Notebooks and Python scripts

The three notebooks are thin entry points, not a separate implementation. Their functions live in importable Python modules. Use a kernel from the environment where you installed NNPD, .venv after uv sync.

Notebook	Purpose
01_run.ipynb	Plan, train and analyze the selected profile.
02_inspect.ipynb	Read completed results, reload models/products, call hooks, and export CSV.
03_extensions.ipynb	Analyze the trained models again with settings_analysis.py, and try new metrics on a saved run. Never trains.

Like the command line, the notebooks read settings_training.py and settings_analysis.py; to change what is trained or analyzed, edit those files. Each notebook starts with PROFILE = "smoke". Set it to "default" or "paper" in all three to work with that profile instead; 03 only needs its models to have been trained, by 01 or by python run.py train --profile <name>.

They are included in the repository under notebooks/. The website offers the same files as downloads, without executing them during documentation builds:

01_run.ipynb · 02_inspect.ipynb · 03_extensions.ipynb

The development dependencies include an IPython kernel and notebook execution tools. Use your existing notebook editor or install a notebook interface separately; NNPD does not require one for ordinary Python execution.

Equivalent script

Run this from the repository root in a normal Python file:

from nnpd import execute, plan


def main():
    jobs = plan("smoke")
    print(f"Planned models: {len(jobs)}")
    return execute("run", profile="smoke")  # or "train" / "analyze"


if __name__ == "__main__":
    main()

plan and execute read the two settings files from the current folder, or from settings_dir=..., exactly like the command line. Alternatively, use the existing run.py instead of creating a script.

Inspection does not train missing models. Run 01_run or the smoke CLI command before 02_inspect. Requiring a derived product may calculate it from compatible saved inputs; this is distinct from training. See Results for examples of restoring contexts and accessing saved arrays.

For collective training, launch the Python entry point with torchrun rather than trying to create multiple GPU worker processes from notebook cells.