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feat: Add complete requirements and installation scripts

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- requirements.txt: Added all dependencies including PyTorch,
  torch-geometric, tensorboard for neural network training

- install.bat: One-click installation script that installs all
  dependencies with proper version constraints

- train_neural.bat: Training script that runs parametric neural
  network training on collected FEA data

Usage:
  1. Double-click install.bat to install dependencies
  2. Double-click train_neural.bat to train on bracket study

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Antoine
2025-11-26 16:42:39 -05:00
parent 20cd66dff6
commit a005a4a98a
3 changed files with 285 additions and 9 deletions
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114
install.bat Normal file
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@echo off
REM ============================================================================
REM Atomizer Installation Script
REM ============================================================================
REM This script installs all dependencies for Atomizer including neural networks
REM
REM Prerequisites:
REM - Python 3.10+ installed
REM - pip available in PATH
REM
REM For GPU support, run this BEFORE the script:
REM pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
REM ============================================================================
echo.
echo ============================================================================
echo ATOMIZER INSTALLATION
echo ============================================================================
echo.
REM Check Python
python --version >nul 2>&1
if errorlevel 1 (
echo ERROR: Python not found in PATH
echo Please install Python 3.10+ and add it to PATH
pause
exit /b 1
)
echo Found Python:
python --version
echo.
REM Upgrade pip
echo Upgrading pip...
python -m pip install --upgrade pip
echo.
REM Install core dependencies
echo Installing core dependencies...
python -m pip install numpy">=1.24.0,<2.0.0" scipy pandas scikit-learn
echo.
REM Install optimization framework
echo Installing optimization framework...
python -m pip install optuna plotly matplotlib
echo.
REM Install FEA tools
echo Installing FEA/Nastran tools...
python -m pip install "pyNastran>=1.4.0,<1.5.0" h5py
echo.
REM Install web framework
echo Installing web framework...
python -m pip install fastapi uvicorn websockets pydantic python-multipart jinja2
echo.
REM Install utilities
echo Installing utilities...
python -m pip install psutil tqdm
echo.
REM Install PyTorch (CPU version by default)
echo.
echo ============================================================================
echo Installing PyTorch...
echo ============================================================================
echo.
echo NOTE: Installing CPU version. For GPU support, run:
echo pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
echo.
python -m pip install torch
echo.
REM Install PyTorch Geometric
echo Installing PyTorch Geometric (Graph Neural Networks)...
python -m pip install torch-geometric
echo.
REM Install TensorBoard
echo Installing TensorBoard...
python -m pip install tensorboard
echo.
REM Verify installation
echo.
echo ============================================================================
echo Verifying installation...
echo ============================================================================
echo.
python -c "import numpy; import optuna; import torch; import torch_geometric; print('All core imports OK!')"
if errorlevel 1 (
echo.
echo WARNING: Some packages may not have installed correctly
echo Please check the error messages above
) else (
echo.
echo ============================================================================
echo INSTALLATION COMPLETE!
echo ============================================================================
echo.
echo Atomizer is ready to use.
echo.
echo To train a neural network:
echo train_neural.bat
echo.
echo To run an optimization study:
echo cd studies\your_study
echo python run_optimization.py --run --trials 100
echo.
)
pause

65
requirements.txt
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@@ -1,13 +1,52 @@
# Core Dependencies # Atomizer - Complete Requirements
optuna>=3.5.0 # =================================
pandas>=2.0.0 # LLM-native FEA optimization framework with Neural Network acceleration
numpy>=1.24.0 #
scipy>=1.10.0 # Installation:
scikit-learn>=1.3.0 # pip install -r requirements.txt
pyNastran>=1.4.0 #
plotly>=5.18.0 # For GPU support (recommended for neural training):
# pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
# pip install torch-geometric
#
# Note: numpy<2.0 required for pyNastran compatibility
# Web Framework # ============================================================================
# Core Scientific Computing
# ============================================================================
numpy>=1.24.0,<2.0.0
scipy>=1.10.0
pandas>=2.0.0
scikit-learn>=1.3.0
# ============================================================================
# Optimization Framework
# ============================================================================
optuna>=3.5.0
plotly>=5.18.0
matplotlib>=3.5.0
# ============================================================================
# FEA/Nastran Integration
# ============================================================================
pyNastran>=1.4.0,<1.5.0
h5py>=3.0.0
# ============================================================================
# Neural Network / AtomizerField
# ============================================================================
# PyTorch - Deep learning framework
torch>=2.0.0
# PyTorch Geometric - Graph neural networks
torch-geometric>=2.3.0
# TensorBoard - Training visualization
tensorboard>=2.13.0
# ============================================================================
# Web Framework (Dashboard/API)
# ============================================================================
fastapi>=0.109.0 fastapi>=0.109.0
uvicorn>=0.27.0 uvicorn>=0.27.0
websockets>=12.0 websockets>=12.0
@@ -15,7 +54,15 @@ pydantic>=2.5.0
python-multipart>=0.0.6 python-multipart>=0.0.6
jinja2>=3.1.3 jinja2>=3.1.3
# ============================================================================
# System Utilities
# ============================================================================
psutil>=5.9.0
tqdm>=4.65.0
# ============================================================================
# Development Tools (optional) # Development Tools (optional)
# ============================================================================
pytest>=7.4.0 pytest>=7.4.0
pytest-cov>=4.1.0 pytest-cov>=4.1.0
black>=23.12.0 black>=23.12.0

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train_neural.bat Normal file
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@echo off
REM ============================================================================
REM Atomizer Neural Network Training Script
REM ============================================================================
REM Trains a parametric neural network on collected FEA data
REM
REM Usage:
REM train_neural.bat (uses default: bracket study)
REM train_neural.bat study_name (specify study name)
REM train_neural.bat study_name 200 (specify epochs)
REM ============================================================================
echo.
echo ============================================================================
echo ATOMIZER NEURAL NETWORK TRAINING
echo ============================================================================
echo.
REM Set defaults
set STUDY_NAME=bracket_stiffness_optimization_atomizerfield
set EPOCHS=100
REM Parse arguments
if not "%~1"=="" set STUDY_NAME=%~1
if not "%~2"=="" set EPOCHS=%~2
set TRAIN_DIR=atomizer_field_training_data\%STUDY_NAME%
set OUTPUT_DIR=atomizer-field\runs\%STUDY_NAME%
echo Study: %STUDY_NAME%
echo Epochs: %EPOCHS%
echo Train Dir: %TRAIN_DIR%
echo Output: %OUTPUT_DIR%
echo.
REM Check training data exists
if not exist "%TRAIN_DIR%" (
echo ERROR: Training data not found at %TRAIN_DIR%
echo.
echo Available training data directories:
dir atomizer_field_training_data /b 2>nul
echo.
pause
exit /b 1
)
REM Count trials
echo Counting training trials...
set /a count=0
for /d %%d in ("%TRAIN_DIR%\trial_*") do set /a count+=1
echo Found %count% trials
echo.
if %count% LSS 20 (
echo WARNING: Less than 20 trials. Training may not converge well.
echo Consider running more FEA simulations first.
echo.
)
REM Check PyTorch
python -c "import torch; print(f'PyTorch {torch.__version__}')" 2>nul
if errorlevel 1 (
echo ERROR: PyTorch not installed
echo Run install.bat first
pause
exit /b 1
)
REM Check for GPU
echo.
echo Checking for GPU...
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}'); print(f'Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"CPU\"}')"
echo.
REM Start training
echo ============================================================================
echo Starting training...
echo ============================================================================
echo.
echo Press Ctrl+C to stop training early (checkpoint will be saved)
echo.
cd atomizer-field
python train_parametric.py ^
--train_dir "..\%TRAIN_DIR%" ^
--epochs %EPOCHS% ^
--output_dir "runs\%STUDY_NAME%" ^
--batch_size 16 ^
--learning_rate 0.001 ^
--hidden_channels 128 ^
--num_layers 4
if errorlevel 1 (
echo.
echo Training failed! Check error messages above.
cd ..
pause
exit /b 1
)
cd ..
echo.
echo ============================================================================
echo TRAINING COMPLETE!
echo ============================================================================
echo.
echo Model saved to: %OUTPUT_DIR%\checkpoint_best.pt
echo.
echo To use the trained model in optimization:
echo cd studies\%STUDY_NAME%
echo python run_optimization.py --run --trials 100 --enable-nn --resume
echo.
pause