torch.utils.data — PyTorch 2.7 documentation
class torch.utils.data.distributed. DistributedSampler (dataset, num_replicas = None, rank = None, shuffle = True, seed = 0, drop_last = False) [source] [source] ¶ Sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with torch.nn.parallel.DistributedDataParallel.
Multi GPU training with DDP — PyTorch Tutorials 2.7.0+cu126 …
DistributedSampler chunks the input data across all distributed processes. The DataLoader combines a dataset and a. sampler, and provides an iterable over the given dataset. Each process will receive an input batch of 32 samples; the effective batch size is 32 * nprocs, or 128 when using 4 GPUs.
Distributed Data Sampler - PyTorch Forums
Apr 28, 2022 · I have been using Speechbrain’s Distributed sampler wrapper : class DistributedSamplerWrapper(DistributedSampler): “”“This wrapper allows using any sampler with Distributed Data Parallel (DDP) correctly.
Getting Started with Distributed Data Parallel - PyTorch
DistributedDataParallel (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 applications. To use DDP, you’ll need to spawn multiple processes and create a …
DistributedSampler - distributed - PyTorch Forums
Jul 22, 2020 · I understand that the distributed sampler chunks the dataset for each GPU. However, when using DDP, it loads the entire Dataset on N GPUs N times. Is this how it works?
PyTorch Distributed Overview — PyTorch Tutorials 2.7.0+cu126 …
The PyTorch Distributed library includes a collective of parallelism modules, a communications layer, and infrastructure for launching and debugging large training jobs.
How to implement a custom distributed sampler - data - PyTorch …
May 11, 2022 · This idea can be implemented succintly through batch_sampler argument of PyTorch Dataloader. batch_sampler accepts 'Sampler' or Iterable object that yields indices of next batch.
Understanding DistributedSampler and DataLoader drop_last
Jul 14, 2024 · Drop last just means that the torch.utils.data.DataLoader will drop the last batch when you iterate over it. This might be useful if for example your last batch will have a batch size of 1 and your model has BatchNorm as normalization layers, causing it to error when there is only 1 …
Probability distributions - torch.distributions — PyTorch 2.7 …
The distribution of the ratio of independent normally distributed random variables with means 0 follows a Cauchy distribution. Example: >>> m = Cauchy ( torch . tensor ([ 0.0 ]), torch . tensor ([ 1.0 ])) >>> m . sample () # sample from a Cauchy distribution with …
How does torch.utils.data.distributed ... - PyTorch Forums
Dec 31, 2021 · train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, num_replicas=args.world_size, rank=rank)