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OmniSci and RAPIDS: End-to-End Open Source Data Science Workflow NVIDIA GTC | San Jose | March 20, 2019 slides: https://speakerdeck.com/omnisci

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© OmniSci 2018 Aaron Williams VP, Global Community @_arw_ [email protected] /in/aaronwilliams/ /williamsaaron Venkat Krishnamurthy VP, Product @niviksha [email protected] /in/vkcmu/

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© OmniSci 2018 3 Fast Hardware + Fast Software 3-Tier Memory Caching Query Compilation In-Situ Rendering

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© OmniSci 2018 4 * open source for single node github.com/omnisci/mapd-core

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© OmniSci 2018 Camp Fire Demo

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© OmniSci 2018 WiFi Access Points Demo

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Unifying GPU-accelerated Analytics and Data Science ✔ With OmniSci’s Arrow-capable python API (and via Ibis), OmniSci can output results direct to cudf, and integrate with RAPIDS via Python (requires pymapd 0.7.0). ✔ OmniSci’s JupyterLab integration (and support for Altair and Ibis) allows for connecting, querying, in-notebook visualization and extraction of data OmniSci User Defined Functions (coming 2019) will allow deeper, lower-level integration with RAPIDs libraries Altair: https://altair-viz.github.io/ Ibis: http://ibis-project.org/ OmniSci query result set in-GPU to RAPIDS GPU-resident outputs from RAPIDS ML algorithms

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© OmniSci 2018 Aaron Williams VP, Global Community @_arw_ [email protected] /in/aaronwilliams/ /williamsaaron Venkat Krishnamurthy VP, Product @niviksha [email protected] /in/vkcmu/ slides: https://speakerdeck.com/omnisci Thank you!