# ML Pipeline Docker Image: Extra dependencies not covered by workbench's pyproject.toml
#
# The Dockerfile uses a two-pass dependency install for layer isolation:
#   1. This file: Heavy ML deps that need special index URLs or aren't in pyproject.toml
#   2. pyproject.toml extraction: All workbench dependencies (auto-extracted at build time)
#   3. workbench installed with --no-deps (~20MB layer that rebuilds on every version bump)
#
# This keeps the frequently-changing workbench layer thin, while the ~2GB dependency
# layer stays cached until these pins or pyproject.toml deps change.

--index-url https://download.pytorch.org/whl/cpu
--extra-index-url https://pypi.org/simple/
torch==2.9.1
