DataParallel splits your data automatically and sends job orders to multiple models on several GPUs. After each model finishes their job, DataParallel collects ...

DDP is a powerful module in PyTorch that allows you to parallelize your model across multiple machines, making it perfect for large-scale deep learning ...

Implements data parallelism at the module level. This container parallelizes the application of the given module by splitting the input across the specified ...

Оценка 5,0 (1) In PyTorch, parallel training allows you to leverage multiple GPUs or computing nodes to speed up the process of training neural networks.

r"""Implements data parallelism at the module level. This container parallelizes the application of the given :attr:`module` by.

Tensor Parallelism(TP) is built on top of the PyTorch DistributedTensor (DTensor) and provides different parallelism styles: Colwise, Rowwise, and Sequence ...

Tensor parallelism is a technique for training large models by distributing layers across multiple devices, improving memory management and efficiency by ...

class DistributedDataParallel(Module, Joinable):. r"""Implement distributed data parallelism based on ``torch.distributed`` at module level.

Learn how to accelerate deep learning tensor computations with 3 multi GPU techniques—data parallelism, distributed data parallelism and model parallelism.

16 янв. 2019 г. · Using multi-GPUs is as simply as wrapping a model in DataParallel and increasing the batch size. Check these two tutorials for a quick start.