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build: dependency version sweep across PyAuto ecosystem #87
Description
Overview
All five PyAuto libraries have accumulated stale dependency caps on core packages (numpy, scipy, jax, astropy, scikit-image, scikit-learn). Many caps were originally needed for platform compatibility (JAX macOS ARM64 wheels) or API stability (NumPy 2.0 breaking changes) but those constraints have been resolved upstream. This sweep updates version constraints to current releases while maintaining CI stability.
Plan
- Bump safe caps first: scipy (→1.17), scikit-image (→0.26), scikit-learn (→1.8), astropy (→7.2), remove threadpoolctl cap
- Raise numpy cap past 2.0.1 (verify
np.bool_usage in PyAutoFit) - Raise JAX cap past 0.5 — update deprecated
jax.tree_util.Partialimports in PyAutoGalaxy, raise floor to 0.4.35 for ARM64 macOS wheels - Bump secondary exact-pinned packages (SQLAlchemy, networkx, psutil) where safe; keep sampler pins stable
- Align PyAutoBuild numpy pin with ecosystem
- Run CI across all repos to validate
Detailed implementation plan
Affected Repositories
- PyAutoConf (primary — defines numpy, jax constraints)
- PyAutoFit
- PyAutoArray
- PyAutoGalaxy
- PyAutoLens
- PyAutoBuild
Work Classification
Library (all five core libraries + build tooling)
Branch Survey
| Repository | Current Branch | Dirty? |
|---|---|---|
| PyAutoConf | main | clean |
| PyAutoFit | main | clean |
| PyAutoArray | main | clean |
| PyAutoGalaxy | main | clean |
| PyAutoLens | main | clean |
| PyAutoBuild | main | clean |
Suggested branch: feature/dependency-sweep
Worktree root: ~/Code/PyAutoLabs-wt/dependency-sweep/
Implementation Steps
Tier 1 — Safe cap bumps (pyproject.toml only):
PyAutoFit/pyproject.toml: scipy<=1.15.2→<=1.17.1, threadpoolctl>=3.1.0,<=3.2.0→>=3.1.0PyAutoArray/pyproject.toml: scipy<=1.15.2→<=1.17.1, scikit-image<=0.24.0→<=0.26.0, scikit-learn<=1.5.1→<=1.8.0, astropy>=5.0,<=6.1.2→>=5.0,<=7.2.0PyAutoGalaxy/pyproject.toml: astropy>=5.0,<=6.1.2→>=5.0,<=7.2.0
Tier 2 — NumPy:
4. PyAutoConf/pyproject.toml: numpy >=1.24.0,<=2.0.1 → >=1.24.0,<3.0.0
5. Verify PyAutoFit/autofit/messages/interface.py:195 np.bool_ annotation (np.bool_ exists in NumPy 2.x; np.bool was removed)
Tier 3 — JAX:
6. PyAutoConf/pyproject.toml: jax/jaxlib >=0.4.13,<0.5.0 → >=0.4.35,<0.10.0
7. PyAutoGalaxy/autogalaxy/profiles/mass/total/jax_utils.py:35: replace from jax.tree_util import Partial as partial with from functools import partial
8. PyAutoGalaxy/autogalaxy/operate/lens_calc.py:986: same Partial fix
Tier 4 — Secondary pins:
9. PyAutoFit/pyproject.toml: SQLAlchemy ==2.0.32 → ==2.0.40, networkx ==3.1 → latest, psutil ==6.1.0 → ==6.1.1
10. Keep sampler pins (dynesty, nautilus, zeus, ultranest) unchanged
Tier 5 — PyAutoBuild:
11. Update numpy cap in PyAutoBuild notebook tooling
Key Files
PyAutoConf/pyproject.toml— numpy, jax, jaxlib constraintsPyAutoFit/pyproject.toml— scipy, threadpoolctl, SQLAlchemy, secondary pinsPyAutoArray/pyproject.toml— scipy, astropy, scikit-image, scikit-learnPyAutoGalaxy/pyproject.toml— astropyPyAutoGalaxy/autogalaxy/profiles/mass/total/jax_utils.py— JAX Partial importPyAutoGalaxy/autogalaxy/operate/lens_calc.py— JAX Partial importPyAutoBuild/— numpy pin alignment
Original Prompt
Click to expand starting prompt
The dependencies of many core libraries, for example numpy, scipy, matplotlib and ultimately JAX,
are capped or tied based on the code API. Its probably been like this for a long time, and looking to update
these dependencies is a good idea, but must be balanced against source code tweaks and updates.
Can you do an assessment of whether we can update the version of these libraries whilst maintaining a stable
build where all github actions run and autobuild works ok. Dont just focus on the core libraries listed, but
also do an assessment of other key libraries like astropy, scikit-image, scikit-learn. Prioritize simplicity if
necessary, but ultimately I think a version sweep update is long overdue.
Activity
build: raise numpy, jax, jaxlib version caps
build: raise scipy cap, relax threadpoolctl and SQLAlchemy pins
build: raise caps on scipy, astropy, scikit-image, scikit-learn
build: raise astropy cap, update JAX Partial imports for 0.5+ compat
Jammy2211 commentedon Apr 12, 2026
Smoke Test Results — 2026-04-12
All 22 passing scripts confirmed unaffected by dependency cap changes.
The 6 failures are pre-existing and unrelated to this PR:
start_here.pyscripts: staleoutput/from prior model structure (PyAutoFit#1179)transformer_classis None (pynufft not configured)PRs
Jammy2211 commentedon Apr 12, 2026
Shipped — 2026-04-12
PRs
Summary
Raised version caps across the PyAuto ecosystem: numpy <3.0.0, jax/jaxlib <0.10.0 (floor >=0.4.35 for ARM64 macOS wheels), scipy <=1.17.1, astropy <=7.2.0, scikit-image <=0.26.0, scikit-learn <=1.8.0. Relaxed threadpoolctl and SQLAlchemy pins. Updated deprecated jax.tree_util.Partial imports in PyAutoGalaxy for forward compatibility with JAX 0.5+. All 3,070 unit tests pass, CI green on all 5 repos, 22/28 smoke tests pass (6 failures pre-existing).
Session Notes