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name: export-and-artifacts description: "Export PySR equations, inspect hall-of-fame artifacts, reload saved runs, and configure optional logging." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PySR export and artifacts

Use this sub-skill when the user already has, or is about to persist, fitted PySR equations and needs to choose rows, export equations, inspect run artifacts, reload a run, or configure search logging.

Route here for

• Reading model.equations_, model.get_best(), selected rows, and model_selection behavior.
• Calling predict(index=...), sympy(index=...), latex(index=...), latex_table(...), jax(index=...), or pytorch(index=...).
• Explaining lambda_format, sympy_format, optional jax_format/torch_format, and custom export mappings.
• Finding or inspecting hall_of_fame.csv, hall_of_fame_output*.csv, backup CSVs, and checkpoint.pkl under a PySR run directory.
• Reloading with PySRRegressor.from_file(run_directory=...) from checkpoint or CSV.
• Choosing output_directory, run_id, temp_equation_file, or TensorBoardLoggerSpec settings.

Route away from here

• If the user has not fit a model yet, start with ../fit-and-diagnose/ for fitting, operator basics, and search-quality diagnosis.
• If export fails because a custom Julia operator has no Python/SymPy/JAX/Torch equivalent, use this sub-skill for the export mapping and ../customization-and-constraints/ for the operator/loss design.
• If the model uses TemplateExpressionSpec, use this sub-skill for artifact/reload mechanics but read ../structured-expressions/ before promising SymPy, LaTeX, JAX, or PyTorch export.
• If the issue is Julia startup, parallelism, Slurm, Docker/Apptainer, or installation, route to ../runtime-and-scaling/.

Required references

1. Read references/export-reference.md for API behavior, row selection, and backend export mappings.
2. Read references/artifacts-and-reload.md before giving file-layout, checkpoint, partial-run, from_file, temp_equation_file, or TensorBoard advice.
3. Read references/troubleshooting.md when an export, prediction, reload, or artifact lookup fails.
4. Use scripts/inspect_equation_artifacts.py --help to inspect a hall-of-fame CSV without importing PySR.

Safe operating pattern

1. Identify whether the user has an in-memory fitted model, a run directory, a CSV, or only printed equations.
2. If in memory, inspect model.equations_ first. For single-output models it is a DataFrame; for multi-output models it is a list of DataFrames.
3. Select rows deliberately. Use model.get_best() for the current model_selection, or pass index= to get_best, predict, sympy, latex, jax, and pytorch when the user wants a specific Pareto-front row.
4. For custom operators, make sure extra_sympy_mappings exists before NumPy/SymPy/LaTeX/prediction, and add extra_jax_mappings or extra_torch_mappings before those optional backends.
5. For persisted runs, prefer PySRRegressor.from_file(run_directory=...). Treat the CSV plus the model-construction code as the durable artifact; treat the pickle checkpoint as convenient but version-sensitive.
6. Do not assume optional dependencies are installed. JAX, PyTorch, TensorBoard, and template-expression exports require separate capability checks.