Distributed Tensorflow Tensorflow Dev Summit 2018 overview

This page collects available information about Distributed Tensorflow Tensorflow Dev Summit 2018 and organizes it in an easy-to-read reference format.

Key information

Igor Saprykin offers a way to train models on one machine and multiple GPUs and introduces an API that is foundational for ...

Clemens Mewald and Raz Mathias present TFX, which is an end-to-end ML platform built around

Magenta explores the role of ML in the process of creating art and music. This involves developing new deep learning and ...

Alex Passos discusses Eager Execution, which provides a simpler, more intuitive interface to

Getting the most out of Machine Learning models requires careful tuning of many knobs. In this short talk, Vijay Vasudevan ...

To efficiently train machine learning models, you will often need to scale your training to multiple GPUs, or even multiple machines ...

Context and analysis

Information related to Distributed Tensorflow Tensorflow Dev Summit 2018 can change over time. Compare new developments with public records and specialist sources.

Frequently asked questions

What information does this page include?

It includes a summary, related details, context, and links to material connected with Distributed Tensorflow Tensorflow Dev Summit 2018.

Is the information updated?

The page is generated dynamically and can incorporate newer information as its available sources are refreshed.

Consult original sources when you need to confirm an important detail.