Nlp environment setup
[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.
npx -y skills add cxcscmu/SkillLearnBench --skill nlp-environment-setupAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
1.5 KB, as published. Nobody here has run it
name: nlp-environment-setup description: Steps to set up and verify a Python environment for NLP research projects. Use this skill when you need to install dependencies, resolve conflicts, and log environment information for reproducibility.
NLP Environment Setup Guide
Setting up a robust environment is crucial for reproducibility in NLP research.
Steps for Environment Setup
- Review Requirements: Analyze
environment.ymlandrequirements.txtto identify the needed Python version and packages. - Environment Creation: Use
condaorpipas appropriate.- For
environment.yml:conda env create -f environment.yml(if available). - Alternatively, install from the
pipsection directly.
- For
- Handle Conflicts: If there are version conflicts:
- Identify the conflicting package.
- Use
pip install <package>==<version>to force a specific version. - For flash-attention or specialized packages, use appropriate flags (e.g.,
--no-build-isolation).
- Verification: After installation, run
python -VVandpython -m pip freezeto verify the state. - Logging: Always log the verified environment state to a file (e.g.,
python_info.txt) for later reference.
Environment Logging Script
python -VV > python_info.txt
python -m pip freeze >> python_info.txt
Success Criteria
- All required packages are installed without errors.
- Python version matches the project requirement.
- Essential libraries like
torch,transformers,trl, andaccelerateare present.