Why it matters

Most ML deployments containerize. Understanding GPU containers unlocks production ML.

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The architecture

nvidia-container-toolkit: injects GPU driver + libraries into container at runtime.

docker run --gpus all: allocates all GPUs; can specify count or specific IDs.

GPU container stackHost driveron host OSToolkitinjects libsContainersees GPUsCUDA version compatibility: host driver must support container's CUDA version
Container GPU model.
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How it works end to end

Image: use nvidia/cuda base images with correct CUDA version.

Runtime: nvidia runtime handles device passthrough.

Kubernetes: NVIDIA device plugin schedules GPU pods.

Compatibility: forward CUDA compat (older driver can run newer CUDA app up to a point).