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.
This guide helps researchers and hobbyists accelerate deep learning model training by setting up a private Kubernetes GPU cluster. It takes existing deep learning algorithms and datasets, automating their extensive training in a scalable cloud-like environment using your own hardware. The ideal user is a deep learning practitioner, scientist, or hobbyist who frequently trains models and seeks to optimize this often-frustrating part of the workflow.
816 stars. No commits in the last 6 months.
Use this if you are a deep learning researcher or hobbyist who wants to automate and accelerate your model training using your own GPU hardware, avoiding the complexities of public cloud providers.
Not ideal if you prefer using managed cloud services for deep learning training, or if you are not comfortable with server administration and command-line setup for bare-metal servers.
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MIT
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Last pushed
Oct 03, 2022
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