High-performance PyTorch-compatible streaming dataset with distributed caching for on-the-fly remote dataset fetching

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  • License: Apache Software License (Apache-2.0)
  • Author: fpgaminer
  • Tags pytorch , dataset , streaming , distributed , machine-learning , deep-learning , cache , mds
  • Requires: Python >=3.9
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Project description

Flowrider

WARNING: FOR PERSONAL USE ONLY, NOT PRODUCTION READY

Overview

Inspired by MosaicML's streaming library (https://github.com/mosaicml/streaming), this library provides a PyTorch IterableDataset implementation that streams data from cloud storage. It is distributed training compatible, and can cache data to disk.

Testing

cargo test --no-default-features --features auto-initialize

NOTE

  • Logging has to use envlogger, even though there are ways to send logs to the Python logger. This is because when sending logs to Python's logger, the GIL is required. Since we have a background thread doing work (and potentially logging), that can create a minefield of either deadlocks or not allowing background threads to work.

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  • License: Apache Software License (Apache-2.0)
  • Author: fpgaminer
  • Tags pytorch , dataset , streaming , distributed , machine-learning , deep-learning , cache , mds
  • Requires: Python >=3.9
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0.2.0

0.1.4

0.1.3

This version

0.1.2

0.1.1

0.1.0

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