Deep Lake, a Lakehouse for Deep Learning: Conclusions, Acknowledgement, and References

Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro Current Challenges Tensor Storage Format Deep Lake System Overview Machine Learning Use Cases Performance Benchmarks Discussion and Limitations Related Work Conclusions, Acknowledgement, and References 9. CONCLUSION We presented Deep Lake, the lakehouse for deep learning. Deep Lake is designed to help deep learning workflows run as seamlessly as analytical workflows run on Modern Data Stack. Notably, Deep Lake is built to retain prominent features of data lakes, such as time travel, querying, and rapid data ingestion at scale. One important distinction from traditional data lakes is Deep Lake’s ability to store unstructured data with all its metadata in deep learning-native columnar format, which enables rapid data streaming. This allows materializing data subsets on-the-fly, visualizing them in-browser, or ingesting them into deep learning frameworks without sacrificing GPU utilization. Finally, we show that Deep Lake achieves state-of-the-art performance for deep learning on large datasets via multiple benchmarks. 10. ACKNOWLEDGEMENT The authors would like to thank Richard Socher, Travis Oliphant, Charu Rudrakshi, Artem Harutyunyan, Iason Ofeidis, Diego Kiedanski, Vishnu Nair, Fayaz Rahman, Dyllan McCreary, Benjamin Hindman, Eduard Grigoryan, Kristina Grigoryan, Ben Chislett, Joubin Houshyar, Andrii Liubimov, Assaf Pinhasi, Vishnu Nair, Eshan Arora, Shashank Agarwal, Pawel Janowski, Kristina Arezina, Gevorg Karapetyan, Vigen Sahakyan and the open-source community including contributors. The project was funded by Activeloop. We also thank the CIDR reviewers for their feedback. REFERENCES [1] 2006. Amazon S3. GitHub 2022, 1 (2006). https://aws.amazon. com/s3 [2] 2009. Clickhouse. GitHub 2022, 1 (2009). https://github.com/ ClickHouse/ClickHouse [3] 2010. Google Cloud Storage. GitHub 2022, 1 (2010). https: //cloud.google.com/storage [4] 2012. Google BigQuery. GitHub 2022, 1 (2012). https://cloud. google.com/bigquery [5] 2014. Protocol Buffers - Google’s data interchange format. 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Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Authors: Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro Current Challenges Tensor Storage Format Deep Lake System Overview Machine Learning Use Cases Performance Benchmarks Discussion and Limitations Related Work Conclusions, Acknowledgement, and References Abstract and Intro Abstract and Intro Current Challenges Current Challenges Tensor Storage Format Tensor Storage Format Deep Lake System Overview Deep Lake System Overview Machine Learning Use Cases Machine Learning Use Cases Performance Benchmarks Performance Benchmarks Discussion and Limitations Discussion and Limitations Related Work Related Work Conclusions, Acknowledgement, and References Conclusions, Acknowledgement, and References 9. CONCLUSION We presented Deep Lake, the lakehouse for deep learning. Deep Lake is designed to help deep learning workflows run as seamlessly as analytical workflows run on Modern Data Stack. Notably, Deep Lake is built to retain prominent features of data lakes, such as time travel, querying, and rapid data ingestion at scale. One important distinction from traditional data lakes is Deep Lake’s ability to store unstructured data with all its metadata in deep learning-native columnar format, which enables rapid data streaming. This allows materializing data subsets on-the-fly, visualizing them in-browser, or ingesting them into deep learning frameworks without sacrificing GPU utilization. Finally, we show that Deep Lake achieves state-of-the-art performance for deep learning on large datasets via multiple benchmarks. 10. ACKNOWLEDGEMENT The authors would like to thank Richard Socher, Travis Oliphant, Charu Rudrakshi, Artem Harutyunyan, Iason Ofeidis, Diego Kiedanski, Vishnu Nair, Fayaz Rahman, Dyllan McCreary, Benjamin Hindman, Eduard Grigoryan, Kristina Grigoryan, Ben Chislett, Joubin Houshyar, Andrii Liubimov, Assaf Pinhasi, Vishnu Nair, Eshan Arora, Shashank Agarwal, Pawel Janowski, Kristina Arezina, Gevorg Karapetyan, Vigen Sahakyan and the open-source community including contributors. The project was funded by Activeloop. We also thank the CIDR reviewers for their feedback. REFERENCES [1] 2006. Amazon S3. GitHub 2022, 1 (2006). https://aws.amazon. com/s3 [2] 2009. Clickhouse. GitHub 2022, 1 (2009). https://github.com/ ClickHouse/ClickHouse [3] 2010. Google Cloud Storage. GitHub 2022, 1 (2010). https: //cloud.google.com/storage [4] 2012. Google BigQuery. GitHub 2022, 1 (2012). https://cloud. google.com/bigquery [5] 2014. Protocol Buffers - Google’s data interchange format. GitHub 2022, 1 (2014). https://github.com/protocolbuffers/ protobuf [6] 2015. The Apache Software Foundation: Apache ORC. GitHub 2022, 1 (2015). https://github.com/apache/orc [7] 2016. Feather. GitHub 2022, 1 (2016). https://github.com/ wesm/feather [8] 2016. Weaviate: The ML-first vector search engine. GitHub 2022, 1 (2016). https://github.com/semi-technologies/weaviate [9] 2017. Apache Airflow. GitHub 2022, 1 (2017). http://airflow. incubator.apache.org [10] 2017. The Apache Software Foundation: Apache Hudi. GitHub 2022, 1 (2017). https://hudi.apache.org [11] 2017. CloudVolume: IO for Neuroglancer Datasets. GitHub 2022, 1 (2017). https://github.com/seung-lab/cloud-volume [12] 2018. Amazon Athena. GitHub 2022, 1 (2018). https://aws. amazon.com/athena [13] 2018. The Apache Software Foundation: Apache Arrow. GitHub 2022, 1 (2018). https://arrow.apache.org [14] 2018. The Apache Software Foundation: Apache Hadoop. GitHub 2022, 1 (2018). https://hadoop.apache.org [15] 2018. The Apache Software Foundation: Apache Iceberg. GitHub 2022, 1 (2018). https://iceberg.apache.org [16] 2018. Feast: open source feature store for machine learning. GitHub 2022, 1 (2018). https://github.com/feast-dev/feast [17] 2018. MinIO high performance object storage server compatible with Amazon S3 API. GitHub 2022, 1 (2018). https: //github.com/minio/minio [18] 2018. Petastorm. GitHub 2022, 1 (2018). https://github.com/ uber/petastorm [19] 2018. The WebDataset Format. GitHub 2022, 1 (2018). https: //github.com/webdataset/webdataset [20] 2019. The Apache Software Foundation: Apache Avro. GitHub 2019, 1 (2019). https://avro.apache.org [21] 2019. LakeFS: data lake with Git-like repository. GitHub 2022, 1 (2019). https://github.com/treeverse/lakeFS [22] 2020. Airbyte. GitHub 2022, 1 (2020). https://github.com/ airbytehq/airbyte [23] 2020. TensorStore: Library for reading and writing large multidimensional arrays. 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