S3FileLoader

class torchdata.datapipes.iter.S3FileLoader(source_datapipe: IterDataPipe[str], request_timeout_ms=- 1, region='', buffer_size=None, multi_part_download=None)

Iterable DataPipe that loads Amazon S3 files from the given S3 URLs (functional name: load_files_by_s3). S3FileLoader iterates all given S3 URLs in BytesIO format with (url, BytesIO) tuples.

Note

1. source_datapipe must contain a list of valid S3 URLs.

2. request_timeout_ms and region will overwrite settings in the configuration file or environment variables.

3. The lack of AWS proper configuration can lead empty response. For more details related to S3 IO DataPipe setup and AWS config, please see the README file.

Parameters:

• source_datapipe – a DataPipe that contains URLs to s3 files

• request_timeout_ms – timeout setting for each reqeust (3,000ms by default)

• region – region for access files (inferred from credentials by default)

• buffer_size – buffer size of each chunk to download large files progressively (128Mb by default)

• multi_part_download – flag to split each chunk into small packets and download those packets in parallel (enabled by default)

Example

>>> from torchdata.datapipes.iter import IterableWrapper, S3FileLoader
>>> dp_s3_urls = IterableWrapper(['s3://bucket-name/folder/', ...]).list_files_by_s3()
# In order to make sure data are shuffled and sharded in the
# distributed environment, `shuffle`  and `sharding_filter`
# are required. For detail, please check our tutorial in:
# https://pytorch.org/data/main/tutorial.html#working-with-dataloader
>>> sharded_s3_urls = dp_s3_urls.shuffle().sharding_filter()
>>> dp_s3_files = S3FileLoader(sharded_s3_urls)
>>> for url, fd in dp_s3_files: # Start loading data
...     data = fd.read()
# Functional API
>>> dp_s3_files = sharded_s3_urls.load_files_by_s3(buffer_size=256)
>>> for url, fd in dp_s3_files:
...     data = fd.read()