Once the training data is prepared, a distributed MPI application is then used to adjust the parameters of the machine- or deep-learning model through a ‘training’ or optimization procedure. All ...

Payman Benham, Sixu Li, Irene Wang, and William Won were chosen from over 150 applicants based on their machine learning (ML) ...

Their own production testbed revealed that it could improve utilization by 1.52X and speed the frameworks listed by up to 2.72X compared to existing mechanisms for distributed machine learning. They ...

Machine learning may help manage and organize enterprise systems -- with their "highly complex interactions between systems and components, complex data access patterns and relationships." ...

Uber AI has open-sourced Fiber, a new library which aims to empower users in implementing large-scale machine learning computation on computer clusters. The main objectives of the library are to lever ...

At its core, what Run.AI offers is a new virtualization layer for distributed machine learning tasks that can across a large number of machines.

Google has updated its TensorFlow open source machine learning code to enable it to be deployed on cloud platforms and across hundreds of distributed machines. TensorFlow 0.8 now gives developers ...