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🌐 Ray Tune Implementation: Distributed Hyperparameter Optimization

Watch a Ray Tune-style cluster fire parallel trials across a 3D loss landscape, pruning weak configurations early with ASHA-style scheduling while the best trial converges on the global minimum.

AI & Machine Learning3DAdvanced60 FPS🔥 Fire

A simulated Ray Tune cluster launches parallel trials across a 3D loss landscape, using an ASHA-style scheduler to prune weak configurations early while the strongest trial converges on the global minimum.

🔬 What It Demonstrates

The purple-to-red surface encodes loss over two hyperparameters. Trials fall from parallel workers toward the surface; the scheduler checks each one mid-flight and kills those trailing far behind the current best, mirroring how ASHA reallocates compute in real Ray Tune runs.

🎮 How to Use

Set the number of parallel workers, pick a search strategy, and tune early-stop aggressiveness to see how many trials get pruned versus reach the valley. Watch the gold beacon jump to the new best configuration as the study progresses.

💡 Did You Know?

Ray Tune's ASHA scheduler can evaluate orders of magnitude more configurations than grid search in the same wall-clock budget, because it stops unpromising trials after only a fraction of their training.

⚙ Under the hood

Watch a Ray Tune-style cluster fire parallel trials across a 3D loss landscape, pruning weak configurations early with ASHA-style scheduling while the best trial converges on the global minimum.

machine learninghyperparameter optimizationdistributed computingasha schedulingparallel trialsloss landscapeoptimizationThree.js

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