"Our training times are about 7-10 times faster, and our memory footprints are 2-4 times smaller than the best baseline performances of previously reported large-scale, distributed deep-learning ...

As the size of AI and machine learning models continues to increase, training requires several servers or nodes to work together in a process called distributed deep learning. When carrying out ...

BigDL is a distributed deep learning library for Apache Spark*. Using BigDL, you can write deep learning applications as Scala or Python* programs and take advantage of the power of scalable Spark ...

A new algorithm is enabling deep learning that is more collaborative and communication-efficient than traditional methods. Army researchers developed algorithms that facilitate distributed ...

Intel is looking to change that with its new open-source platform for distributed deep learning in Kubernetes, Nauta. Nauta provides a distributed computing environment for training deep learning ...

The goal of Horovod is to make distributed deep learning fast and easy to use. Horovod borrows ideas from Baidu’s draft implementation of the TensorFlow ring-allreduce algorithm and builds upon it.

Though Uber didn’t open source Michelangelo, it documented the design and best practices of implementing scalable machine learning pipelines. Horovod - Distributed Deep Learning Framework for ...

Deep learning is a form of machine learning that ... While TensorFlow has its own way of coordinating distributed training with parameter servers, a more general approach uses Open MPI (message ...

IBM has unveiled a Distributed Deep Learning software library it says has demonstrated “a leap forward in deep learning performance.” The software, available now in beta, aims to improve how ...