nlp-environment-management

NLP Environment Management

Setting up an environment for NLP research requires specific versions of deep learning libraries and often custom local modules.

Installation via Conda

If an environment.yml is provided:

# Update existing environment
conda env update -n base --file environment.yml

Or create a new one:

conda env create -f environment.yml

Troubleshooting Common Conflicts

1. Flash Attention: Requires flash-attn and often specific CUDA versions. Install using:

   pip install flash-attn --no-build-isolation
   

2. Transformers/TRL Versions: Ensure transformers and trl versions match the codebase's expectations.

3. Local Modules: If a project uses local modules, ensure they are in the PYTHONPATH:

   export PYTHONPATH=$PYTHONPATH:$(pwd)
   

Logging Environment Info

Always log the environment for reproducibility:

python -VV > python_info.txt
python -m pip freeze >> python_info.txt