Home▸Machine Learning & Neural Networks▸PyTorch's Dynamic Computation Graph: Why Define-by-Run Changed Research Speed

🔥 PyTorch's Dynamic Computation Graph

Interactive 3D computation graph where editing a simulated model's operations on the fly rebuilds the graph dynamically each forward pass, contrasted against a static-graph equivalent.

Machine Learning & Neural Networks3DModerate60 FPS

A 3D computation graph that tears itself down and rebuilds each forward pass — following live input values and sequence length through Python-style control flow — set against a frozen static graph that silently goes stale when its inputs change underneath it.

🔬 What It Demonstrates

In dynamic mode, changing the input value or sequence length rebuilds the graph instantly, mirroring PyTorch's define-by-run execution. In static mode, the graph is traced once and frozen, so later parameter changes no longer affect which path the data actually takes — until you recompile.

🎮 How to Use

Move the input slider past zero to flip the branch, change the sequence length to unroll more recurrent cells, then switch between Dynamic and Static mode to see the graph respond immediately or fall stale. Try autoplay to watch dynamic mode reshape itself batch after batch.

💡 Did You Know?

PyTorch's eager, define-by-run design let researchers debug models with ordinary Python tools — print statements, breakpoints, pdb — instead of a symbolic graph compiler, a productivity gain TensorFlow eventually matched by making eager execution the default in TF2.

⚙ Under the hood

Interactive 3D computation graph where editing a simulated model's operations on the fly rebuilds the graph dynamically each forward pass, contrasted against a static-graph equivalent.

pytorchcomputation-graphdynamic-graphsdeep-learningautogradmachine-learning

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install