The Problem

I was struggling to share my research environment with a colleague. Simply sharing requirements.txt wasn’t enough because we use conda for package management, and some packages aren’t available on PyPI.

The Solution

Export Environment with Exact Versions

# Export with exact versions (platform-specific)
conda env export > environment.yml

# Export cross-platform (recommended for sharing)
conda env export --from-history > environment.yml

Key Differences

• conda env export: Includes all dependencies with exact versions
• conda env export --from-history: Only includes explicitly installed packages

Better Approach for Sharing

Create a minimal environment.yml:

name: research-env
channels:
  - conda-forge
  - bioconda
dependencies:
  - python=3.11
  - pandas
  - numpy
  - scikit-learn
  - jupyter
  - pip
  - pip:
      - seaborn

Create Environment from File

conda env create -f environment.yml

Why This Matters

• Reproducibility: Exact environments across machines
• Collaboration: Easy environment sharing
• Version Control: Track environment changes in git

Additional Tips

1. Use --from-history for cross-platform compatibility
2. Pin major versions when stability is crucial
3. Separate development and production environments
4. Use mamba for faster environment solving

This simple change has made our research much more reproducible!