Python apex.parallel.DDP is a module provided by the Apex library that stands for Distributed Data Parallel. It is used for achieving parallel training in PyTorch multi-GPU setups. DDP allows the user to parallelize the training process across multiple GPUs, with each GPU maintaining a copy of the model and computing gradients independently. This module helps in reducing the training time by distributing the workload and effectively utilizing the available GPU resources. It also handles communication and synchronization between the GPUs, ensuring consistent updates to the model parameters. With DDP, users can take advantage of multi-GPU systems to accelerate the training of deep learning models in Python.
Python DDP - 30 examples found. These are the top rated real world Python examples of apex.parallel.DDP extracted from open source projects. You can rate examples to help us improve the quality of examples.