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Ryan Shpeherd
2026-09-08 13:51:31 -04:00
parent baa7ded329
commit 83f980f7f8
5 changed files with 409 additions and 11 deletions
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README.md
+207
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@@ -82,6 +82,213 @@ open http://localhost:3000 (admin / your_grafana_password)
open http://localhost:9090
```
## System Operation
### How Processes Start
**Service Initialization:**
1. **MariaDB** starts first with health check
2. **Worker** initializes via `src/main.py`:
- Loads `config.yaml`
- Sets up JSON logging with rotation
- Connects to MariaDB (connection pooling)
- Initializes database schema
- Verifies GPU availability (CUDA/PyTorch)
- Starts the `DirectoryScanner` in a background thread (scans permanent storage in place)
- Creates `WorkerPool` with 1 worker thread
- Enters job processing loop
3. **UI** starts Flask review interface via Gunicorn (2 workers)
4. **Monitoring** starts Prometheus and Grafana independently
### Processing Pipeline
The worker follows this flow for each video:
```
Pending → Lock → Probe → Sample → Detect → Classify → Aggregate → Route → Persist → Export → Cleanup → Completed
```
**Detailed Steps:**
1. **Job Queue** - Atomically lock `PENDING` jobs via `UPDATE status = 'PROCESSING'`
- Priority: newest files first (`last_scan_time DESC`)
- Max concurrent: 1 per GPU
2. **Probe Video** - Extract metadata via FFprobe
- Duration, codec, resolution
- Validate against codec whitelist (H.264, H.265, VP8/9, AV1)
- Mark `UNSCANNABLE` if invalid
3. **Sample Frames** - Extract frames at configured interval (default: 30s)
- Save as JPEG to `/scratch/{video_id}/frames/`
- Quality: 2 (lower=better)
4. **Detect Faces** - YOLOv8n TensorRT inference (FP32)
- Batch size auto-tuned by GPU memory monitor
- NMS filtering (IoU: 0.45, confidence: 0.25)
- Cap: 10 faces/frame, 100 faces/video
5. **Extract Crops** - Resize detected faces to 224×224
- Save to `/scratch/{video_id}/crops/`
6. **Classify Crops** - MobileNetV3-Small TensorRT inference
- Temperature-scaled softmax (T=1.0)
- Returns confidence per crop
7. **Aggregate Confidence** - Combine crop confidences into video-level score
- Strategy: `max` (most conservative)
- Alternatives: `weighted_mean`, `top_k_mean`
8. **Route Decision** - Threshold-based routing:
- `C ≥ 0.75` → **MATCH**
- `0.45 ≤ C < 0.75` → **REVIEW** (human annotation)
- `C < 0.45` → **SKIP**
- No faces → **SKIP**
9. **Persist Results** - Atomic transaction:
- Update `videos` table (confidence, routing, status)
- Insert `processing_logs` row (audit trail)
- State guard: only update if `status='PROCESSING'`
10. **Export** - Buffer and batch export (default: 100 videos)
- Format: Parquet with Snappy compression
- Path: `/data/output/{model_version}/`
- Fallback: JSONL if Parquet fails
11. **Cleanup** - Delete `/scratch/{video_id}/` directory
- Only after successful persistence
- Prevents orphaned scratch files
**Directory Scanner Service (runs alongside the worker):**
- Scans the permanent storage location **in place** (no staging/copy step)
- Walks `/data/input` every **2 hours** by default (configurable via `scanner.scan_interval_seconds`)
- Detects new, modified, and removed video files by comparing against the DB
- Filters to video files by extension
- Computes SHA256 hash, probes metadata, validates codec
- Queues any video that has not been scanned yet as `PENDING` for the worker pool
- **Single-instance guard:** an in-process lock plus a DB lock (with a lease) ensure only one scan runs at a time — a long-running scan never overlaps another, even across multiple worker replicas. The lock lease is refreshed via heartbeats during the scan and is taken over automatically if a scanner crashes.
### Configuration Reference
All configuration is in `config.yaml`. Environment variable override format: `VD_<SECTION>_<KEY>` (e.g., `VD_SAMPLING_INTERVAL_SECONDS=60`).
#### Key Configuration Sections
**Sampling & Thresholds:**
```yaml
sampling:
interval_seconds: 30 # Frame extraction frequency
quality: 2 # JPEG quality (1-31, lower=better)
format: jpeg
thresholds:
T_high: 0.75 # MATCH threshold
T_low: 0.45 # REVIEW threshold
```
**GPU & Batching:**
```yaml
gpu:
max_memory_gb: 18 # Target VRAM usage
batch_size: auto # Auto-tune based on available VRAM
batching:
max_batch_size: 16 # Maximum batch size
vram_target_gb: 16 # Target VRAM for batch tuning
vram_reduce_threshold_gb: 16 # Reduce batch if above
vram_increase_threshold_gb: 10 # Increase batch if below
```
**Storage Paths:**
```yaml
storage:
scratch_path: /scratch # Temporary processing (tmpfs)
input_path: /data/input # Source videos (NFS)
output_path: /data/output # Results (local/NAS)
models_path: /models # TensorRT models
training_path: /data/training # Training data
```
**Database:**
```yaml
database:
host: mariadb
port: 3306
name: videodetect
user: videodetect
password: videodetect123
pool_size: 20 # Connection pool size
pool_min: 5
pool_recycle: 3600 # Recycle connections after 1h
```
**Face Detection:**
```yaml
face_detection:
model: yolo8n
model_path: /models/face_detector/face_detector.trt
input_size: 640
confidence_threshold: 0.25
iou_threshold: 0.45
max_faces_per_frame: 10
max_faces_per_video: 100
```
**Classification & Aggregation:**
```yaml
classifier:
model: mobilenetv3-small
model_path: /models/classifier/classifier.trt
input_size: 224
temperature: 1.0 # Calibration temperature
aggregation:
strategy: max # max, weighted_mean, top_k_mean
alpha: 1.0 # weighted_mean weight for mean
beta: 0.1 # weighted_mean weight for variance
top_k: 3 # top_k_mean: average top 3 scores
```
**Export:**
```yaml
export:
format: parquet # parquet, jsonl, or both
compression: snappy
batch_size: 100 # Export after N videos
include_frame_confidences: true
```
**Review UI:**
```yaml
review_ui:
host: "0.0.0.0"
port: 5000
per_page: 20 # Pagination
top_k_frames: 5 # Show top-k contributing frames
auth_enabled: false # No auth per TC-06
ssl_enabled: false # Internal LAN only
```
#### Volume Mounts
From `docker-compose.yml`:
- **Input**: NFS mount → `/data/input` (read-only)
- **Output**: `./output` → `/data/output`
- **Models**: `./models` → `/models`
- **Training**: `./training` → `/data/training`
- **Scratch**: 100GB tmpfs at `/scratch` (RAM disk)
### Key Design Principles
- **Atomic state transitions** - Database locks prevent race conditions
- **Crash recovery** - `PROCESSING` jobs automatically requeued on restart
- **Idempotent** - Re-running same video produces same result
- **Stateless** - Scratch cleanup after each job
- **Fail-safe** - 3 retry attempts before marking `ERROR`
- **No auth/SSL** - Internal LAN deployment per TC-06
## Project Structure
```
config.yaml
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@@ -70,12 +70,18 @@ model:
# -----------------------------------------------------
# Directory Scanner
# -----------------------------------------------------
# Scans the permanent storage location in place (no staging/copy step).
# The corpus is large (~163k files / ~41TB), so the default interval is 2 hours.
# A single-instance guard (in-process lock + DB lock with a lease) ensures a
# long-running scan never overlaps another scan, even across replicas.
scanner:
scan_interval_seconds: 60
scan_interval_seconds: 7200 # 2 hours (adaptive: raise for very large corpora)
walker_threads: 8
ffprobe_timeout_seconds: 10
hash_algorithm: sha256
hash_chunk_size_mb: 1
lock_lease_seconds: 21600 # 6 hours: max time a scan may hold the lock before it is considered stale
heartbeat_interval_files: 500 # refresh the lock lease every N files processed
# -----------------------------------------------------
# Codec Validation
db/schema.sql
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@@ -115,6 +115,20 @@ CREATE TABLE IF NOT EXISTS scan_history (
error_message TEXT DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
-- -----------------------------------------------------
-- Table: scanner_lock
-- Single-instance guard for the directory scanner. Ensures only one scan
-- runs at a time across all replicas. A lease (locked_at + lease_seconds)
-- lets a live scanner keep the lock via heartbeats, and lets a crashed
-- scanner's lock be taken over once it goes stale.
-- -----------------------------------------------------
CREATE TABLE IF NOT EXISTS scanner_lock (
lock_name VARCHAR(64) PRIMARY KEY,
owner VARCHAR(128) NOT NULL COMMENT 'Instance id (host-pid-uuid) holding the lock',
locked_at DATETIME NOT NULL COMMENT 'Last time the lock was acquired or heartbeated',
lease_seconds INT NOT NULL DEFAULT 21600 COMMENT 'Lock is stale if older than this (6 hours)'
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
-- -----------------------------------------------------
-- Insert default model entry
-- -----------------------------------------------------
src/main.py
+24 -1
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@@ -8,6 +8,7 @@ Initializes all components and starts the processing pipeline.
import logging
import signal
import sys
import threading
import time
from pathlib import Path
@@ -18,6 +19,7 @@ from config_loader import get_config
from db_connector import DBConnector
from logging_config import setup_logging
from orchestrator import WorkerPool
from scanner import DirectoryScanner
logger = logging.getLogger(__name__)
@@ -84,7 +86,27 @@ def main():
except ImportError:
logger.warning("PyTorch not installed. GPU features disabled.")
logger.info("Worker initialization complete. Starting processing loop...")
# Initialize the directory scanner.
# It scans the permanent storage location in place (no staging/copy step),
# periodically discovering new/removed video files and queueing any that
# have not been scanned yet as PENDING for the worker pool to pick up.
storage_config = config.get_section("storage")
scanner_config = config.get_section("scanner")
scanner = DirectoryScanner(
db_connector=db,
config=config.data,
input_path=storage_config.get("input_path", "/data/input"),
scan_interval=int(scanner_config.get("scan_interval_seconds", 60)),
walker_threads=int(scanner_config.get("walker_threads", 8)),
)
logger.info("Worker initialization complete. Starting scanner and processing loop...")
# Run the scanner in a background thread (its start() is a blocking loop).
scanner_thread = threading.Thread(
target=scanner.start, name="directory-scanner", daemon=True
)
scanner_thread.start()
pool = WorkerPool(db, config.data, max_workers=1)
@@ -93,6 +115,7 @@ def main():
except KeyboardInterrupt:
logger.info("Worker shutting down.")
pool.stop()
scanner.stop()
if __name__ == "__main__":
src/scanner.py
+152 -4
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@@ -10,9 +10,12 @@ import hashlib
import json
import logging
import os
import socket
import threading
import time
import uuid
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime, timezone
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Dict, List, Optional, Tuple
@@ -54,19 +57,62 @@ class DirectoryScanner:
self._total_files_modified = 0
self._total_files_unscannable = 0
# Single-instance guard: prevents overlapping scans both within this
# process (threading lock) and across replicas (DB lock with a lease).
self._instance_id = f"{socket.gethostname()}-{os.getpid()}-{uuid.uuid4().hex[:8]}"
self._lock_name = "directory_scanner"
self._lock_lease_seconds = int(
config.get("scanner", {}).get("lock_lease_seconds", 21600)
)
self._heartbeat_every = int(
config.get("scanner", {}).get("heartbeat_interval_files", 500)
)
self._scan_lock = threading.Lock()
self._lock_table_ensured = False
def start(self):
"""Start the scanner loop."""
"""Start the scanner loop.
Each cycle is guarded so that at most one scan runs at a time:
- an in-process threading lock prevents re-entrant scans, and
- a DB lock (with a lease) prevents overlapping scans across replicas.
A long-running scan keeps its lease alive via heartbeats, so the next
scheduled tick (or another replica) waits instead of starting a second
parallel scan.
"""
self._running = True
logger.info("Scanner starting: input_path=%s interval=%ds threads=%d",
self.input_path, self.scan_interval, self.walker_threads)
logger.info(
"Scanner starting: input_path=%s interval=%ds threads=%d instance=%s",
self.input_path, self.scan_interval, self.walker_threads, self._instance_id,
)
while self._running:
# In-process re-entrancy guard: never run two scans at once.
if not self._scan_lock.acquire(blocking=False):
logger.warning("A scan is already in progress; skipping this cycle.")
self._sleep_interval()
continue
try:
if self.acquire_lock():
try:
self._run_scan()
finally:
self.release_lock()
else:
logger.info(
"Scanner lock held by another instance; skipping this cycle."
)
except Exception as e:
logger.error("Scanner error: %s", e, exc_info=True)
finally:
self._scan_lock.release()
# Sleep until next scan
self._sleep_interval()
def _sleep_interval(self):
"""Sleep for the scan interval, waking early if stopped."""
for _ in range(self.scan_interval):
if not self._running:
break
@@ -77,6 +123,98 @@ class DirectoryScanner:
self._running = False
logger.info("Scanner stopping. Total scans: %d", self._scan_count)
# ------------------------------------------------------------------
# Single-instance lock (cross-process / cross-replica guard)
# ------------------------------------------------------------------
def _ensure_lock_table(self):
"""Create the scanner_lock table if it does not already exist."""
if self._lock_table_ensured:
return
self.db.execute(
"""CREATE TABLE IF NOT EXISTS scanner_lock (
lock_name VARCHAR(64) PRIMARY KEY,
owner VARCHAR(128) NOT NULL,
locked_at DATETIME NOT NULL,
lease_seconds INT NOT NULL DEFAULT 21600
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci"""
)
self._lock_table_ensured = True
def acquire_lock(self) -> bool:
"""Attempt to acquire the cross-process scanner lock.
Returns True if this instance now owns the lock, False otherwise.
A stale lock (held longer than the lease) is taken over so a crashed
scanner does not block scanning forever.
"""
try:
self._ensure_lock_table()
except Exception as e:
logger.warning("Could not ensure scanner_lock table: %s", e)
return True # fail-open: keep scanning rather than block entirely
now = datetime.now(timezone.utc).replace(tzinfo=None)
# 1) Try to insert a fresh lock row.
try:
self.db.execute(
"""INSERT INTO scanner_lock (lock_name, owner, locked_at, lease_seconds)
VALUES (%s, %s, %s, %s)""",
(self._lock_name, self._instance_id, now, self._lock_lease_seconds),
transaction=True,
)
logger.info("Acquired scanner lock (fresh). owner=%s", self._instance_id)
return True
except Exception:
# Row already exists -> fall through to steal-if-stale.
pass
# 2) Take over the lock if it is stale or already ours.
stale_before = now - timedelta(seconds=self._lock_lease_seconds)
try:
affected = self.db.execute(
"""UPDATE scanner_lock
SET owner = %s, locked_at = %s
WHERE lock_name = %s
AND (owner = %s OR locked_at < %s)""",
(self._instance_id, now, self._lock_name, self._instance_id, stale_before),
transaction=True,
)
if affected and affected > 0:
logger.info("Acquired scanner lock (stale takeover). owner=%s",
self._instance_id)
return True
except Exception as e:
logger.warning("Failed to check scanner lock: %s", e)
return True # fail-open
logger.info("Scanner lock held by another instance; not acquiring.")
return False
def release_lock(self):
"""Release the scanner lock if we own it."""
try:
self.db.execute(
"""DELETE FROM scanner_lock WHERE lock_name = %s AND owner = %s""",
(self._lock_name, self._instance_id),
transaction=True,
)
except Exception as e:
logger.warning("Failed to release scanner lock: %s", e)
def _heartbeat(self):
"""Refresh the lock lease so a long-running scan is not stolen."""
try:
now = datetime.now(timezone.utc).replace(tzinfo=None)
self.db.execute(
"""UPDATE scanner_lock SET locked_at = %s
WHERE lock_name = %s AND owner = %s""",
(now, self._lock_name, self._instance_id),
transaction=True,
)
except Exception as e:
logger.debug("Scanner lock heartbeat failed: %s", e)
def _run_scan(self):
"""Execute a single scan cycle."""
scan_start = time.time()
@@ -181,6 +319,7 @@ class DirectoryScanner:
def _process_files_batch(self, files: List[Path]) -> List[dict]:
"""Process a batch of files in parallel."""
results = []
processed = 0
with ThreadPoolExecutor(max_workers=self.walker_threads) as executor:
future_to_file = {
@@ -201,6 +340,11 @@ class DirectoryScanner:
"error_message": str(e),
})
processed += 1
# Keep the single-instance lock alive during long scans.
if self._heartbeat_every and processed % self._heartbeat_every == 0:
self._heartbeat()
return results
def _process_single_file(self, file_path: Path) -> dict:
@@ -384,6 +528,10 @@ class DirectoryScanner:
"total_files_new": self._total_files_new,
"total_files_modified": self._total_files_modified,
"total_files_unscannable": self._total_files_unscannable,
"instance_id": self._instance_id,
"scan_interval_seconds": self.scan_interval,
"lock_lease_seconds": self._lock_lease_seconds,
"scan_in_progress": self._scan_lock.locked(),
"input_path": str(self.input_path),
"is_running": self._running,
}