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INTERACTIVE

Live Graph2Code: Drag-and-Drop Neural Networks

Build neural network architectures visually and watch them become production-ready PyTorch code in real time. No boilerplate, no guessing—just instant, synchronized code generation as you design.

TorchStack Labs
Interactive Research Tool

Building neural networks is still largely a "write code, run, tweak, repeat" loop. It works—but it's slow, error-prone, and not very welcoming when you're trying to explore architectures quickly (or teach someone what a model actually is).

So we built Live Graph2Code: a visual editor where you can drag-and-drop neural network layers into a graph, connect them, and immediately see the corresponding PyTorch code generated in real time.

Try It NowDrag layers from the left • Connect nodes
See code update instantly

Live Graph2Code

Input
Output
Linear
Dropout
ReLU
Add
Mult
Click to interact with graph

Generated Code

What the Tool Does

Live Graph2Code has two synchronized views that work together seamlessly:

Visual Architecture Editor

Construct models by placing and connecting components in a directed computation graph:

  • Input / Output nodes to define data flow boundaries
  • Linear layers for fully-connected transformations
  • Activations like ReLU, Sigmoid, Tanh
  • Regularization with Dropout layers
  • Operations like Add, Mult for branches and merges

Instant PyTorch Generation

On every change, the tool regenerates a valid PyTorch model skeleton:

  • Proper imports (torch, torch.nn)
  • A complete nn.Module class
  • __init__ defining all layers and modules
  • forward() that reflects your graph structure
  • A dummy input snippet for shape verification

The result is a "what you see is what you run" workflow: the graph is the source of truth, and the code is always up-to-date.

Why We Built It

Faster Experimentation

Early-stage model work is architecture exploration: try variants, branch, merge, compare. Writing code for each iteration creates friction and discourages breadth of exploration. A visual graph speeds up iteration and helps you compare ideas quickly.

Fewer "Silent" Implementation Mistakes

A surprising amount of modeling time is spent debugging issues like:

Layer ordering errors
Missing activations
Shape mismatches
Forgotten dropout/batchnorm
Accidentally reused tensors

Graph2Code makes the structure explicit. If the architecture is wrong, you see it immediately—before you even run training.

Better Learning and Collaboration

Not everyone thinks in code first. A graph editor is more accessible for:

  • Students learning basic deep learning building blocks
  • Teams collaborating across ML + product + research
  • Explaining architectures in reviews, docs, and demos

How It Works

Live Graph2Code follows a simple but powerful pipeline:

1

User builds a directed graph

Nodes represent layers/operations, and edges define tensor flow between them.

2

We validate the graph

Must be connected from Input → Output, must be acyclic (for standard feed-forward graphs), and must have compatible in/out ports.

3

We compile the graph into code

A topological ordering determines execution order. Modules are defined in __init__, and tensor operations are expressed in forward().

4

We regenerate on every edit

Changes are instantly reflected, so the code never drifts away from the architecture.

This keeps the tool responsive and makes it feel "live"—like editing a document where the compiled output updates continuously.

What You Can Do With It

Prototype Models Quickly

Start with a simple baseline (Input → Linear → Output), then iterate:

  • Add ReLU activations
  • Stack more layers
  • Introduce dropout
  • Branch and merge with Add/Mult

Use as a Teaching Tool

Demonstrate exactly how:

  • A layer becomes a PyTorch module
  • Activations shape expressivity
  • Adding depth changes structure
  • Graphs map to forward passes

Bridge to Real Projects

The generated code is a starting point you can extend:

  • Plug into your dataloader
  • Replace dummy shapes with real tensors
  • Add loss functions and optimizers
  • Export into your codebase

Why This Matters for Modern ML Development

Model building shouldn't feel like translating your idea into boilerplate. The closer the tool is to the way you think, the more time you spend on the actual research: hypotheses, experiments, and results.

Live Graph2Code is our step toward that: a live bridge between visual architecture design and production-ready PyTorch code—so you can move from idea → implementation with less friction and more confidence.

Try It Now