From Nvidia's latest GPUs to Intel's Lake Crest and Google's TPUs, there is a plethora of options for training deepnets. Should you build your own GPU rig or is it better to use the cloud? Where should you train models? What about inference at scale?
This conference will focus on some best practices for deploying deep learning models into production on a variety of hardware and cloud platforms.
Speakers will discuss topics like:
Using GPU acceleration with Pytorch to make your algorithms 2000% faster
Most developers are aware that some algorithms can be run on a GPU, instead of a CPU, and see orders of magnitude speedups. However, many people assume that:
1. Only specialist areas like deep learning are suitable for GPU
2. Learning to program a GPU takes years of developing specialist knowledge
It turns out that neither assumption is true! Nearly any non-recursive algorithm that operates on datasets of 1000+ items can be accelerated by a GPU. And recent libraries like Pytorch make it nearly as simple to write a GPU accelerated algorithm as a regular CPU algorithm.
In this talk we’ll explain what the mean-shift clustering algorithm is, and why it’s important for many data science applications. We’ll first implement it in python (with numpy), and will then show how to port it to Pytorch, showing how to get a 20x performance improvement in the process.