Env And Assets Bootstrap

display_name: Rigor Setup
short_description: Rigor Setup mode for conservative environment and asset assumptions before a reproduction run.
default_prompt: Prepare a conservative conda-first environment plus checkpoint, dataset, and cache assumptions for this README-documented reproduction target before any run.


# Assets Policy

## Goal

Prepare checkpoints, datasets, and caches conservatively and transparently.

## Order of evidence

1. README links and paths
2. config files and default arguments
3. code-level constants or path joins
4. careful inference from filenames

## Behavior

- prefer documented asset sources
- preserve source URLs or identifiers when recording downloads
- avoid mirroring unofficial files unless the project explicitly points to them
- never claim an asset is the canonical one unless the repository or primary paper source supports it

## Common asset groups

- model checkpoints
- tokenizer files
- dataset archives or prepared splits
- cache directories
- output directories

## Reporting

Record:

- requested asset
- source
- target local path
- status: present, missing, downloaded, skipped, unknown


# Environment Policy

## Default preference

Prefer conda or Anaconda-style setup for deep learning research repositories because it is common in research code and helps isolate conflicting dependencies.

## Order of trust

1. README environment instructions
2. repository environment files
3. package metadata files
4. conservative inference from imports or script names

## OS guidance

- Linux is the default reference environment.
- Support Windows and macOS where practical.
- If a repository is clearly Linux-only, record that rather than pretending otherwise.
- When virtualenv activation is needed, emit platform-specific commands instead of a fake one-size-fits-all activation step.
- Prefer Python entrypoints over shell-only helpers when the same setup logic should run on Windows, macOS, and Linux.

## Dependency handling

- prefer existing `environment.yml`
- otherwise translate README requirements into a simple conda-plus-pip setup
- avoid aggressive upgrades unless needed for a verified fix
- record version uncertainty explicitly

## Out of scope by default

- container orchestration
- cluster schedulers
- custom CUDA builds unless clearly required


#!/usr/bin/env python3
"""Bootstrap a conservative research environment on Windows, macOS, or Linux."""

from __future__ import annotations

import argparse
import shutil
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional

from plan_setup import ENV_FILES, find_first, parse_env_name, venv_activation_commands


CONDA_ENV_FILES = {"environment.yml", "environment.yaml", "conda.yml"}


def format_command(command: Iterable[str]) -> str:
    return " ".join(str(part) for part in command)


def run_command(command: List[str], *, cwd: Path, dry_run: bool) -> None:
    print(f"+ {format_command(command)}")
    if dry_run:
        return
    subprocess.run(command, cwd=cwd, check=True)


def choose_manager(preferred: str) -> Optional[str]:
    if preferred != "auto":
        if shutil.which(preferred):
            return preferred
        raise FileNotFoundError(f"Requested manager `{preferred}` was not found on PATH.")

    for candidate in ["conda", "mamba"]:
        if shutil.which(candidate):
            return candidate
    return None


def venv_python(env_dir: Path) -> Path:
    if sys.platform.startswith("win"):
        return env_dir / "Scripts" / "python.exe"
    return env_dir / "bin" / "python"


def print_activation_instructions(env_name: Optional[str], using_conda: bool) -> None:
    if using_conda:
        target = env_name or "<env-name>"
        print(f"Activate with: conda activate {target}")
        return

    print("Activate the virtualenv with one of:")
    for item in venv_activation_commands():
        platforms = ", ".join(item.get("platforms", []))
        print(f"  [{platforms}] {item['comman