Prepare a Python environment for reproducing NLP research code by checking the repository’s declared dependencies and Python version first. The workflow records the current setup, aligns versions when needed, installs from dependency files, and finishes with a quick import check.
Align Python version and repo-declared dependencies (requirements.txt / environment.yml) before installing packages for NLP research code reproduction.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
When reproducing an NLP research repo, always align the environment to the repo’s declared dependencies first. Most failures come from Python version mismatch or installing packages without following requirements.txt / environment.yml.
environment.yml / environment.yaml (often pins Python + channels + non-pip deps)requirements.txt (pip deps)environment.yml as the base, requirements.txt as supplemental unless README says otherwise/root/python_int.txt containing:python -VV (required; Python version is often the root cause)python -m pip --versionpython -m pip freezeIf the repo expects a specific Python major/minor and the current Python does not match, it’s usually best to set up a matching environment before installing dependencies. Example: set up a fresh Python 3.11 environment (Docker / Ubuntu) with uv # Install uv apt-get update apt-get install -y --no-install-recommends curl ca-certificates rm -rf /var/lib/apt/lists/* curl -LsSf https://astral.sh/uv/0.9.7/install.sh | sh export PATH="/root/.local/bin:$PATH"
# Install Python + create a venv
uv python install 3.11.8
uv venv --python 3.11.8 /opt/py311
# Use the new Python for installs/runs
/opt/py311/bin/python -VV
/opt/py311/bin/python -m pip install -U pip setuptools wheel
Prefer installing from the repo’s dependency files (avoid random upgrades), then run a quick import/smoke test.
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