5 min read
Quick Start
Create a project in DLWAY Studio, write or design a model, train it in the browser and export the result.
Create a project
Open the Studio and choose New Project. Pick a starting template: TensorFlow.js + WebGPU, Python Data Science, React + TypeScript, or start from an empty project.
Write or design
Use the Code Editor to write Python or JavaScript directly, or switch to the Model Builder to assemble a network visually by dragging layers onto the canvas and connecting them.
import torchimport torch.nn as nn class NeuralNetwork(nn.Module): def __init__(self): super().__init__() self.layers = nn.Sequential( nn.Linear(784, 512), nn.ReLU(), nn.Linear(512, 10), ) def forward(self, x): return self.layers(x)Train
Run training from the Code Editor or the Model Builder's Train button. Browser runs use a dedicated worker; when Jupyter · Python is selected, generated native Python executes on the shared remote kernel and streams its output back to the terminal.
Ship
Open the Deployment panel to export a quantized model, a browser bundle, or a server wrapper.
Where to go next
- Bring in data: see Data Hub
- Clean it: see PrepFlow
- Follow a worked example: see the tutorials
- Find your way around: see the Studio Tour and Keyboard shortcuts