8ee031342a
- environment.yml: Added PyTorch with CUDA 12.1, PyG (torch-geometric), and TensorBoard for neural network training - INSTALL_INSTRUCTIONS.md: Step-by-step guide for installing Miniconda and setting up the Atomizer environment 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
104 lines
3.3 KiB
YAML
104 lines
3.3 KiB
YAML
# Atomizer Conda Environment
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# ===========================
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#
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# Complete environment for Atomizer FEA optimization framework
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# Includes: Core optimization, NX/Nastran integration, Neural surrogates (AtomizerField)
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#
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# Installation:
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# conda env create -f environment.yml
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# conda activate atomizer
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#
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# Update existing environment:
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# conda env update -f environment.yml --prune
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name: atomizer
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channels:
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- pytorch
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- nvidia
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- pyg
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- conda-forge
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- defaults
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dependencies:
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# Python version (compatible with NX 2412)
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- python=3.11
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# ============================================================================
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# Core Scientific Computing
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# ============================================================================
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# numpy<2 required for pyNastran compatibility
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- numpy>=1.24.0,<2.0.0
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- scipy>=1.10.0
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- pandas>=2.0.0
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- scikit-learn>=1.3.0
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# ============================================================================
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# Optimization Framework
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# ============================================================================
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- optuna>=3.5.0
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- plotly>=5.18.0
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- matplotlib>=3.5.0
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# ============================================================================
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# FEA/Nastran Parsing
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# ============================================================================
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- h5py>=3.0.0
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# ============================================================================
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# Neural Network / AtomizerField
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# ============================================================================
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# PyTorch with CUDA support
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- pytorch>=2.0.0
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- pytorch-cuda=12.1
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# PyTorch Geometric for Graph Neural Networks
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- pyg>=2.3.0
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# TensorBoard for training visualization
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- tensorboard>=2.13.0
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# ============================================================================
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# Web Framework (Dashboard/API)
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# ============================================================================
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- fastapi>=0.109.0
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- uvicorn>=0.27.0
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- websockets>=12.0
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- pydantic>=2.5.0
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- jinja2>=3.1.3
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# ============================================================================
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# System Utilities
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# ============================================================================
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- psutil>=5.9.0
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- tqdm>=4.65.0
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# ============================================================================
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# Development Tools
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# ============================================================================
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- pytest>=7.4.0
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- pytest-cov>=4.1.0
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# ============================================================================
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# pip-only packages (not available in conda)
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# ============================================================================
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- pip
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- pip:
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# Nastran file parser (numpy<2 constraint is handled above)
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- pyNastran>=1.4.0,<1.5.0
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# Python-multipart for FastAPI file uploads
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- python-multipart>=0.0.6
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# ============================================================================
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# Post-installation notes:
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# ============================================================================
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#
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# 1. Verify installation:
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# python -c "import optuna; import pyNastran; import torch; print('All imports OK')"
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#
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# 2. Check GPU:
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# python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')"
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#
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# 3. For NX integration, ensure NX Python stubs are accessible:
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# See config.py for NX_PYTHON_STUBS path
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