Langhalsdino/Kubernetes-GPU-Guide
This guide should help fellow researchers and hobbyists to easily automate and accelerate there deep leaning training with their own Kubernetes GPU cluster.
Provides bare-metal cluster setup automation via shell scripts and YAML configurations, targeting Ubuntu 16.04 systems with a master-worker architecture where a CPU-only control plane manages GPU-accelerated worker nodes. Includes containerization guidance for packaging deep learning workloads and leverages Kubernetes' native GPU resource scheduling with feature-gate enablement for hardware acceleration. Simplifies the deployment pipeline by reducing manual infrastructure provisioning to two steps: local algorithm development and cloud-based distributed training execution.
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Language
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License
MIT
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Last pushed
Oct 03, 2022
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