For everyone frustrated by how long it takes to train deep learning models, IBM has some good news: It has unveiled a way to automatically split deep-learning training jobs across multiple physical servers — not just individual GPUs, but whole systems with their own separate sets of GPUs.Now the bad news: It’s available only in IBM’s PowerAI 4.0 software package, which runs exclusively on IBM’s own OpenPower hardware systems.[ Roundup: TensorFlow, Spark MLlib, Scikit-learn, MXNet, Microsoft Cognitive Toolkit, and Caffe machine learning and deep learning frameworks. | Cut to the key news and issues in cutting-edge enterprise technology with the InfoWorld Daily newsletter. ]
Distributed Deep Learning (DDL) doesn’t require developers to learn an entirely new deep learning framework.
It repackages several common frameworks for machine learning: TensorFlow, Torch, Caffe, Chainer, and Theano.

Deep learning projecs that use those frameworks can then run in parallel across multiple hardware nodes.To read this article in full or to leave a comment, please click here

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