Since Wolfram is investing in Machine learning (as I understand using MXNet) and using this nice Wolfram technology
might result in some ML production projects with or without Wolfram technology as a follow-up, a decision must be made on what
hardware infra to choose.
Hewlett Packard created a project (by the HPE Labs) on github that can be used to test several setups.
Deep Learning Benchmarking Suite Deep Learning Benchmarking Suite
(DLBS) is a set of command line tools for providing consistent and
reproducible benchmark experiments on various hardware/software
combinations. In particular, DLBS provides the following
functionality:Implements internally various deep models. Our goal is to provide same
model implementations for all supported frameworks. Deep models that
are supported include various VGGs, ResNets, AlexNet and GoogleNet
models. Benchmarks single node multi GPU configurations. Frameworks
that are now supported: BVLC Caffe, NVIDIA Caffe, Intel Caffe, Caffe2,
TensorFlow, MXNet and TensorRT. Supports inference and training
phases. Can use real data if dataset is available. Else, falls back to
synthetic data. Supports bare metal and docker environments.
If you think it’s useful you can read more on:
https://github.com/HewlettPackard/dlcookbook-dlbs