Speed Up PyTorch by Building from Source on Ubuntu 18.04
In my experience, building PyTorch from source reduced training time from 35 seconds to 24 seconds per epoch for an AlexNet-like…
In part 1, we explained the TensorFlow-Serving architecture, showed how to export a model the official way…
(Update: Part 2 is out!)
After following a few popular tutorials such as Stian Lind Petlund’s TensorFlow-Serving 101 (Parts 1…
NVIDIA recently released CUDA 9.2 and cuDNN 7.1, which have been supported by PyTorch but not TensorFlow. To take advantage of them, here’s my working installation instructions, based on my previous post.
When you Google “Random Hyperparameter Search,” you only find guides on how to randomize learning rate, momentum, dropout, weight decay, etc. What if you also want to experiment…
NOTE: (7/23/2018) I’m primarily a PyTorch dev and am new to TensorFlow, and this is my first attempt to get it working. I will update this post to reflect changes in my understanding of the framework.
TL;DCompile: If you have the below stack, just install my wheel: