nlp-project-setup

name	nlp-project-setup
description	Set up Python environment for NLP and preference optimization projects.

NLP Project Environment Setup

Environment Requirements for SimPO

Core Dependencies

• PyTorch: Deep learning framework (torch, torchvision, torchaudio)
• Transformers: Hugging Face library for LLMs
• NumPy: Numerical computing
• SciPy: Scientific computing utilities
• tqdm: Progress bars for training loops

Optional but Recommended

• wandb: Experiment tracking
• accelerate: Distributed training
• bitsandbytes: 8-bit optimization
• Flash-Attn: Efficient attention

Installation Steps

1. Check Python Version

python --version  # Should be 3.8+
python -VV        # Detailed version info

2. Create Virtual Environment (Optional)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Core Dependencies

# PyTorch (CUDA 12.1 example)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Transformers
pip install transformers

# Other essentials
pip install numpy scipy tqdm

4. Verify Installation

python -c "import torch; print(torch.__version__)"
python -c "import transformers; print(transformers.__version__)"

Dependency Version Considerations

For SimPO Specifically

• transformers >= 4.30.0 (for AutoTokenizer, model loading)
• torch >= 1.13.0 (for modern PyTorch features)
• numpy (for .npz file saving)

Compatibility Notes

• Different CUDA versions may require different torch builds
• GPU memory requirements: typically 10-20GB for 7B models
• CPU-only mode works but is much slower

Requirements File

Create requirements.txt:

torch>=1.13.0
transformers>=4.30.0
numpy
scipy
tqdm
accelerate>=0.20.0

Then install:

pip install -r requirements.txt

Logging Installed Packages

# Save package list
python -m pip freeze > /root/python_info.txt

# Or capture with version info
python -VV >> /root/python_info.txt
python -m pip freeze >> /root/python_info.txt

Troubleshooting

CUDA/GPU Issues

# Check if CUDA available
python -c "import torch; print(torch.cuda.is_available())"

# Find CUDA version
nvidia-smi  # Shows CUDA version

# Match PyTorch to CUDA version
# Visit: https://pytorch.org/get-started/locally/

Missing Dependencies

# Install specific package
pip install <package_name>

# Or reinstall all from requirements
pip install --force-reinstall -r requirements.txt

Version Conflicts

# Show package version
pip show <package_name>

# Check compatibility
pip check