Lorena S. Reyes-Rubiano hope this helps you.

Managing large experiment campaigns for optimization models and heuristics in Python involves systematic organization, robust tracking, scalable storage, and efficient workflow automation. With three years of research experience, several best practices and tools can streamline this process for reproducibility and efficiency.

Workflow Organization

A directory structure that separates configurations, raw outputs, and processed results is essential. Tools like Sacred or Hydra can help organize experiment parameters and maintain tidy records of setups and runs, while providing tools for experiment management and reproducibility.

Tracking and Reproducibility

It is crucial to log random seeds, software versions, and configuration files for every run. Use:

• Git or DVC for code and data versioning.

• YAML/JSON config files managed with Hydra or argparse.

• Sacred or MLflow to capture parameters, code snapshot, and outputs.

Data Handling and Storage

For large result sets:

• Use pandas or polars DataFrames for in-memory manipulation.

• Parquet or HDF5 for disk storage and quick loading; for SQL queries or joining many experiments, SQLite can be effective at moderate scale.

• Avoid plain .csv in favor of compressed, schema-rich formats.

Experiment Parallelization

On a laptop, use concurrent.futures, joblib, or Ray for multiprocessing. For clusters, Snakemake or Slurm-managed workflows permit scalable and portable execution across HPC environments or cloud (e.g., Google Cloud, AWS EC2 with dask-jobqueue).

Generating Tables and Plots

Seaborn, matplotlib, and pandas support publication-quality tables and plots. Automate report generation using Jupyter notebooks or nbconvert scripts to ensure reproducible outcomes for teaching and publication scenarios.

Common Pitfalls

• Not logging software, code, and library versions leads to irreproducibility.

• Relying on ad hoc CSV files causes file proliferation and data ambiguity.

• Manual run management scales poorly; automation early in project lifecycle pays off later.

By adopting modular configuration files, robust logging, and scalable data formats, large experiment campaigns remain manageable, reproducible, and easy to analyze, especially as experiments scale in size and complexity.

• Use Python scripts that iterate over parameter combinations automatically.
• Use libraries like multiprocessing, joblib, or concurrent.

Model freeze option in GAMSpy may also keep the model in memory and speeds up iterations over various parameter instances