Really happy to share a new visualization project I've been working on!
VegaFusion integrates with @vega_vis and Altair (@jakevdp) to accelerate visualizations by executing intensive calculations in the Python kernel process (with efficient parallelization).
I had to pleasure of having @coding_matt intern with us on the @RAPIDSai Viz team this last year. Check out his blog post detailing one of neat things he helped us with for @nodejs + RAPIDS - https://t.co/Iqpb9Q6uwb
Take time to check out the talk @exactlyAllan will give today!! Did you know we have RAPIDS Node.js? If you did, do you want to know great ways to put it to use? The talk is free! And check out his blog post https://t.co/PyNuK2fkvg
Excited for sharing some of the work we have been doing at @RAPIDSai: "GPU Accelerating Node.js with the Node-Rapids Data Science Framework on Oct 19, 2021 @ 11:00 AM PDT" by @exactlyAllan via @nodeconfremote https://t.co/zSFAPzf2he
Bokeh 2.4 is out!
Built-in LaTeX equation support for axes and markups (with more coming), SVG improvements, and a re-written Contributor's Guide all part of @cziscience grant work! Plus many other improvements, read about everything at https://t.co/mldfWf79Vm
On Sept 6, you’ll meet the four civilians going into space.
On Sept 13, you’ll see them prepare.
On Sept 15, you’ll watch the live launch
On Sept 30, you’ll be in space alongside them
Countdown: Inspiration4 Mission To Space takes off next week
Out now, RAPIDS release 21.06! New #cuML and #cuGraph algorithms, new list functionality, a whole new way to measure @RAPIDSai progress with the change to CalVer, and much more! https://t.co/dpVG6V6XJA
@NVIDIAGTC is next week. Me, @AjayTh123, @inlineptx, and @bigreddot have a pretty interesting talk if you've heard about a little something called Node.js. Check it out, its free: https://t.co/xOxXeMyhjg
RAPIDS cuxfilter also just added some really fancy dynamic dashboard layouts too - if you haven't used it before, now is a great time. Props to @AjayTh123!
Converting from on-disk file formats to in-memory DataFrame records takes precious time and resources. Filtered reads with @RAPIDSai & @dask_dev ease the pain. Working w/ the NYC Taxi dataset, skip irrelevant records 7x faster w/ 5x lower peak memory. https://t.co/TT4Mfth3ez
Don't miss our tutorial at #JupyterCon2020 tomorrow, where we walk through a generalized dataviz-focused workflow that takes advantage of the performance of @rapidsai and RAPIDS-integrated libraries.