---

name: runtime-and-scaling description: "Install, start, probe, and scale PySR runtime without tuning equations." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PySR runtime and scaling

Use this sub-skill when the task is about making PySR import, start its Julia/SymbolicRegression backend, run within a resource budget, use threads/processes/Slurm safely, or call PySR's command-line test wrapper. Keep equation design, operators, constraints, losses, and objective debugging routed to customization-and-constraints; keep hall-of-fame CSVs, checkpoints, SymPy/LaTeX/JAX/Torch export, and reload work routed to export-and-artifacts.

Route within this sub-skill

Request	Use
Install PySR, choose pip/conda/source, explain first import, set JuliaCall environment variables, containers, backend checkout	references/install-runtime.md
Pick parallelism, procs, Slurm settings, batching, time/evaluation budgets, deterministic recipe	references/scaling-and-parallelism.md
Run or interpret python -m pysr commands and native test subsets	references/cli-reference.md
Diagnose import hangs, Julia package resolution, stdin/notebook hangs, GLIBCXX, worker, Slurm, and reproducibility symptoms	references/troubleshooting.md
Probe an environment without fitting a model	scripts/pysr_environment_probe.py

Non-negotiable runtime facts

• import pysr imports JuliaCall and loads the SymbolicRegression Julia backend. On a fresh environment, the first import can download or resolve Julia packages; the first fit can spend additional time compiling Julia code. Do not diagnose that alone as a failed search.
• Set JuliaCall environment variables before any import pysr or import juliacall. PySR sets sensible defaults only if JuliaCall was not already imported. For thread control use PYTHON_JULIACALL_THREADS=<n> or auto before import; JULIA_NUM_THREADS is not the PySR/JuliaCall control path.
• In notebooks, embedded shells, CI, or noninteractive runners that may not provide usable stdin, pass input_stream="devnull" to PySRRegressor and bound the run with timeout_in_seconds or max_evals.
• The default search is multithreaded. Use parallelism="serial" only for tiny checks or deterministic reproduction; use parallelism="multiprocessing" with procs=... only when the extra Julia worker startup cost is worth it.
• Slurm is launched from one Python process inside an existing allocation: parallelism="multiprocessing", cluster_manager="slurm", procs=<total allocation tasks>. Do not wrap the Python script in srun.
• Deterministic runs require all three: deterministic=True, a fixed random_state, and parallelism="serial". Seeded parallel runs remain stochastic.

Fast safe probe

The bundled probe never fits a model. Start with the no-import form when you only need package metadata and environment-variable state:

python scripts/pysr_environment_probe.py --skip-import --json

Then, when a first import/Julia initialization is acceptable:

python scripts/pysr_environment_probe.py --json
python scripts/pysr_environment_probe.py --json --check-cli

--check-cli is intentionally optional because python -m pysr --help imports the package and can trigger the same first-import Julia setup.

Minimal bounded run template

import os

# Must happen before import pysr/juliacall if overriding the default thread policy.
os.environ.setdefault("PYTHON_JULIACALL_THREADS", "auto")

import numpy as np
from pysr import PySRRegressor

rng = np.random.RandomState(0)
X = rng.randn(200, 2)
y = X[:, 0] + 2 * X[:, 1]

model = PySRRegressor(
    niterations=20,
    timeout_in_seconds=120,
    max_evals=50_000,
    binary_operators=["+", "*", "-"],
    parallelism="multithreading",
    input_stream="devnull",
    random_state=0,
)
model.fit(X, y)
print(model.equations_)

For operator or loss changes in this template, route to customization-and-constraints. For inspecting the resulting equation table or persisted files, route to export-and-artifacts.