Train Resnet50 Multiray Wids
The notebook demonstrates how to perform distributed PyTorch training using the
wids library, specifically focusing on the use of ShardListDataset for
dataset construction and DistributedChunkedSampler for efficient sampling in a
distributed environment. It highlights the ease of integrating wids with
PyTorch code for training models on datasets stored in the cloud, with an
example using a fake version of ImageNet. The notebook also shows how to set up
distributed training using Ray and includes a configuration dataclass to manage
training parameters.
Train Resnet50 Wids
The notebook demonstrates the use of the wids library for handling large-scale
image datasets in a distributed training context. It illustrates how to load and
preprocess images from a sharded dataset stored remotely using
wids.ShardListDataset, apply transformations, and efficiently sample the data
using wids.DistributedChunkedSampler for training a deep learning model. The
notebook also shows how to integrate these datasets with PyTorch's DataLoader
for training a model with reporting on training progress.