Product
Introducing License Enforcement in Socket
Ensure open-source compliance with Socket’s License Enforcement Beta. Set up your License Policy and secure your software!
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.
StreamingDataLoader
StreamingDataset.process_sample
You can install streaming-wds
using pip:
pip install streaming-wds
Here's a basic example of how to use streaming-wds:
from streaming_wds import StreamingWebDataset, StreamingDataLoader
# Create the dataset
dataset = StreamingWebDataset(
remote="s3://your-bucket/your-dataset",
split="train",
profile="your_aws_profile",
shuffle=True,
max_workers=4,
schema={".jpg": "PIL", ".json": "json"}
)
# or use a custom processing function
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
# Create a StreamingDataLoader for mid-epoch resumption
dataloader = StreamingDataLoader(dataset, batch_size=32, num_workers=4)
# Iterate through the data
for batch in dataloader:
# Your training loop here
pass
# You can save the state for resumption
state_dict = dataloader.state_dict()
# Later, you can resume from this state
dataloader.load_state_dict(state_dict)
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".Contributions to streaming-wds are welcome! Please feel free to submit a Pull Request.
MIT License
FAQs
Iterable Streaming Webdataset for PyTorch from boto3 compliant storage
We found that streaming-wds demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Product
Ensure open-source compliance with Socket’s License Enforcement Beta. Set up your License Policy and secure your software!
Product
We're launching a new set of license analysis and compliance features for analyzing, managing, and complying with licenses across a range of supported languages and ecosystems.
Product
We're excited to introduce Socket Optimize, a powerful CLI command to secure open source dependencies with tested, optimized package overrides.