Bridging analytics and data science workflows with GPUs | Hacker News Reader

Bridging analytics and data science workflows with GPUs

omnisci.com

19 pointstmostak11 comments

A few comments:

1. I'm usually down on dark-mode but this looks clean as hell 2. The JupyterHub integration looks nice! It was mentioned in the article it was the "tip of the iceberg" I'm wondering how hard it would be to integrate with a tool like Observable.

Observable: https://observablehq.com/

It's certainly possible, I guess the bigger question is whether people would be comfortable having a database do an automatic data transfer into a public tool. In the case of this Jupyter integration, it's a Docker image that's running next to the OmniSci database, so it's all within the same network

Thanks! We're big Observable fans, and you can find quite a few examples here: https://observablehq.com/search?query=mapd and https://observablehq.com/search?query=omnisci. But imagine we could do something deeper, any thoughts?

This is a good point. There are lots of interesting possibilities with Observable, especially using the OmniSciDB open-source project for analyses that people intentionally do want to share

Mike Bostock's notebook here is a great starting point, showing how to import and use mapd-connector [1] in an Observable notebook and then feed its data into a vega-lite chart:

https://observablehq.com/@mbostock/hello-mapd-connector

[1]: The Javascript client for connecting to OmniSci, https://github.com/omnisci/mapd-connector

The support for JupyterLab looks great and +1 for Dark Mode.

Thanks @jonbaer, and lot's more exciting stuff to come!

Dark Mode looks amazing. So clean.

I'm looking forward to exploring the integration with Jupyter.

Does it scale?

Could you be more specific? Does which part scale?

Check out this example crunching 1.45 billion rows on my 32GB Macbook Pro (CPU-only). https://twitter.com/ToddMostak/status/1162067442081751040?s=... With a GPU cluster it's very possible to handle 100+ billion rows with sub-second response times.

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