a

• My assumption is that users will start a new project, import FLE, use env = gym.make("Lab") and then feed it some configuration, and use env.run() which would spin up the container (maybe pull down the image) and handle all of the complex handling. So the user wouldn't even need to know what was in the FLE codebase.
• this sounds good apart from our current config.json setup requiring FLE codebase knowledge, and the aforementioned .env files. Please discuss this at tonights meeting

Best practices from other Python packages:

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1. Config Files

• Ship example configs as package data (e.g. config.example.yaml, default_config.json)
• Expose a CLI command to copy these to the user’s working directory (e.g. mytool init or mytool config generate)
• Allow user to override config location via CLI flag or env var (e.g. --config path/to/config.json or MYTOOL_CONFIG=...)
• Use sensible defaults if config is missing

Examples:

• mlflow: mlflow server --default-artifact-root ... (uses default config, can override)
• dvc: dvc init creates .dvc/config
• jupyter: jupyter notebook --generate-config creates ~/.jupyter/jupyter_notebook_config.py

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2. .env and Environment Variables

• Ship a .env.example or similar template
• Document required variables in README and error messages
• Load .env automatically (using python-dotenv) if present in CWD
• Allow all config via env vars as fallback/override
• Fail with clear error if required env vars are missing

Examples:

• python-dotenv is used by many projects (e.g. openai, fastapi templates)
• prefect and mlflow both support env var config

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3. CLI Bootstrapping

• Provide a CLI command to scaffold config and .env
  (e.g. mytool init or mytool setup)
• Print next steps (e.g. “Edit .env and fill in your API keys”)

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4. Summary Table

Practice	Example Packages	FLE Should...
Ship example configs	jupyter, dvc, mlflow	Include in package data
CLI to copy/init config	jupyter, dvc	Add fle init
.env template	openai, fastapi	Include .example.env
Auto-load .env	openai, fastapi	Use python-dotenv
Env var fallback	mlflow, prefect	Support for all config
Clear error on missing	all good packages	Print helpful error messages

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TL;DR:

• Ship example configs and .env in the package
• Provide a CLI to scaffold/copy them
• Auto-load .env, allow env var overrides
• Print clear errors if missing