Deep Lake, a Lakehouse for Deep Learning: Performance Benchmarks

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;.

In this section, we experimentally demonstrate Deep Lake’s performance at scale from the point of ingestion into the format up to training at scale against other dataloaders and formats. We compare streaming datasets from different storage backends, and showcase performance gains and scalability while training on the cloud. 6.1 Ingestion speed to various formats 10,000 images from FFHQ dataset were uncompressed and stored in NumPy format. Each 1024x1024x3 raw image is a 3MB array.

In this section, we experimentally demonstrate Deep Lake’s performance at scale from the point of ingestion into the format up to training at scale against other dataloaders and formats. We compare streaming datasets from different storage backends, and showcase performance gains and scalability while training on the cloud. 6.1 Ingestion speed to various formats 10,000 images from FFHQ dataset were uncompressed and stored in NumPy format. Each 1024x1024x3 raw image is a 3MB array.

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