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🐳 Containerization for Machine Learning Workloads

An interactive 3D view of a layered ML container image built once, cached, and replicated across a GPU-backed cluster to serve a model at scale.

AI & Machine Learning3DAdvanced60 FPS

Watch a layered ML container image get built once and replicated across a GPU-backed cluster, with each layer's cache reuse and each replica's GPU passthrough rendered live in 3D.

🔬 What It Demonstrates

Every replica is a stack of read-only layers — base OS, CUDA runtime, Python dependencies, model weights, app code. Identical layers are shared across containers via the image cache, so only the unique top layer differs per replica.

🎮 How to Use

Scale replicas, change model weight size, and toggle layer caching and GPU passthrough. Watch the pull-size stat and beam brightness respond, and compare cached vs. uncached total transfer.

💡 Did You Know?

Because image layers are content-addressed, scaling out an inference service to ten pods usually re-downloads only the small application layer — the multi-gigabyte CUDA runtime and model weights are pulled once and reused.

⚙ Under the hood

An interactive 3D view of a layered ML container image built once, cached, and replicated across a GPU-backed cluster to serve a model at scale.

machine learningcontainersgpucloud computingmodel deploymentdeep learningThree.js

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