conda vs pip: Which for Python CI Environments?
conda and pip solve overlapping but different problems - and in CI the difference shows up in setup time and native libraries.
pip installs Python packages from PyPI. conda is a cross-language package and environment manager that also installs non-Python dependencies (compilers, CUDA, system libs).
| pip | conda | |
|---|---|---|
| Scope | Python packages (PyPI) | Python + native / non-Python libs |
| Environment manager | No (use venv) | Yes, built in |
| Native dependency handling | Wheels only | Strong (binary packages) |
| CI setup weight | Light | Heavier (installer + solver) |
| Typical use | Web/app code | Data science / ML / scientific |
In CI
conda shines when you need hard-to-build native dependencies (NumPy/SciPy stacks, GDAL, CUDA toolkits) handled as prebuilt binaries - it avoids fragile from-source compiles. The cost is a heavier CI setup and a historically slow solver (mamba/micromamba mitigate this). For ordinary Python apps, pip is lighter and faster to set up.
Speed up either
Cache the conda package directory or the pip wheel cache keyed on your environment.yml or requirements lock. For conda, prefer micromamba in CI for a smaller, faster install.
The verdict
Heavy native/scientific dependencies: conda (ideally micromamba). Plain Python apps: pip with a venv. Match the tool to whether your dependencies are pure-Python or native.