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Minimal Python Track

If you can run a command and copy-paste, this track is for you. Goal: run an experiment and get results in minutes, without learning all the jargon up front.

Status as of March 31, 2026: the quickstart wizard and the base single-run CLI are smoke-tested for the standard NSGA-II/ZDT1 path. If you want the absolute shortest path to a first script, the Python API in docs/guide/getting-started.md is still the lightest option.

1. Install

Create a virtual environment and install VAMOS:

python -m venv .venv
source .venv/bin/activate  # Windows: .\.venv\Scripts\Activate.ps1
pip install -e ".[analysis]"

If you do not need plots, you can install just the core:

pip install -e .

2. Run the guided wizard

The quickstart wizard asks a few questions, writes a config file, and runs one experiment:

vamos quickstart

Want to see domain-flavored templates?

vamos quickstart --template list

Run a template without prompts:

vamos quickstart --template physics_design --yes --no-plot

3. Find your results

Results are stored under:

results/quickstart/<PROBLEM>/.../seed_<N>/

Key files:

  • manifest.json: requested/resolved settings, actual seed, provenance, outcome, and hashes
  • result.npz: objective, decision, constraint, population, and archive arrays
  • environment.json: bounded runtime environment details
  • pareto_front_*.png: plot output outside the canonical run directory (if enabled)

4. Re-run or change settings

Re-run the same config:

vamos --config results/quickstart/quickstart_YYYYMMDD_HHMMSS.json

Change budget or seed without editing files:

vamos --config results/quickstart/quickstart_YYYYMMDD_HHMMSS.json --max-evaluations 8000 --seed 1

5. Quick summary

List recent runs:

vamos summarize --results results/quickstart

Open the latest run folder:

vamos open-results --results results/quickstart --open

Glossary (plain language)

  • Problem: the task you want to optimize (a dataset or a math function).
  • Algorithm: the search method (default is NSGA-II).
  • Objective: the quantity you want to minimize (often two or more).
  • Pareto front: the best trade-offs found so far (no single solution is best at everything).
  • Budget: how many evaluations to spend (more budget = longer run).
  • Population size: how many candidate solutions are kept each step.
  • Seed: fixes randomness so runs are repeatable.
  • Engine: compute backend (numpy is the default).

If something fails

  • Run: vamos check to verify your install.
  • For biology/chemistry templates, install scikit-learn: pip install -e ".[examples]".
  • For plots, install the analysis extras: pip install -e ".[analysis]".

Next steps

  • docs/guide/getting-started.md for the full API overview.
  • docs/guide/cli.md for CLI details and config files.
  • docs/guide/cookbook.md for copy-paste recipes.