Deep Lake, a Lakehouse for Deep Learning: Related Work

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Deep Lake, a Lakehouse for Deep Learning: Related Work
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Researchers introduce Deep Lake, an open-source lakehouse for deep learning, optimizing complex data storage and streaming for deep learning frameworks.

Authors: Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; Abhinav Tuli, Activeloop, Mountain View, CA, USA; Levon Ghukasyan, Activeloop, Mountain View, CA, USA; Fariz Rahman, Activeloop, Mountain View, CA, USA;.

Multiple projects have tried to improve upon or create new formats for storing unstructured datasets including TFRecord extending Protobuf , Petastorm extending Parquet , Feather extending arrow , Squirrel using MessagePack , Beton in FFCV . Designing a universal dataset format that solves all use cases is very challenging. Our approach was mostly inspired by CloudVolume , a 4-D chunked NumPy storage for storing large volumetric biomedical data.

Multiple projects have tried to improve upon or create new formats for storing unstructured datasets including TFRecord extending Protobuf , Petastorm extending Parquet , Feather extending arrow , Squirrel using MessagePack , Beton in FFCV . Designing a universal dataset format that solves all use cases is very challenging. Our approach was mostly inspired by CloudVolume , a 4-D chunked NumPy storage for storing large volumetric biomedical data.

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