Domi31tls
d957e72aff
- Nouveau module scripts/check_resources.py : état GPU/VRAM/RAM/CPU, require_resources() et wait_for_resources() avec polling - Intégré dans finetune_camembert_bio.py (8 Go VRAM + 8 Go RAM) - Intégré dans run_batch_silver_export.py (workers × 4 Go RAM) - Évaluateur : EVA et RAI ajoutés aux termes médicaux (score 100.0/100) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
266 lines
9.8 KiB
Python
266 lines
9.8 KiB
Python
#!/usr/bin/env python3
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"""Batch anonymisation parallèle de PDFs pour enrichir le dataset silver.
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Traite TOUS les PDFs disponibles en mode CPU (sans VLM), avec N workers
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parallèles. Chaque worker charge ses propres modèles NER.
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Reprend automatiquement là où il s'est arrêté (skip les déjà traités).
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Usage:
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python run_batch_silver_export.py # 6 workers (défaut)
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python run_batch_silver_export.py --workers 4 # 4 workers
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"""
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import sys
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import os
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import time
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import argparse
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import multiprocessing as mp
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent))
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SRC = Path("/home/dom/Téléchargements/II-1 Ctrl_T2A_2025_CHCB_DocJustificatifs (1)")
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OUTDIR = SRC / "anonymise_silver_extra"
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CONFIG = Path("/home/dom/ai/anonymisation/config/dictionnaires.yml")
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# PDFs déjà traités dans l'audit 30 (à exclure)
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ALREADY_DONE_AUDIT30 = {
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"CONSULTATION ANESTHESISTE 23060661.pdf",
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"trackare-05000272-23074376_05000272_23074376.pdf",
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"CONSULTATION ANESTHESISTE 23056022.pdf",
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"trackare-BA042686-23090597_BA042686_23090597.pdf",
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"trackare-23000862-23018396_23000862_23018396.pdf",
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"LETTRE DE SORTIE 23087212.pdf",
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"CRO 23159905.pdf",
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"trackare-99246761-23159905_99246761_23159905.pdf",
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"CONSULTATION ANESTHESISTE 23139653.pdf",
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"CRO 23160703.pdf",
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"trackare-BA192486-23127395_BA192486_23127395.pdf",
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"BACTERIO 23232115.pdf",
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"CR consultation anesth-290-23025988.pdf",
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"trackare-05012965-23060770_05012965_23060770.pdf",
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"trackare-BA065989-23102874_BA065989_23102874.pdf",
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"trackare-BA127127-23135726_BA127127_23135726.pdf",
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"trackare-99252128-23177582_99252128_23177582.pdf",
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"trackare-BA171849-23214501_BA171849_23214501.pdf",
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"trackare-17015185-23043950_17015185_23043950.pdf",
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"CRH 60_23106634.pdf",
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"trackare-00260974-23070213_00260974_23070213.pdf",
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"trackare-BA067657-23076655_BA067657_23076655.pdf",
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"trackare-05012679-23098722_05012679_23098722.pdf",
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"trackare-11004431-23124019_11004431_23124019.pdf",
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"trackare-07003136-23135847_07003136_23135847.pdf",
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"trackare-13013848-23165708_13013848_23165708.pdf",
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"trackare-03020576-23175616_03020576_23175616.pdf",
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"trackare-BA093659-23074520_BA093659_23074520.pdf",
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"trackare-14025311-23034958_14025311_23034958.pdf",
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"trackare-BA121804-23016863_BA121804_23016863.pdf",
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}
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TIMEOUT_PER_FILE = 120 # secondes max par PDF
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# Variables globales par worker (initialisées une seule fois)
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_worker_ner = None
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_worker_gliner = None
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_worker_camembert = None
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_worker_id = None
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def init_worker(worker_id):
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"""Initialise les modèles NER dans chaque worker (appelé une seule fois)."""
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global _worker_ner, _worker_gliner, _worker_camembert, _worker_id
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_worker_id = worker_id
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# Limiter les threads ONNX/OpenMP par worker pour éviter la contention
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n_threads = max(2, 32 // (mp.cpu_count() // 2)) # répartir équitablement
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os.environ["OMP_NUM_THREADS"] = str(n_threads)
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os.environ["MKL_NUM_THREADS"] = str(n_threads)
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import anonymizer_core_refactored_onnx as core # noqa: F401
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from eds_pseudo_manager import EdsPseudoManager
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from gliner_manager import GlinerManager
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from camembert_ner_manager import CamembertNerManager
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_worker_ner = EdsPseudoManager()
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_worker_ner.load()
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print(f" [W{worker_id}] EDS-Pseudo chargé", flush=True)
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_worker_gliner = GlinerManager()
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try:
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_worker_gliner.load()
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print(f" [W{worker_id}] GLiNER chargé", flush=True)
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except Exception as e:
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print(f" [W{worker_id}] GLiNER indisponible ({e})", flush=True)
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_worker_gliner = None
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_worker_camembert = CamembertNerManager()
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try:
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_worker_camembert.load()
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print(f" [W{worker_id}] CamemBERT-bio chargé", flush=True)
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except Exception as e:
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print(f" [W{worker_id}] CamemBERT-bio indisponible ({e})", flush=True)
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_worker_camembert = None
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print(f" [W{worker_id}] Prêt (threads={n_threads})", flush=True)
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def process_one_pdf(args):
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"""Traite un seul PDF. Appelé par le pool de workers."""
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pdf_path, idx, total = args
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import signal
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import anonymizer_core_refactored_onnx as core
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ogc = pdf_path.parent.name.split("_")[0]
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# Timeout via alarm
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def _timeout_handler(signum, frame):
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raise TimeoutError("Timeout")
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signal.signal(signal.SIGALRM, _timeout_handler)
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signal.alarm(TIMEOUT_PER_FILE)
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try:
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core.process_pdf(
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pdf_path=pdf_path,
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out_dir=OUTDIR,
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make_vector_redaction=False,
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also_make_raster_burn=False,
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config_path=CONFIG,
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use_hf=True,
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ner_manager=_worker_ner,
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ner_thresholds=None,
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ogc_label=ogc,
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vlm_manager=None,
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gliner_manager=_worker_gliner,
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camembert_manager=_worker_camembert,
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)
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signal.alarm(0)
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return ("OK", pdf_path.name, idx, total)
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except TimeoutError:
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signal.alarm(0)
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return ("TIMEOUT", pdf_path.name, idx, total)
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except Exception as e:
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signal.alarm(0)
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err = str(e)
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if "encrypted" in err.lower() or "password" in err.lower():
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return ("SKIP", pdf_path.name, idx, total)
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return ("ERROR", pdf_path.name, idx, total, str(e)[:100])
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def main():
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parser = argparse.ArgumentParser(description="Batch silver export parallèle")
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parser.add_argument("--workers", type=int, default=6,
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help="Nombre de workers parallèles (défaut: 6)")
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args = parser.parse_args()
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n_workers = args.workers
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# Vérification des ressources (RAM surtout — chaque worker charge ~4 Go de modèles NER)
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from scripts.check_resources import require_resources
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ram_needed = n_workers * 4
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print(f"Vérification des ressources ({n_workers} workers × ~4 Go = ~{ram_needed} Go RAM)...")
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try:
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status = require_resources(ram_free_gb=ram_needed)
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print(f" RAM OK : {status.ram_available_gb:.1f} Go disponible")
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if status.gpu_available:
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print(f" GPU : {status.gpu_name}, {status.vram_free_mb} Mo VRAM libre")
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print()
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except RuntimeError as e:
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print(f"\n{e}", file=sys.stderr)
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sys.exit(1)
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# Collecter tous les PDFs disponibles (excluant audit_30)
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all_pdfs = []
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for ogc_dir in sorted(SRC.iterdir()):
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if not ogc_dir.is_dir() or ogc_dir.name.startswith("anonymise"):
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continue
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for pdf in ogc_dir.glob("*.pdf"):
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if pdf.name not in ALREADY_DONE_AUDIT30:
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all_pdfs.append(pdf)
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all_pdfs.sort(key=lambda p: (p.parent.name, p.name))
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# Détecter les fichiers déjà traités (reprise)
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OUTDIR.mkdir(exist_ok=True)
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already_done = {
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p.name.replace(".pseudonymise.txt", ".pdf")
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for p in OUTDIR.glob("*.pseudonymise.txt")
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}
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pdfs_to_do = [p for p in all_pdfs if p.name not in already_done]
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print(f"PDFs disponibles: {len(all_pdfs)} (excl. audit_30)")
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print(f"Déjà traités: {len(already_done)}")
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print(f"Restant: {len(pdfs_to_do)}")
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print(f"Workers: {n_workers}")
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print(f"RAM par worker: ~4 Go (NER models)")
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print(f"RAM totale estimée: ~{n_workers * 4} Go\n")
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if not pdfs_to_do:
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print("Rien à faire.")
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return
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# Préparer les arguments : (pdf_path, index, total)
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tasks = [(pdf, i, len(pdfs_to_do)) for i, pdf in enumerate(pdfs_to_do, 1)]
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print(f"Chargement des modèles dans {n_workers} workers...", flush=True)
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# Créer le pool avec initialisation des modèles par worker
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# On utilise mp.Pool avec initializer pour charger les modèles une seule fois
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# Note: fork + ONNX peut poser problème, on utilise 'spawn'
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ctx = mp.get_context("spawn")
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ok = ko = skip_encrypted = skip_timeout = 0
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t0 = time.time()
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# Lancer les workers séquentiellement pour l'init (éviter pic mémoire)
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# puis traiter en parallèle
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with ctx.Pool(
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processes=n_workers,
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initializer=init_worker,
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initargs=(0,), # worker_id simplifié
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) as pool:
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for result in pool.imap_unordered(process_one_pdf, tasks, chunksize=1):
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status = result[0]
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name = result[1]
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idx = result[2]
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total = result[3]
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elapsed = time.time() - t0
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done = ok + ko + skip_encrypted + skip_timeout + 1
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if status == "OK":
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ok += 1
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rate = ok / elapsed * 3600 if elapsed > 0 else 0
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print(f"[{done}/{total}] {name} OK ({rate:.0f}/h)", flush=True)
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elif status == "TIMEOUT":
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skip_timeout += 1
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print(f"[{done}/{total}] {name} TIMEOUT", flush=True)
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elif status == "SKIP":
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skip_encrypted += 1
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print(f"[{done}/{total}] {name} SKIP (chiffré)", flush=True)
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else:
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ko += 1
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err_msg = result[4] if len(result) > 4 else "?"
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print(f"[{done}/{total}] {name} ERREUR: {err_msg}", flush=True)
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# Rapport intermédiaire toutes les 50 fichiers
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if done % 50 == 0:
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remaining = (elapsed / done) * (total - done)
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print(f"\n --- Progression: {done}/{total} | OK: {ok} | "
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f"Erreurs: {ko} | Timeout: {skip_timeout} | "
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f"Débit: {ok/elapsed*3600:.0f}/h | "
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f"Restant: {remaining/60:.0f}min ---\n", flush=True)
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elapsed = time.time() - t0
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total_pseudo = len(list(OUTDIR.glob("*.pseudonymise.txt")))
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print(f"\n{'='*60}")
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print(f"Terminé en {elapsed:.0f}s ({elapsed/60:.1f}min)")
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print(f"OK: {ok}, Chiffrés: {skip_encrypted}, Timeout: {skip_timeout}, Erreurs: {ko}")
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print(f"Total .pseudonymise.txt: {total_pseudo}")
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print(f"Débit moyen: {ok/elapsed*3600:.0f} fichiers/h")
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print(f"Sortie: {OUTDIR}")
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if __name__ == "__main__":
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main()
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