In addition to distributed support, the 0.8 release comes with a distributed trainer for Google’s Inception neural network, along with code for defining how distributed models should work.

The figure below shows the entire workflow (including training, evaluation/inference and online serving) for the distributed TensorFlow on Apache Spark pipelines in Analytics Zoo.

TensorFlow 0.8 adds distributed computing support to speed up the learning process for Google's machine learning system.

Recently released TensorFlow v2.9 introduces a new API for the model, data, and space-parallel (aka spatially tiled) deep network training. DTensor aims to decouple sharding directives from the ...

Google today announced the launch of version 0.8 of TensorFlow, its open source library for doing the hard computation work that makes machine learning ...

In distributed TensorFlow, gradient updates are a critical step governing the total model training time. These updates incur a massive volume of data transfer over the network. In this talk, we first ...

The key to abstraction is the addition of two new TensorFlow operators; a global broadcast and an MPI all-reduce operator for the model results for training. Both of these additions allow MaTEx to ...

The solutions will include: 1) MPI-driven Deep Learning, 2) Co-designing Deep Learning Stacks with High-Performance MPI, 3) Out-of- core DNN training, and 4) Hybrid (Data and Model) parallelism. Case ...

Over the past year, Google’s TensorFlow has asserted itself as a popular open source toolkit for deep learning. But training a TensorFlow model can be cumbersome and slow—especially when the ...