#!/usr/bin/env python3 """ Ingénieur Automatique pour Développement IA Système intelligent de développement, optimisation et déploiement d'IA """ import asyncio import logging import json import re import ast import inspect from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass from datetime import datetime import hashlib import subprocess import sys import os @dataclass class AIPipeline: """Pipeline de développement IA""" name: str version: str components: Dict[str, Any] dependencies: List[str] performance_metrics: Dict[str, float] training_history: List[Dict] @dataclass class CodeAnalysis: """Analyse de code IA""" quality_score: float issues: List[Dict] optimizations: List[Dict] security_concerns: List[str] performance_recommendations: List[str] class AutomaticAIEngineer: """ Ingénieur automatique pour le développement d'IA """ def __init__(self): self.logger = logging.getLogger("ai_engineer") self.pipelines: Dict[str, AIPipeline] = {} self.code_bases: Dict[str, Any] = {} # Templates d'architectures IA self.ai_templates = { "neural_network": { "type": "deep_learning", "framework": "pytorch", "structure": self._get_nn_template(), "dependencies": ["torch", "torchvision", "numpy"] }, "transformer": { "type": "nlp", "framework": "transformers", "structure": self._get_transformer_template(), "dependencies": ["transformers", "torch", "tokenizers"] }, "computer_vision": { "type": "cv", "framework": "opencv_pytorch", "structure": self._get_cv_template(), "dependencies": ["torch", "opencv-python", "pillow"] }, "reinforcement_learning": { "type": "rl", "framework": "stable_baselines3", "structure": self._get_rl_template(), "dependencies": ["stable-baselines3", "gym", "numpy"] } } # Règles d'optimisation IA self.optimization_rules = { "performance": { "batch_size": "Ajustement dynamique selon la mémoire disponible", "learning_rate": "Scheduling adaptatif", "architecture": "Optimisation des couches et connexions" }, "memory": { "gradient_checkpointing": "Réduction mémoire pendant l'entraînement", "mixed_precision": "Utilisation de float16 quand possible", "model_pruning": "Élagage des poids non essentiels" }, "training": { "early_stopping": "Arrêt automatique si sur-entraînement", "data_augmentation": "Augmentation automatique des données", "cross_validation": "Validation croisée intégrée" } } async def create_ai_pipeline(self, pipeline_type: str, requirements: Dict) -> Dict[str, Any]: """Crée un pipeline IA automatique basé sur les requirements""" try: if pipeline_type not in self.ai_templates: return { "success": False, "error": f"Type de pipeline non supporté: {pipeline_type}", "available_types": list(self.ai_templates.keys()) } template = self.ai_templates[pipeline_type] pipeline_id = f"{pipeline_type}_{hashlib.md5(str(requirements).encode()).hexdigest()[:8]}" # Génération du code IA generated_code = await self._generate_ai_code(template, requirements) # Création des fichiers file_structure = await self._create_project_structure(pipeline_id, generated_code, template) # Installation des dépendances dependencies_result = await self._install_dependencies(template['dependencies']) pipeline = AIPipeline( name=pipeline_id, version="1.0.0", components=generated_code, dependencies=template['dependencies'], performance_metrics={}, training_history=[] ) self.pipelines[pipeline_id] = pipeline return { "success": True, "pipeline_id": pipeline_id, "files_created": file_structure, "dependencies_installed": dependencies_result, "next_steps": await self._get_next_steps(pipeline_type), "code_examples": await self._get_usage_examples(pipeline_type) } except Exception as e: self.logger.error(f"Erreur création pipeline: {e}") return {"success": False, "error": str(e)} async def analyze_ai_code(self, code: str, code_type: str = "python") -> CodeAnalysis: """Analyse et optimise du code IA automatiquement""" analysis = CodeAnalysis( quality_score=0.0, issues=[], optimizations=[], security_concerns=[], performance_recommendations=[] ) try: # Analyse syntaxique syntax_issues = await self._check_syntax(code, code_type) analysis.issues.extend(syntax_issues) # Analyse des performances performance_analysis = await self._analyze_performance(code) analysis.performance_recommendations.extend(performance_analysis) # Vérification de sécurité security_checks = await self._check_security(code) analysis.security_concerns.extend(security_checks) # Optimisations IA spécifiques ai_optimizations = await self._optimize_ai_code(code) analysis.optimizations.extend(ai_optimizations) # Calcul du score de qualité analysis.quality_score = await self._calculate_quality_score(analysis) return analysis except Exception as e: self.logger.error(f"Erreur analyse code: {e}") analysis.issues.append({"type": "analysis_error", "message": str(e)}) return analysis async def auto_train_model(self, pipeline_id: str, dataset_config: Dict) -> Dict[str, Any]: """Lance l'entraînement automatique du modèle IA""" try: if pipeline_id not in self.pipelines: return {"success": False, "error": "Pipeline non trouvé"} pipeline = self.pipelines[pipeline_id] # Préparation des données data_prep = await self._prepare_training_data(dataset_config) # Configuration de l'entraînement training_config = await self._auto_configure_training(pipeline, dataset_config) # Lancement de l'entraînement training_result = await self._execute_training(pipeline_id, training_config) # Analyse des résultats performance_metrics = await self._analyze_training_results(training_result) # Mise à jour du pipeline pipeline.performance_metrics = performance_metrics pipeline.training_history.append({ "timestamp": datetime.now().isoformat(), "config": training_config, "results": training_result, "metrics": performance_metrics }) return { "success": True, "training_id": f"train_{pipeline_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}", "config": training_config, "results": training_result, "metrics": performance_metrics, "recommendations": await self._get_training_recommendations(performance_metrics) } except Exception as e: self.logger.error(f"Erreur entraînement: {e}") return {"success": False, "error": str(e)} async def optimize_model(self, pipeline_id: str, optimization_target: str = "performance") -> Dict[str, Any]: """Optimisation automatique du modèle IA""" try: pipeline = self.pipelines[pipeline_id] optimizations = [] if optimization_target == "performance": optimizations = await self._optimize_performance(pipeline) elif optimization_target == "memory": optimizations = await self._optimize_memory(pipeline) elif optimization_target == "accuracy": optimizations = await self._optimize_accuracy(pipeline) else: optimizations = await self._optimize_all(pipeline) # Application des optimisations applied_optimizations = await self._apply_optimizations(pipeline_id, optimizations) return { "success": True, "optimizations_proposed": optimizations, "optimizations_applied": applied_optimizations, "performance_improvement": await self._measure_improvement(pipeline_id) } except Exception as e: self.logger.error(f"Erreur optimisation: {e}") return {"success": False, "error": str(e)} async def deploy_model(self, pipeline_id: str, deployment_target: str = "huggingface") -> Dict[str, Any]: """Déploiement automatique du modèle IA""" try: pipeline = self.pipelines[pipeline_id] deployment_config = { "huggingface": await self._prepare_huggingface_deployment(pipeline), "api": await self._prepare_api_deployment(pipeline), "mobile": await self._prepare_mobile_deployment(pipeline) } if deployment_target not in deployment_config: return { "success": False, "error": f"Cible de déploiement non supportée: {deployment_target}", "supported_targets": list(deployment_config.keys()) } deployment_steps = deployment_config[deployment_target] deployment_result = await self._execute_deployment(pipeline_id, deployment_steps) return { "success": True, "deployment_target": deployment_target, "steps": deployment_steps, "result": deployment_result, "access_urls": await self._get_deployment_urls(pipeline_id, deployment_target) } except Exception as e: self.logger.error(f"Erreur déploiement: {e}") return {"success": False, "error": str(e)} async def debug_ai_model(self, pipeline_id: str, issue_description: str) -> Dict[str, Any]: """Débogage automatique des modèles IA""" try: analysis = await self._analyze_issues(pipeline_id, issue_description) fixes = await self._generate_fixes(analysis) applied_fixes = await self._apply_fixes(pipeline_id, fixes) return { "success": True, "issue_analysis": analysis, "proposed_fixes": fixes, "applied_fixes": applied_fixes, "verification": await self._verify_fixes(pipeline_id) } except Exception as e: self.logger.error(f"Erreur débogage: {e}") return {"success": False, "error": str(e)} # Méthodes d'implémentation async def _generate_ai_code(self, template: Dict, requirements: Dict) -> Dict[str, str]: """Génère du code IA basé sur le template et les requirements""" code_files = {} if template['type'] == 'deep_learning': code_files = { "model.py": self._generate_model_architecture(requirements), "train.py": self._generate_training_script(requirements), "config.py": self._generate_config_file(requirements), "utils.py": self._generate_utility_functions(requirements) } elif template['type'] == 'nlp': code_files = { "model.py": self._generate_transformer_model(requirements), "tokenizer.py": self._generate_tokenizer_script(requirements), "train.py": self._generate_nlp_training(requirements) } return code_files async def _create_project_structure(self, pipeline_id: str, code_files: Dict, template: Dict) -> List[str]: """Crée la structure de projet pour le pipeline IA""" project_path = f"projects/{pipeline_id}" os.makedirs(project_path, exist_ok=True) created_files = [] for filename, content in code_files.items(): filepath = os.path.join(project_path, filename) with open(filepath, 'w', encoding='utf-8') as f: f.write(content) created_files.append(filepath) # Création du fichier requirements requirements_file = os.path.join(project_path, "requirements.txt") with open(requirements_file, 'w') as f: for dep in template['dependencies']: f.write(f"{dep}\n") created_files.append(requirements_file) return created_files async def _install_dependencies(self, dependencies: List[str]) -> Dict[str, Any]: """Installe les dépendances automatiquement""" results = {} for dep in dependencies: try: # Simulation d'installation - dans la réalité on utiliserait subprocess results[dep] = {"status": "success", "version": "latest"} except Exception as e: results[dep] = {"status": "failed", "error": str(e)} return results def _get_nn_template(self) -> str: """Template de réseau de neurones""" return ''' import torch import torch.nn as nn class NeuralNetwork(nn.Module): def __init__(self, input_size, hidden_sizes, output_size, dropout=0.3): super(NeuralNetwork, self).__init__() layers = [] prev_size = input_size for i, hidden_size in enumerate(hidden_sizes): layers.append(nn.Linear(prev_size, hidden_size)) layers.append(nn.ReLU()) layers.append(nn.Dropout(dropout)) prev_size = hidden_size layers.append(nn.Linear(prev_size, output_size)) self.network = nn.Sequential(*layers) def forward(self, x): return self.network(x) # Configuration automatique def auto_configure_model(input_dim, output_dim, complexity='medium'): if complexity == 'simple': hidden_layers = [64, 32] elif complexity == 'medium': hidden_layers = [128, 64, 32] else: # complex hidden_layers = [256, 128, 64, 32] return NeuralNetwork(input_dim, hidden_layers, output_dim) ''' def _get_transformer_template(self) -> str: """Template de modèle Transformer""" return ''' from transformers import AutoModel, AutoTokenizer import torch.nn as nn class TransformerClassifier(nn.Module): def __init__(self, model_name='bert-base-uncased', num_classes=2, dropout=0.1): super(TransformerClassifier, self).__init__() self.transformer = AutoModel.from_pretrained(model_name) self.dropout = nn.Dropout(dropout) self.classifier = nn.Linear(self.transformer.config.hidden_size, num_classes) def forward(self, input_ids, attention_mask): outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask) pooled_output = outputs.pooler_output output = self.dropout(pooled_output) return self.classifier(output) # Utilisation automatique def create_transformer_model(task_type='classification', model_size='base'): model_map = { 'base': 'bert-base-uncased', 'large': 'bert-large-uncased', 'distilled': 'distilbert-base-uncased' } return TransformerClassifier(model_name=model_map[model_size]) ''' async def _analyze_performance(self, code: str) -> List[str]: """Analyse les performances du code IA""" recommendations = [] # Détection de patterns non optimaux patterns = { "for loops": "Remplacez les boucles Python par des opérations vectorisées", "explicit loops": "Utilisez torch.optimisé ou numpy vectorisé", "memory copy": "Évitez les copies inutiles de tenseurs", "device transfer": "Minimisez les transferts CPU/GPU" } for pattern, recommendation in patterns.items(): if pattern in code.lower(): recommendations.append(recommendation) return recommendations async def _optimize_ai_code(self, code: str) -> List[Dict]: """Propose des optimisations pour le code IA""" optimizations = [] # Optimisations automatiques détectées if "for i in range" in code and "torch" in code: optimizations.append({ "type": "vectorization", "description": "Remplacer la boucle par des opérations vectorisées PyTorch", "priority": "high", "estimated_improvement": "70%" }) if "model.eval()" not in code and "with torch.no_grad()" not in code: optimizations.append({ "type": "inference_optimization", "description": "Ajouter model.eval() et torch.no_grad() pour l'inférence", "priority": "medium", "estimated_improvement": "30%" }) return optimizations # Exemple d'utilisation async def main(): engineer = AutomaticAIEngineer() # Création d'un pipeline IA result = await engineer.create_ai_pipeline("neural_network", { "input_size": 784, "output_size": 10, "complexity": "medium", "task": "classification" }) print("Pipeline créé:", result) if __name__ == "__main__": asyncio.run(main())