_time": 0.0004703998565673828,
"max_time": 0.001514434814453125,
"POST /api/v1/remove_bg": {
"count": 10,
"avg_time": 1.089443039894104,
"min_time": 0.24539661407470703,
"max_time": 8.455913782119751,
"current_concurrent": 0
},
"GET /docs": {
"GET /health": {
"count": 1,
"avg_time": 0.001049041748046875,
"min_time": 0.001049041748046875,
"max_time": 0.001049041748046875,
"current_concurrent": 0
},
"GET /api/v1/model": {
"count": 28,
"avg_time": 0.0010251828602382115,
"min_time": 0.0008790493011474609,
"max_time": 0.0013933181762695312,
"current_concurrent": 0
},
"POST /api/v1/inpaint": {
"count": 25,
"avg_time": 0.9369040489196777,
"min_time": 0.40611815452575684,
"max_time": 7.1920835971832275,
"current_concurrent": 1
},
"POST /api/v1/run_plugin_gen_image": {
"count": 2,
"avg_time": 0.0005669593811035156,
"min_time": 0.0005576610565185547,
"max_time": 0.0005762577056884766,
"current_concurrent": 0
},
"GET /openapi.json": {
"count": 2,
"avg_time": 0.0098419189453125,
"min_time": 0.0009410381317138672,
"max_time": 0.018742799758911133,
"current_concurrent": 0
},
"GET /api/v1/stats": {
"count": 469,
"avg_time": 0.0007652616500854492,
"min_time": 0.0005528926849365234,
"max_time": 0.0024929046630859375,
"avg_time": 0.28528332710266113,
"min_time": 0.26673054695129395,
"max_time": 0.3038361072540283,
"current_concurrent": 0
}
},
"average_response_time": 0.0007812113761901855,
"min_response_time": 0.00044035911560058594,
"max_response_time": 0.018742799758911133,
"current_concurrent": 0,
"average_response_time": 0.5290507945147428,
"min_response_time": 0.0008790493011474609,
"max_response_time": 8.455913782119751,
"current_concurrent": 1,
"max_concurrent": 1,
"requests_per_second": 0.6468627716181136,
"uptime": 772.9614717960358,
"requests_per_second": 0.13702728923148325,
"uptime": 481.65588307380676,
"recent_errors": []
},
"timestamp": 1759240482.3948221
"timestamp": 1759241936.8845046
}

2
te.py
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@ -1,2 +0,0 @@
import tensorrt as trt
print(trt.__version__)

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4
tests/test_rembg_onnx.py
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@ -7,8 +7,8 @@ from io import BytesIO
# --- 설정 ---
SERVER_URL = "http://127.0.0.1:8008/api/v1/remove_bg"
IMAGE_PATH = "tests/rembg_test/456.webp"
OUTPUT_PATH = "tests/rembg_test/output_bria.png"
IMAGE_PATH = "/workspace/inpaintServer/tests/rembg_test/456.webp"
OUTPUT_PATH = "/workspace/inpaintServer/tests/rembg_test/output_bria.png"
NUM_TESTS = 5
# --- 테스트 준비 ---