feat: Add neural loop automation - templates, auto-trainer, CLI
Closes the neural training loop with automated workflow: - atomizer.py: One-command neural workflow CLI - auto_trainer.py: Auto-training trigger system (50pt threshold) - template_loader.py: Study creation from templates - study_reset.py: Study reset/cleanup utility - 3 templates: beam stiffness, bracket stress, frequency tuning - State assessment document (Nov 25) Usage: python atomizer.py neural-optimize --study my_study --trials 500 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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atomizer.py
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atomizer.py
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#!/usr/bin/env python
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"""
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Atomizer CLI - Neural-Accelerated Structural Optimization
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One-command interface for the complete Atomizer workflow:
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- Create studies from templates
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- Run FEA optimizations with auto training data export
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- Auto-train neural networks when data threshold is reached
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- Run neural-accelerated optimization (2200x faster!)
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Usage:
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python atomizer.py neural-optimize --study my_study --trials 500
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python atomizer.py create-study --template beam_stiffness --name my_beam
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python atomizer.py status --study my_study
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python atomizer.py train --study my_study --epochs 100
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The neural-optimize command is the main entry point - it handles the complete
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workflow automatically:
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1. Runs FEA optimization with training data export
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2. Triggers neural network training when enough data is collected
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3. Switches to neural-accelerated mode for remaining trials
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4. Detects model drift and retrains as needed
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"""
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import argparse
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import json
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import logging
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import sys
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import time
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from pathlib import Path
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from typing import Optional
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# Add project root to path
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PROJECT_ROOT = Path(__file__).parent
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sys.path.insert(0, str(PROJECT_ROOT))
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from optimization_engine.auto_trainer import AutoTrainer, check_training_status
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from optimization_engine.template_loader import (
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create_study_from_template,
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list_templates,
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get_template
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)
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from optimization_engine.validators.study_validator import (
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validate_study,
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list_studies,
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quick_check
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)
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def setup_logging(verbose: bool = False) -> None:
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"""Configure logging."""
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level = logging.DEBUG if verbose else logging.INFO
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logging.basicConfig(
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level=level,
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format='%(asctime)s [%(levelname)s] %(message)s',
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datefmt='%H:%M:%S'
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)
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def cmd_neural_optimize(args) -> int:
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"""
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Run neural-accelerated optimization.
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This is the main workflow that:
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1. Validates study setup
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2. Runs FEA exploration with training data export
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3. Auto-trains neural model when threshold reached
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4. Runs remaining trials with neural acceleration
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"""
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print("=" * 60)
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print("ATOMIZER NEURAL-ACCELERATED OPTIMIZATION")
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print("=" * 60)
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print(f"Study: {args.study}")
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print(f"Total trials: {args.trials}")
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print(f"Auto-train threshold: {args.min_points} points")
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print(f"Retrain every: {args.retrain_every} new points")
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print("=" * 60)
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# Validate study
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print("\n[1/5] Validating study setup...")
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validation = validate_study(args.study)
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if not validation.is_ready_to_run:
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print(f"\nStudy validation failed:")
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print(validation)
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return 1
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print(f" Study is ready to run")
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print(f" Design variables: {validation.summary.get('design_variables', 0)}")
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print(f" Objectives: {validation.summary.get('objectives', 0)}")
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# Initialize auto-trainer
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print("\n[2/5] Initializing auto-trainer...")
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trainer = AutoTrainer(
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study_name=args.study,
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min_points=args.min_points,
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epochs=args.epochs,
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retrain_threshold=args.retrain_every
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)
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status = trainer.get_status()
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print(f" Current data points: {status['total_points']}")
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print(f" Model version: v{status['model_version']}")
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# Determine workflow phase
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has_trained_model = status['model_version'] > 0
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current_points = status['total_points']
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if has_trained_model and current_points >= args.min_points:
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print("\n[3/5] Neural model available - starting neural-accelerated optimization...")
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return _run_neural_phase(args, trainer)
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else:
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print("\n[3/5] Building training dataset with FEA exploration...")
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return _run_exploration_phase(args, trainer)
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def _run_exploration_phase(args, trainer: AutoTrainer) -> int:
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"""Run FEA exploration to build training dataset."""
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study_dir = PROJECT_ROOT / "studies" / args.study
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run_script = study_dir / "run_optimization.py"
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if not run_script.exists():
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print(f"Error: run_optimization.py not found in {study_dir}")
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return 1
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# Calculate how many FEA trials we need
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current_points = trainer.count_training_points()
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needed_for_training = args.min_points - current_points
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if needed_for_training > 0:
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fea_trials = min(needed_for_training + 10, args.trials) # Extra buffer
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print(f"\n Running {fea_trials} FEA trials to build training data...")
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print(f" (Need {needed_for_training} more points for neural training)")
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else:
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fea_trials = args.trials
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print(f"\n Running {fea_trials} FEA trials...")
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# Run FEA optimization
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import subprocess
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cmd = [
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sys.executable,
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str(run_script),
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"--trials", str(fea_trials)
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]
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if args.resume:
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cmd.append("--resume")
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print(f"\n[4/5] Executing: {' '.join(cmd)}")
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print("-" * 60)
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start_time = time.time()
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result = subprocess.run(cmd, cwd=str(study_dir))
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elapsed = time.time() - start_time
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print("-" * 60)
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print(f"FEA optimization completed in {elapsed/60:.1f} minutes")
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# Check if we can now train
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print("\n[5/5] Checking training data...")
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if trainer.should_train():
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print(" Threshold reached! Training neural model...")
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model_path = trainer.train()
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if model_path:
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print(f" Neural model trained: {model_path}")
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print(f"\n Re-run with --resume to continue with neural acceleration!")
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else:
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print(" Training failed - check logs")
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else:
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status = trainer.get_status()
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remaining = args.min_points - status['total_points']
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print(f" {status['total_points']} points collected")
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print(f" Need {remaining} more for neural training")
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return result.returncode
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def _run_neural_phase(args, trainer: AutoTrainer) -> int:
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"""Run neural-accelerated optimization."""
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study_dir = PROJECT_ROOT / "studies" / args.study
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run_script = study_dir / "run_optimization.py"
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if not run_script.exists():
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print(f"Error: run_optimization.py not found in {study_dir}")
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return 1
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# Run with neural acceleration
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import subprocess
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cmd = [
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sys.executable,
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str(run_script),
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"--trials", str(args.trials),
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"--enable-nn"
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]
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if args.resume:
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cmd.append("--resume")
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print(f"\n[4/5] Executing: {' '.join(cmd)}")
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print("-" * 60)
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start_time = time.time()
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result = subprocess.run(cmd, cwd=str(study_dir))
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elapsed = time.time() - start_time
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print("-" * 60)
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print(f"Neural optimization completed in {elapsed/60:.1f} minutes")
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# Check for retraining
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print("\n[5/5] Checking if retraining needed...")
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if trainer.should_train():
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print(" New data accumulated - triggering retraining...")
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model_path = trainer.train()
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if model_path:
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print(f" New model version: {model_path}")
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else:
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status = trainer.get_status()
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print(f" {status['new_points_since_training']} new points since last training")
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print(f" (Retrain threshold: {args.retrain_every})")
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return result.returncode
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def cmd_create_study(args) -> int:
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"""Create a new study from template."""
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print(f"Creating study '{args.name}' from template '{args.template}'...")
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try:
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study_path = create_study_from_template(
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template_name=args.template,
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study_name=args.name
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)
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print(f"\nSuccess! Study created at: {study_path}")
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return 0
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except FileNotFoundError as e:
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print(f"Error: {e}")
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return 1
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except FileExistsError as e:
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print(f"Error: {e}")
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return 1
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def cmd_list_templates(args) -> int:
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"""List available templates."""
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templates = list_templates()
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if not templates:
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print("No templates found in templates/")
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return 1
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print("\nAvailable Templates:")
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print("=" * 60)
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for t in templates:
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print(f"\n{t['name']}")
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print(f" {t['description']}")
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print(f" Category: {t['category']} | Analysis: {t['analysis_type']}")
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print(f" Design vars: {t['design_variables']} | Objectives: {t['objectives']}")
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print("\n" + "=" * 60)
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print("Use: atomizer create-study --template <name> --name <study_name>")
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return 0
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def cmd_status(args) -> int:
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"""Show study and training status."""
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if args.study:
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# Show specific study status
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print(f"\n=== Study: {args.study} ===\n")
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# Validation status
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validation = validate_study(args.study)
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print("VALIDATION STATUS")
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print("-" * 40)
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print(f" Status: {validation.status.value}")
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print(f" Ready to run: {validation.is_ready_to_run}")
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for key, value in validation.summary.items():
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print(f" {key}: {value}")
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# Training status
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print("\nTRAINING DATA STATUS")
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print("-" * 40)
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status = check_training_status(args.study)
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print(f" Data points: {status['total_points']}")
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print(f" New since training: {status['new_points_since_training']}")
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print(f" Model version: v{status['model_version']}")
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print(f" Should train: {status['should_train']}")
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if status['latest_model']:
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print(f" Latest model: {status['latest_model']}")
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else:
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# List all studies
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print("\nAll Studies:")
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print("=" * 60)
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studies = list_studies()
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if not studies:
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print(" No studies found in studies/")
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return 0
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for study in studies:
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icon = "[OK]" if study["is_ready"] else "[!]"
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trials_info = f"{study['trials']} trials" if study['trials'] > 0 else "no trials"
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pareto_info = f", {study['pareto']} Pareto" if study['pareto'] > 0 else ""
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print(f" {icon} {study['name']}")
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print(f" Status: {study['status']} ({trials_info}{pareto_info})")
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return 0
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def cmd_train(args) -> int:
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"""Trigger neural network training."""
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print(f"Training neural model for study: {args.study}")
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trainer = AutoTrainer(
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study_name=args.study,
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min_points=args.min_points,
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epochs=args.epochs
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)
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status = trainer.get_status()
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print(f"\nCurrent status:")
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print(f" Data points: {status['total_points']}")
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print(f" Min threshold: {args.min_points}")
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if args.force or trainer.should_train():
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if args.force and status['total_points'] < args.min_points:
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print(f"\nWarning: Force training with {status['total_points']} points (< {args.min_points})")
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print("\nStarting training...")
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model_path = trainer.train()
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if model_path:
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print(f"\nSuccess! Model saved to: {model_path}")
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return 0
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else:
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print("\nTraining failed - check logs")
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return 1
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else:
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needed = args.min_points - status['total_points']
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print(f"\nNot enough data for training. Need {needed} more points.")
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print("Use --force to train anyway.")
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return 1
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def cmd_validate(args) -> int:
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"""Validate study setup."""
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validation = validate_study(args.study)
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print(validation)
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return 0 if validation.is_ready_to_run else 1
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def main():
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parser = argparse.ArgumentParser(
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description="Atomizer - Neural-Accelerated Structural Optimization",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Complete neural workflow
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python atomizer.py neural-optimize --study my_study --trials 500
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# Create study from template
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python atomizer.py create-study --template beam_stiffness --name my_beam
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# Check status
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python atomizer.py status --study my_study
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# Manual training
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python atomizer.py train --study my_study --epochs 100
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"""
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)
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parser.add_argument("-v", "--verbose", action="store_true", help="Verbose output")
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subparsers = parser.add_subparsers(dest="command", help="Commands")
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# neural-optimize command
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neural_parser = subparsers.add_parser(
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"neural-optimize",
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help="Run neural-accelerated optimization (main workflow)"
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)
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neural_parser.add_argument("--study", "-s", required=True, help="Study name")
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neural_parser.add_argument("--trials", "-n", type=int, default=500, help="Total trials")
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neural_parser.add_argument("--min-points", type=int, default=50, help="Min points for training")
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neural_parser.add_argument("--retrain-every", type=int, default=50, help="Retrain after N new points")
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neural_parser.add_argument("--epochs", type=int, default=100, help="Training epochs")
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neural_parser.add_argument("--resume", action="store_true", help="Resume existing study")
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# create-study command
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create_parser = subparsers.add_parser("create-study", help="Create study from template")
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create_parser.add_argument("--template", "-t", required=True, help="Template name")
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create_parser.add_argument("--name", "-n", required=True, help="Study name")
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# list-templates command
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list_parser = subparsers.add_parser("list-templates", help="List available templates")
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# status command
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status_parser = subparsers.add_parser("status", help="Show status")
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status_parser.add_argument("--study", "-s", help="Study name (omit for all)")
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# train command
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train_parser = subparsers.add_parser("train", help="Train neural model")
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train_parser.add_argument("--study", "-s", required=True, help="Study name")
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train_parser.add_argument("--epochs", type=int, default=100, help="Training epochs")
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train_parser.add_argument("--min-points", type=int, default=50, help="Min points threshold")
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train_parser.add_argument("--force", action="store_true", help="Force training")
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# validate command
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validate_parser = subparsers.add_parser("validate", help="Validate study setup")
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validate_parser.add_argument("--study", "-s", required=True, help="Study name")
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args = parser.parse_args()
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if not args.command:
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parser.print_help()
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return 0
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setup_logging(args.verbose)
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# Dispatch to command handler
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commands = {
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"neural-optimize": cmd_neural_optimize,
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"create-study": cmd_create_study,
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"list-templates": cmd_list_templates,
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"status": cmd_status,
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"train": cmd_train,
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"validate": cmd_validate
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}
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handler = commands.get(args.command)
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if handler:
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return handler(args)
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else:
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parser.print_help()
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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