emergingstack/es-dev-stack
An on-premises, bare-metal solution for deploying GPU-powered applications in containers
This helps operations engineers and IT managers deploy high-performance applications that need graphics processing units (GPUs) right on their own servers. You provide a CoreOS machine with an Nvidia GPU, and it outputs a ready-to-use system for running GPU-accelerated applications like TensorFlow in isolated containers. It's for IT professionals managing on-premises infrastructure for AI or data science teams.
259 stars. No commits in the last 6 months.
Use this if you need to quickly set up a dedicated server for machine learning or scientific computing tasks that require direct GPU access, without relying on cloud services.
Not ideal if you're looking for a cloud-based solution or a managed service, or if your applications don't require GPU acceleration.
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259
Forks
43
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 02, 2016
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