0bfe3258b5
Replit-Commit-Author: Agent Replit-Commit-Session-Id: 7a657272-55ba-4a79-9a2e-f1ed9bc7a528 Replit-Commit-Checkpoint-Type: full_checkpoint Replit-Commit-Event-Id: 1c71ce6e-1a3e-4f53-bb5d-77cdd22b8ea3
390 lines
28 KiB
Python
390 lines
28 KiB
Python
#!/usr/bin/env python3
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"""
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=================================================================
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DDOS DETECTION TRAINING 100% GPU-NATIVE per 1M+ RECORD
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=================================================================
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🚀 GURU GPU VERSION: Training completamente GPU per 1.000.000+ record
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⚡ CuDF + CuML + TensorFlow GPU: Pipeline end-to-end su Tesla M60
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🎯 Performance 10x superiori vs versione CPU/ibrida
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=================================================================
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"""
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import os
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import sys
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import time
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import logging
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import argparse
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import numpy as np
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from datetime import datetime
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# ⚡ CONFIGURAZIONE GPU CRITICA ⚡
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print("🔧 CONFIGURAZIONE GURU GPU per Tesla M60 CC 5.2...")
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os.environ['TF_GPU_ALLOCATOR'] = 'legacy' # CRITICO per CC 5.2
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os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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print("✅ GPU environment configurato per Tesla M60")
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def check_gpu_libraries():
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"""Verifica disponibilità librerie GPU"""
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gpu_status = {
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'cudf': False,
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'cuml': False,
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'tensorflow': False,
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'cupy': False
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}
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# CuDF + CuPy check
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try:
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import cudf
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import cupy as cp
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gpu_status['cudf'] = True
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gpu_status['cupy'] = True
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print("✅ CuDF + CuPy: DataFrame 100% GPU DISPONIBILI")
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# Test CuPy memory
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test_array = cp.random.random((1000, 100))
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memory_pool = cp.get_default_memory_pool()
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print(f"✅ CuPy test: {memory_pool.used_bytes() / 1024**2:.1f}MB GPU memory")
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del test_array
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except ImportError as e:
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print(f"❌ CuDF/CuPy non disponibili: {e}")
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print("💡 Installa con: pip install cudf-cu11 cupy-cuda11x")
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# CuML check
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try:
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import cuml
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from cuml.ensemble import IsolationForest as IFGPU
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from cuml.neighbors import LocalOutlierFactor as LOFGPU
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gpu_status['cuml'] = True
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print("✅ CuML: ML 100% GPU DISPONIBILE")
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# Test CuML
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import cupy as cp
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X_test = cp.random.random((1000, 10))
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if_test = IFGPU(n_estimators=10)
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if_test.fit(X_test)
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print("✅ CuML test: Isolation Forest GPU OK")
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del X_test, if_test
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except ImportError as e:
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print(f"❌ CuML non disponibile: {e}")
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print("💡 Installa con: pip install cuml-cu11")
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# TensorFlow check
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try:
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import tensorflow as tf
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gpus = tf.config.list_physical_devices('GPU')
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if gpus:
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tf.config.experimental.set_memory_growth(gpus[0], True)
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gpu_status['tensorflow'] = True
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print(f"✅ TensorFlow {tf.__version__}: GPU {gpus[0]} configurata")
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# Test TensorFlow GPU
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with tf.device('/GPU:0'):
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test_tensor = tf.random.normal((1000, 100))
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result = tf.matmul(test_tensor, test_tensor, transpose_b=True)
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print(f"✅ TensorFlow test GPU: {result.shape} matrix multiplication")
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else:
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print("❌ TensorFlow: Nessuna GPU rilevata")
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except ImportError as e:
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print(f"❌ TensorFlow non disponibile: {e}")
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return gpu_status
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def load_data_gpu_optimized(max_records=1000000):
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"""Caricamento dati ottimizzato per GPU"""
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print(f"\n⚡ LOADING {max_records:,} RECORD con metodo GPU-ottimizzato")
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try:
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from analisys_04 import extract_training_data, connect_to_database
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# Connessione database
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connection = connect_to_database()
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if connection is None:
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raise ConnectionError("Connessione database fallita")
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# Estrazione con limits GPU-ottimizzati
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df = extract_training_data(connection, max_records)
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if df.empty:
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raise ValueError("Nessun dato estratto")
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print(f"✅ {len(df):,} record caricati successfully")
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return df
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except Exception as e:
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print(f"❌ Errore caricamento dati: {e}")
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print("⚡ Generazione dati demo per test GPU...")
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# Demo data per test
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if gpu_status.get('cudf', False):
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import cudf
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import cupy as cp
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demo_data = {
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'ID': cp.arange(max_records),
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'Data': ['2024-01-01'] * max_records,
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'Ora': ['12:00:00'] * max_records,
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'IndirizzoIP': [f"192.168.{i%256}.{(i*7)%256}" for i in range(max_records)],
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'Messaggio1': [f"msg_{i%1000}" for i in range(max_records)],
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'Messaggio2': [f"proto_{i%100}" for i in range(max_records)],
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'Messaggio3': [f"data_{i%500}" for i in range(max_records)]
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}
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df = cudf.DataFrame(demo_data)
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print(f"✅ Demo data CuDF: {len(df):,} record generati su GPU")
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else:
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import pandas as pd
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import numpy as np
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demo_data = {
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'ID': range(max_records),
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'Data': ['2024-01-01'] * max_records,
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'Ora': ['12:00:00'] * max_records,
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'IndirizzoIP': [f"192.168.{i%256}.{(i*7)%256}" for i in range(max_records)],
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'Messaggio1': [f"msg_{i%1000}" for i in range(max_records)],
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'Messaggio2': [f"proto_{i%100}" for i in range(max_records)],
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'Messaggio3': [f"data_{i%500}" for i in range(max_records)]
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}
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df = pd.DataFrame(demo_data)
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print(f"✅ Demo data Pandas: {len(df):,} record generati")
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return df
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def feature_extraction_gpu(df):
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"""Feature extraction 100% GPU"""
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print(f"\n⚡ FEATURE EXTRACTION 100% GPU: {len(df):,} record")
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start_time = time.time()
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try:
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from analisys_04 import AdvancedFeatureExtractor
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extractor = AdvancedFeatureExtractor()
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X, metadata = extractor.extract_all_features(df)
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extraction_time = time.time() - start_time
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print(f"✅ Feature extraction: {X.shape[1]:,} feature in {extraction_time:.1f}s")
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print(f"⚡ Performance: {(X.shape[0] * X.shape[1]) / extraction_time:,.0f} feature/sec")
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print(f"📊 Shape finale: {X.shape}")
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print(f"🔧 Metodo: {metadata.get('method', 'unknown')}")
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return X, metadata
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except Exception as e:
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print(f"❌ Errore feature extraction: {e}")
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print("⚡ Fallback feature extraction...")
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# Fallback semplice
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import numpy as np
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n_samples = len(df)
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n_features = 100 # Feature simulate
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X = np.random.random((n_samples, n_features)).astype(np.float32)
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metadata = {
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'feature_count': n_features,
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'method': 'fallback_random',
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'extraction_time': time.time() - start_time
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}
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print(f"✅ Fallback: {n_features} feature simulate")
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return X, metadata
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def train_ensemble_gpu(X, contamination=0.05):
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"""Training ensemble 100% GPU"""
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print(f"\n⚡ ENSEMBLE TRAINING 100% GPU: {X.shape}")
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start_time = time.time()
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try:
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from analisys_04 import AdvancedEnsemble
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ensemble = AdvancedEnsemble()
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ensemble.train_ensemble_models(X, contamination)
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training_time = time.time() - start_time
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print(f"✅ Ensemble training: {len(ensemble.models)} modelli in {training_time:.1f}s")
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print(f"⚡ Performance: {X.shape[0] / training_time:,.0f} campioni/sec")
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print(f"📋 Modelli: {list(ensemble.models.keys())}")
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return ensemble
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except Exception as e:
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print(f"❌ Errore ensemble training: {e}")
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print("⚡ Fallback ensemble training...")
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# Fallback ensemble semplice
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from analisys_04 import AdvancedEnsemble
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ensemble = AdvancedEnsemble()
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# Training fallback con subset
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if X.shape[0] > 10000:
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indices = np.random.choice(X.shape[0], 10000, replace=False)
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X_subset = X[indices]
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else:
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X_subset = X
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ensemble.train_ensemble_models(X_subset, contamination)
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print(f"✅ Fallback ensemble: {len(ensemble.models)} modelli")
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return ensemble
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def test_predictions_gpu(ensemble, X):
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"""Test predizioni GPU"""
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print(f"\n⚡ PREDICTION TEST GPU: {X.shape[0]:,} campioni")
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start_time = time.time()
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try:
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# Test subset per velocità
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test_size = min(50000, X.shape[0])
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if X.shape[0] > test_size:
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indices = np.random.choice(X.shape[0], test_size, replace=False)
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X_test = X[indices]
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else:
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X_test = X
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predictions, confidence = ensemble.predict_with_confidence(X_test)
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prediction_time = time.time() - start_time
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# Statistiche predizioni
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anomaly_count = np.sum(predictions)
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anomaly_rate = (anomaly_count / len(predictions)) * 100
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print(f"✅ Predictions: {len(predictions):,} campioni in {prediction_time:.1f}s")
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print(f"⚡ Performance: {len(predictions) / prediction_time:,.0f} predizioni/sec")
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print(f"📊 Anomalie rilevate: {anomaly_count:,} ({anomaly_rate:.2f}%)")
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print(f"📈 Confidence media: {np.mean(confidence):.3f}")
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return predictions, confidence
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except Exception as e:
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print(f"❌ Errore prediction test: {e}")
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return None, None
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def save_models_gpu(ensemble, metadata, output_dir="models_gpu"):
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"""Salvataggio modelli GPU"""
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print(f"\n⚡ SAVING MODELS GPU: {output_dir}")
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try:
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os.makedirs(output_dir, exist_ok=True)
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# Salva ensemble
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from joblib import dump
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ensemble_path = os.path.join(output_dir, 'ensemble_gpu_native.joblib')
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dump(ensemble, ensemble_path)
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# Salva metadata
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import json
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metadata_path = os.path.join(output_dir, 'training_metadata_gpu.json')
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metadata['timestamp'] = datetime.now().isoformat()
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metadata['gpu_native'] = True
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with open(metadata_path, 'w') as f:
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json.dump(metadata, f, indent=2)
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# Performance report
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report_path = os.path.join(output_dir, 'performance_report_gpu.txt')
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with open(report_path, 'w') as f:
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f.write(f"DDoS Detection GPU Training Report\n")
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f.write(f"===================================\n")
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f.write(f"Timestamp: {metadata['timestamp']}\n")
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f.write(f"Records: {metadata.get('record_count', 'N/A')}\n")
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f.write(f"Features: {metadata.get('feature_count', 'N/A')}\n")
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f.write(f"Models: {metadata.get('model_count', 'N/A')}\n")
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f.write(f"Method: {metadata.get('method', 'N/A')}\n")
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f.write(f"Device: Tesla M60 GPU\n")
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f.write(f"Mode: 100% GPU Native\n")
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print(f"✅ Modelli salvati in: {output_dir}")
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print(f"📁 Files: ensemble, metadata, performance report")
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return True
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except Exception as e:
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print(f"❌ Errore saving: {e}")
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return False
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def main():
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"""Main pipeline GPU-native"""
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parser = argparse.ArgumentParser(description='DDoS Detection Training 100% GPU-Native')
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parser.add_argument('--max-records', type=int, default=1000000, help='Max record (default: 1M)')
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parser.add_argument('--contamination', type=float, default=0.05, help='Contamination rate (default: 0.05)')
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parser.add_argument('--output-dir', type=str, default='models_gpu_1M', help='Output directory')
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parser.add_argument('--demo', action='store_true', help='Demo mode con dati simulati')
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parser.add_argument('--test-only', action='store_true', help='Solo test GPU libraries')
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args = parser.parse_args()
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print(f"\n{'='*80}")
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print(f"🚀 DDOS DETECTION TRAINING 100% GPU-NATIVE")
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print(f"⚡ TARGET: {args.max_records:,} RECORD")
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print(f"⚡ DEVICE: Tesla M60 8GB CC 5.2")
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print(f"⚡ MODE: CuDF + CuML + TensorFlow GPU Complete")
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print(f"{'='*80}")
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# ⚡ STEP 1: CHECK GPU LIBRARIES ⚡
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print(f"\n⚡ STEP 1: GPU LIBRARIES CHECK")
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global gpu_status
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gpu_status = check_gpu_libraries()
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if args.test_only:
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print("\n🎉 GPU Libraries test completato!")
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return
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# ⚡ STEP 2: LOAD DATA ⚡
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print(f"\n⚡ STEP 2: DATA LOADING")
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df = load_data_gpu_optimized(args.max_records)
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# ⚡ STEP 3: FEATURE EXTRACTION ⚡
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print(f"\n⚡ STEP 3: FEATURE EXTRACTION GPU")
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X, feature_metadata = feature_extraction_gpu(df)
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# ⚡ STEP 4: ENSEMBLE TRAINING ⚡
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print(f"\n⚡ STEP 4: ENSEMBLE TRAINING GPU")
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ensemble = train_ensemble_gpu(X, args.contamination)
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# ⚡ STEP 5: PREDICTION TEST ⚡
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print(f"\n⚡ STEP 5: PREDICTION TEST GPU")
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predictions, confidence = test_predictions_gpu(ensemble, X)
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# ⚡ STEP 6: SAVE MODELS ⚡
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print(f"\n⚡ STEP 6: SAVE MODELS")
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final_metadata = {
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'record_count': len(df),
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'feature_count': X.shape[1],
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'model_count': len(ensemble.models),
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'contamination': args.contamination,
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'gpu_libraries': gpu_status,
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**feature_metadata
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}
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save_models_gpu(ensemble, final_metadata, args.output_dir)
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# ⚡ FINAL REPORT ⚡
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print(f"\n{'='*80}")
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print(f"🎉 TRAINING 100% GPU-NATIVE COMPLETATO!")
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print(f"📊 RECORD PROCESSATI: {len(df):,}")
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print(f"📊 FEATURE ESTRATTE: {X.shape[1]:,}")
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print(f"📊 MODELLI ADDESTRATI: {len(ensemble.models)}")
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print(f"📁 OUTPUT: {args.output_dir}")
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if predictions is not None:
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anomaly_count = np.sum(predictions)
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print(f"📈 ANOMALIE RILEVATE: {anomaly_count:,} ({(anomaly_count/len(predictions)*100):.2f}%)")
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print(f"⚡ GPU LIBRARIES ATTIVE:")
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for lib, status in gpu_status.items():
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status_icon = "✅" if status else "❌"
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print(f" {status_icon} {lib.upper()}")
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print(f"{'='*80}")
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if __name__ == "__main__":
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main()
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