Distributed training may be necessary. If the components of a model can be partitioned and distributed to optimized nodes for processing in parallel, the time needed to train a model can be ...

This makes it possible to implement certain training methods for ML models such as Distributed Data Parallel (DDP), in which only one model replica is executed per high-speed accelerator area and ...

AWS recently announced a distributed map for Step Functions, a solution for large-scale parallel data processing. Optimized for S3, the new feature of the AWS orchestration service targets interactive ...

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 model ...

A technical paper titled “Optimizing Distributed Training on Frontier for Large Language Models” was published by researchers at Oak Ridge National Laboratory (ORNL) and Universite Paris-Saclay.

Parallel Domain envisions a world in which autonomy companies use synthetic data for most, if not all, of their training and testing needs. Today, the ratio of synthetic to real-world data varies ...