OmniSci 101: Accelerating the Data Science Workflow Workshop

Attendees will focus on learning all facets of OmniSci Immerse, from loading data to creating effective dashboards that utilize the power of the OmniSci platform. At the end of the workshop, attendees will feel comfortable importing and updating data using Data Manager, building single- and multi-table charts using Visual Data Fusion, multi-layer geospatial charts and even custom colormaps within OmniSci Cloud, as well as launching Jupyter Notebook from Immerse to do some prediction modeling. Lastly, attendees will learn using Omnisci’s pymapd for high-performance and data science libraries as part of the RAPIDS GPU Analytics project.

Wamsi Viswanath, Data Scientist, OmniSci
Alex Haruty, QA Lead, OmniSci

Transcript

3. • • • • • • • •
7. GPU Processing Keeps Moore’s Law Alive
8. Core Density Makes a Huge Difference
9. Advanced Memory Management
10. OmniSci Leverages GPU Parallelism High Memory Bandwidth Native Rendering Pipeline Supercomputer Processing
12. OmniSci: The GPU-accelerated Platform * open source for single node
16. OmniSci Geospatial Features • ◦ • ◦ • ◦ ◦ ◦ ◦ ▪
20. ST_DISTANCE & ST_CONTAINS use cases
21. ST_DISTANCE(poly1, ST_GeomFromText('POINT(0 0)')) Returns shortest planar distance between geometries. When you call ST_DISTANCE on two geometry objects, the engine returns the shortest straight-line planar distance, in degrees, between those points. For example, the following query returns the shortest distance between the point(s) in p1 and the polygon(s) in poly1: SELECT ST_DISTANCE(p1, poly1) from geo1; SELECT count(*) FROM geo1 WHERE ST_CONTAINS(poly1, 'POINT(0 0)'); Return the count of polys that contain the point (here as WKT) SELECT ST_INTERSECTS('POLYGON((0 0, 2 0, 2 2, 0 2, 0 0))','POINT(1 1)') FROM tbl; Returns true if two geometries intersect spatially, false if they do not share space. Details: • https://www.omnisci.com/docs/latest/5_geospatial_functions.html •
31. Building a dashboard using data provided by Tutela
32. Number chart examples:
33. Crossfilter chart examples:
34. Import Sample Datasets • San Francisco bikepaths (ESRI shape file with LINESTRINGS) https://s3.amazonaws.com/mapd-data/geodata/SFMTA-Bikeway-Network.zip • San Francisco city facilities (ESRI shapefile with POINTS) https://s3.amazonaws.com/mapd-data/geodata/sffacs_current.zip • San Francisco city parcels (GeoJSON file with MULTIPOLYGONS) https://s3.amazonaws.com/mapd-data/geodata/citylots_small.json
36. pymapd • • • • •
37. Generate API Keys For Cloud Instance To connect to your OmniSci cloud instance, you need to generate the API Access/Secret keys for your account. Go to your cloud instance SETTINGS -> DEVELOPER and click the Create Write Key button as shown in the caption.
38. • • • • • • •
40. ibis • • • • •
41. • • • •
42. GPU Open Analytics Initiative (GOAI) Seamless data interchange framework in GPU memory
43. ✔ ✔ 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
44. • • • •
45. •
46. • • • • •