PyTorch Playground

pytorch_playground_project is AGILAB’s PyTorch-native visual learning app. It keeps the immediate decision-boundary feedback people expect from classic neural-network playgrounds, then adds the engineering pieces those playgrounds usually do not own: replayable configuration, deterministic evidence, exported artifacts, and code handoff.

Positioning

TensorFlow Playground remains the reference for a pure browser-first beginner lesson. AGILAB’s PyTorch Playground is stronger when the lesson must become a reproducible PyTorch experiment that can be replayed, inspected, archived, and handed to another engineer.

Use it when you need:

  • live play/pause boundary learning and a deterministic Train / refresh evidence path in the same UI, with Run instant demo as the one-click boundary-first route;

  • a boundary-first panel that uses a WebGL-first agi-web island with Canvas2D fallback for fluid decision-surface interaction, local epoch scrubbing, play/pause replay, and hover probability readouts, plus a confidence HUD, clickable replay timeline, keyboard scrubbing, and glowing uncertainty contour while keeping Plotly detail tabs for evidence inspection;

  • preset lessons for circles, XOR feature engineering, spiral capacity, and a gaussian sanity check;

  • boundary snapshots, training curves, hidden-neuron activation maps, network diagnostics, and optional 3D loss terrain;

  • a shareable replay token, evidence ZIP, manifest, and generated plain PyTorch or PyTorch Lightning scripts.

What It Adds Over A Classic Playground

Capability

Classic browser playground

AGILAB PyTorch Playground

Teaching feedback

Immediate visual boundary changes

Immediate visual boundary changes plus bounded live play/pause ticks

Framework handoff

Mostly educational visualization

Real PyTorch configuration and generated PyTorch/Lightning code

Replay

Manual knob recreation

URL replay token and persisted ORCHESTRATE arguments

Evidence

Screenshot or manual notes

Manifest, CSV artifacts, boundary snapshots, model diagnostics, and ZIP

Engineering route

Browser-only lesson

Local Streamlit, hosted Hugging Face surface, and AGILAB app execution

One-Minute Demo Route

agilab app surface pytorch_playground_project --list
agilab app surface pytorch_playground_project --ui streamlit

Then:

  1. Keep Instant wow: clean circles and press Run instant demo.

  2. Scrub or play the boundary replay, hover the surface, then try the XOR lesson card.

  3. Switch Training mode to Live play/pause and use Step or Play to watch the boundary form.

  4. Remove then restore x1_x2 in Features to show why nonlinear features matter.

  5. Download the evidence pack and copy the replay token from Evidence pack.

Hosted Route

Use the hosted surface when you want the browser-first demo:

Public Space page: https://huggingface.co/spaces/jpmorard/agilab

agilab app surface pytorch_playground_project --ui hf

The shortcut is equivalent:

agilab pytorch-playground --backend hf

Scope

This app is an educational and engineering-prototype playground. It is not a model registry, serving stack, or production trainer. Its value is that the visual lesson can become a reproducible AGILAB app run with inspectable artifacts.

See also: