sciencehub-team/constellarationPublic

Evaluate a stellarator boundary

The evaluator of Proxima Fusion's ConStellaration benchmarks, constellaration 0.2.6, as the leaderboard on Hugging Face runs it, wrapped in functions that other notebooks can call.

File	What it holds
data/reference-boundary.json	Proxima's example boundary for the single-objective problems, upstream inputs/boundary.json
data/reference-boundaries.json	Proxima's two-boundary example for the MHD-stable problem, upstream inputs/boundaries.json
data/leading-front.json	The 688 feasible (L∇B, A) points of the leading MHD-stable submission

• Source: https://github.com/proximafusion/constellaration, tag v0.2.6, hugging_face_competition/inputs/. flux_compression_both_weights adapts compute_flux_compression_in_regions_of_bad_curvature from constellaration/mhd/turbulent_transport.py. MIT licence, Copyright (c) 2025-present Proxima Fusion GmbH; the notice is in UPSTREAM-LICENSE.
• Leading front: written by build_data.py of the leaderboard notebook from the leaderboard's results dataset as recorded on 25 October 2026 (submission of Eppy@EP-Hard, 23 October 2026, score 249.14). Recorded objectives only, no boundary.
• Needs: Python 3.10 to 3.13. The geometrical problem fits a 1.5 GiB ScienceHub kernel for boundaries of the dataset's size; the two QI problems need about 3 GiB, more than ScienceHub compute offers, so run them on your own machine. With the booz-xform override in the notebook's header no dependency needs a compiler on Linux x86-64 or on macOS 14 or later.
• Call it: evaluate, evaluate_many, compute_metrics, constraint_report, added_hypervolume, leader_front, feasible_points and rotating_ellipse; the notebook documents each and shows how to load them on your own machine or copy them into a ScienceHub notebook.

The booz-xform override

The notebook pins constellaration==0.2.6 and overrides one dependency: booz-xform 0.1.0 instead of 0.0.9. Release 0.0.9 publishes no wheels, and building it needs a C++ compiler, CMake and libnetcdf, which ScienceHub's kernel image does not have. With the override no package needs a compiler; two pure-Python packages (pylatexenc, nvgpu) come as source archives. On Python 3.13 the kernel image installs everything in 18 s.

The override does not change any number. The reference boundary and the paper's baselines for all three problems (eight forward-model runs) give bit-identical metrics, QI residual included, in two environments:

Environment	Python	booz-xform	simsopt	jax	scipy
ScienceHub kernel image	3.13	0.1.0	1.9.0	0.5.3	1.17.1
Leaderboard Space	3.10	0.0.9, built from source	1.8.1	0.6.0	1.15.3

The 16 runs are rows of data/rescored.parquet in the leaderboard notebook of this project, with run "kernel image" or "leaderboard environment". That notebook pairs them by boundary and reports the largest difference over their 110 metric values: 0.

Cost of one evaluation

Measured on Hetzner CPX42 servers (AMD EPYC), one core per evaluation, with VMEC++ single-threaded as upstream sets it. The kernel-image rows ran one process per evaluation on one CPU, so their memory belongs to that evaluation. Rows from the leaderboard environment ran on Python 3.10, where worker processes are reused, so their memory is an upper bound.

Problem	Boundary	Environment	Time	Peak memory
Geometrical	reference, 5 × 9 modes	kernel image	72 s	1.0 GiB
Geometrical	paper baseline	kernel image	35 s	1.0 GiB
Geometrical	top 3 entries, 13 × 25 modes	leaderboard	117–146 s	≤ 1.72 GiB
Simple-to-build	reference and paper baseline	kernel image	76–77 s	2.7 GiB
MHD-stable	paper baseline, per point	kernel image	70–120 s	2.7 GiB
MHD-stable	leading front, 24 points	leaderboard	54–127 s	≤ 3.1 GiB
Flux compression only	two leading fronts, per point	leaderboard	median 67 s, up to 252 s	≤ 1.45 GiB

The memory of the two QI problems comes from one line of the QI residual (omnigeneity/qi.py, the angle array): |B| is evaluated for every Boozer mode on a 601 × 75 grid at once, about 2,300 × 45,075 doubles per array. The largest peak, 3,136 MiB for an MHD-stable point in the leaderboard environment, sets the 3.1 GiB below which the notebook refuses the QI problems.

A ScienceHub kernel has 1.5 GiB of memory. It fits a geometrical evaluation of a boundary of the dataset's size; the largest leaderboard entries, measured at up to 1.72 GiB, may not fit.