agi-app-pytorch-playground 2026.5.22

AGILAB PyTorch playground app for reproducible neural-network experiments

agi-app-pytorch-playground

agi-app-pytorch-playground publishes the pytorch_playground_project AGILAB app as a self-contained PyPI payload. It turns an interactive neural-network playground into an executable AGILAB app with persisted arguments, worker execution, and deterministic evidence artifacts.

Purpose

Use this package to train a small PyTorch classifier on generated visual datasets, inspect the resulting boundary/layers/loss terrain, and keep the configuration and artifacts replayable.

Installed Project

The distribution name is agi-app-pytorch-playground; the AGILAB project name is pytorch_playground_project. The package exposes both pytorch_playground and pytorch_playground_project through the agilab.apps entry point group, so AgiEnv(app="pytorch_playground_project") resolves the project without a monorepo checkout.

Install

pip install agi-app-pytorch-playground

The app project itself installs PyTorch when AGILAB prepares its project environment. The payload package stays lightweight and only exposes the project root.

Run In AGILAB

Select pytorch_playground_project, then open ANALYSIS for the app-owned PyTorch Playground surface. That surface places persisted ORCHESTRATE arguments next to the decision boundary, training curves, neuron/loss views, evidence download, and a run button that refreshes the evidence without leaving ANALYSIS.

Open ORCHESTRATE when you want the reproducible AGILAB execution path: tune the sidebar fields, then run INSTALL and RUN. Enable loss-landscape computation only when you want the heavier 3D projection in the evidence bundle.

Expected Inputs

The default run generates a synthetic dataset. No external dataset, API key, notebook, cloud service, or private model is required.

Expected Outputs

The run writes the playground config, samples, training history, decision grid, network-layer summary, activation maps, optional loss landscape, a manifest, and a deterministic evidence ZIP.

Change One Thing

Switch the dataset from circles to XOR or spiral, then rerun the app. The manifest and training-history artifacts should make the changed behavior auditable.

Scope

This is an educational reproducibility app. It is not a production trainer, model registry, serving stack, or generic app-agnostic analysis page. The PyTorch-specific UI stays inside the app project; reusable apps-pages remain optional artifact readers for shared contracts.