How good is the NVIDIA GTX 1080Ti for CUDA accelerated Machine Learning workloads? About the same as the TitanX! I ran a Deep Neural Network training calculation on a million image dataset using both the new GTX 1080Ti and a Titan X Pascal GPU and got very similar runtimes.
The short answer to that question is, yes. If you want to know why you would want to do that then read on.
Does PCIe X16 give better performance than X8 for training models with Caffe when using cuDNN? Yes, but not by much!
NVIDIA’s Pascal GPU’s have twice the computational performance of the last generation. A great use for this compute capability is for training deep neural networks. We have tested NVIDIA DIGITS 4 with Caffe on 1 to 4 Titan X and GTX 1070 cards. Training was for classification of a million image data set from ImageNet. Read on to see how it went.
If you are happy to use Ubuntu 14.04 LTS (Ubuntu-MATE in our case) then setting up a system with the NVIDIA DIGITS software stack is simple. I’ll give you some guidance on getting everything working, from the Linux install to the DIGITS web interface.
Machine Learning is getting a lot of attention these days and with good reason. There are mountains of data to work with and computing resources to handle the problems are easily attainable. Even a single GPU accelerated workstation is capable of serious work.
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