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Environment Setup Guide

Option 1: Using Conda (Recommended)

  1. Create a new conda environment:
conda create -n push_physics python=3.10
conda activate push_physics
  1. Install PyTorch 2.4.0 with CUDA support:
# For CUDA 12.1
conda install pytorch=2.4.0 torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

# For CPU only
conda install pytorch=2.4.0 torchvision torchaudio cpuonly -c pytorch
  1. Install other dependencies:
pip install -r requirements.txt

Option 2: Using pip venv

  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows
  1. Install PyTorch 2.4.0:
# For CUDA 12.1
pip install torch==2.4.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# For CPU only
pip install torch==2.4.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
  1. Install other dependencies:
pip install --upgrade pip
pip install -r requirements.txt

Verify Installation

Run the test script to verify your setup:

python -c "import torch; print(f'PyTorch version: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}')"

Expected output:

PyTorch version: 2.4.0
CUDA available: True  # If CUDA is installed

Development Setup (Optional)

  1. Install pre-commit hooks for code quality:
pip install pre-commit
pre-commit install
  1. Setup JupyterLab for development:
jupyter lab

Common Issues

CUDA Issues

  • Ensure your NVIDIA drivers are up to date
  • Required CUDA version: 12.1
  • Use nvidia-smi to verify CUDA installation

Package Conflicts

  • If you encounter package conflicts, try:
pip uninstall torch torchvision torchaudio  # Remove existing PyTorch
pip install -r requirements.txt  # Reinstall dependencies

Memory Issues

  • For large datasets, you might need to adjust:
    • Batch size in config/default.yaml
    • Number of workers in DataLoader
    • CUDA memory settings

System Requirements

  • Python 3.10 or higher
  • CUDA 11.8-12.2 (for GPU support)
  • NVIDIA GPU with 4GB+ memory (recommended)
  • 16GB RAM (minimum)
  • 30GB free disk space