siddie

Setting up a GPU server with scheduling and containers

Our group recently acquired a new server to do some deep learning: a SuperMicro 4029GP-TRT2, stuffed with 8x NVidia RTX 2080 Ti. Though maybe a bit overpowered, with upcoming networks like BigGAN and fully 3D networks, as well as students joining our group, this machine will be used quite a lot in the future.

One challenge is, is how to manage these GPUs. There are many approaches, but given that most PhD candidates aren't sysadmins, these range from 'free-for-all', leading to one person hogging all GPUs for weeks due to a bug in the code, to Excel sheets that noone understands and noone adheres to because changing GPU ids in code is hard. This leads to a lot of frustration, low productivity and under-utilisation of these expensive servers. Another issue is conflicting software versions. TensorFlow and Keras, for example, tend to do breaking API changes every now and then. As t

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1. What does the Rendering Engine Need?

• Rendering Engine
  • Support for Multiple Graphics APIs : OpenGL | Vulkan | DirectX12 | DirectX11 | Metal
  • Content Export Pipeline : Create Maya/Max Plugins to export meshes based on Renderers needs. (Assimp Commercial Licence -> Pay)
  • Texture Compression Libraries
  • Material System : Artists Configure shaders, textures, parameters to import in game
  • Game-side Manager of Models/Materials/Lights
  • Good Visibility System (Frustum/Occlusion) (VisibilityBuffers?)
  • Multi-Threded Submission System to reduce cost of submission to GPU
• Lighting/Shadow Rendering System

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# MIT License

# Copyright (c) 2016 Chandler Abraham

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

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##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman