streaming-wds (Streaming WebDataset)
streaming-wds
is a Python library that enables efficient streaming of WebDataset-format datasets from boto3-compliant object stores for PyTorch. It's designed to handle large-scale datasets with ease, especially in distributed training contexts.
Features
- Streaming of WebDataset-format data from S3-compatible object stores
- Efficient sharding of data across both torch distributed workers and dataloader multiprocessing workers
- Supports (approximate) shard-level mid-epoch resumption when used with
StreamingDataLoader
- Blazing fast data loading with local caching and explicit control over memory consumption
- Customizable decoding of dataset elements via
StreamingDataset.process_sample
TODO
- Faster tar extraction in C++ threads (using pybind11)
- Key-level mid-epoch resumption
- Tensor Parallel replication strategy
Installation
You can install streaming-wds
using pip:
pip install streaming-wds
Quick Start
Here's a basic example of how to use streaming-wds:
from streaming_wds import StreamingWebDataset, StreamingDataLoader
dataset = StreamingWebDataset(
remote="s3://your-bucket/your-dataset",
split="train",
profile="your_aws_profile",
shuffle=True,
max_workers=4,
schema={".jpg": "PIL", ".json": "json"}
)
import torchvision.transforms.v2 as T
class ImageNetWebDataset(StreamingWebDataset):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.transforms = T.Compose([
T.ToImage(),
T.Resize((64,)),
T.ToDtype(torch.float32),
T.Normalize(mean=(128,), std=(128,)),
])
def process_sample(self, sample):
sample[".jpg"] = self.transforms(sample[".jpg"])
return sample
dataloader = StreamingDataLoader(dataset, batch_size=32, num_workers=4)
for batch in dataloader:
pass
state_dict = dataloader.state_dict()
dataloader.load_state_dict(state_dict)
Configuration
remote
(str): The S3 URI of the dataset.split
(Optional[str]): The dataset split (e.g., "train", "val", "test"). Defaults to None.profile
(str): The AWS profile to use for authentication. Defaults to "default".shuffle
(bool): Whether to shuffle the data. Defaults to False.max_workers
(int): Maximum number of worker threads for download and extraction. Defaults to 2.schema
(Dict[str, str]): A dictionary defining the decoding method for each data field. Defaults to {}.memory_buffer_limit_bytes
(Union[Bytes, int, str]): The maximum size of the memory buffer in bytes per worker. Defaults to "2GB".file_cache_limit_bytes
(Union[Bytes, int, str]): The maximum size of the file cache in bytes per worker. Defaults to "2GB".
Contributing
Contributions to streaming-wds are welcome! Please feel free to submit a Pull Request.
License
MIT License