Google today is announcing the release of version 0.8 of its TensorFlow open-source machine learning software. The release is significant because it supports the ability to train machine learning ...

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Google's TensorFlow machine learning system can now be distributed across multiple machines in an update, TensorFlow 0.8. The machine learning software is already distributed across hundreds of ...

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 mission ...

We have written much over the last few years about the convergence of deep learning and traditional supercomputing but as the two grow together, it is clear that the tools for one area don’t always ...

In this video from the Swiss HPC Conference, DK Panda from Ohio State University presents: Scalable and Distributed DNN Training on Modern HPC Systems. The current wave of advances in Deep Learning ...

Two months ago, Facebook’s AI Research Lab (FAIR) published some impressive training times for massively distributed visual recognition models. Today IBM is firing back with some numbers of its own.

There is no real middle ground when it comes to TensorFlow use cases. Most implementations take place either in a single node or at the drastic Google-scale, with few scalability stories in between.