TorchStack

Revolutionize your neural network development with dynamic model visualization, real-time explainability, and effortless scaling.

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Elevate Your ML Infrastructure

Explainability

Our advanced visualization tools allow you to explore every layer of your neural network, helping you understand how your model processes information and makes decisions. This level of transparency is crucial for debugging, optimizing, and building trust in your AI systems.

Explainability

Peer behind the black box and gain deeper insights into your model's decision-making process.

Architectural Hyperparametrization

Effortlessly manage and experiment with hyperparameters that define your model's structure.

Rapid Scaling

Scale your models quickly with our innovative templates and optimization techniques.

Visual Manipulation

Intuitively manipulate neural networks using our advanced visual graph interface.

Real-time Training Insights

Watch your model learn in real-time with dynamic visualizations of the training process.

Seamless PyTorch Integration

Drag and drop your existing PyTorch models for instant import and enhancement.

Build Neural Networks

Drag and drop layers to create your neural network. Try placing a Linear layer between the Input and Output below!

Drag this layer to the canvas

Linear

Live Code Export

Input

Output

Linear

Dropout

ReLU

Add

Mult

Layers

Common

Input

Output

Linear

Dropout

Activation

ReLU

Operations

Add

Mult

Live Graph2Code

Drag and drop the layers to build your neural network — your changes are instantly reflected in the generated code!

Generated Code

Real-time Explainability

Understand how your model makes decisions. Watch as the CNN's internal representation evolves to classify MNIST digits.

Training Iteration: 1003D t-SNE VisualizationLoading...

Instant Model Import

Seamlessly integrate your existing PyTorch models into TorchStack. Just drag, drop, and watch your development process accelerate.

Example Files

simple_nn.py

cnn.py

transformer_block.py

import torch
import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(10, 5)
        self.fc2 = nn.Linear(5, 1)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

Drag your PyTorch model here

or click to select files

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