Dask

@dask_dev

Dask natively scales Python Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love

dask.org
Joined July 2017
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  1. Sep 16

    The Dask User Survey results are out now! Come read about how our community has changed, and what people want more of today:

    6 retweets 10 likes
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  2. Retweeted
    Aug 31

    Lazy loading, browsing a 1.4 Gb LIF using thanks to AICSImage python module making a array. Just amazing! 🚀🔥 (Widget based on code from )

    2 replies 9 retweets 20 likes
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  3. Retweeted
    Aug 31

    A recent change in triggered an upstream CI build in to fail, which resulted in an update to sparse. This is what a community working together looks like.

    1 reply 3 retweets 29 likes
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  4. Sep 2

    "Nothing fancy" I've never been so proud ☺️ Good software doesn't shine. If does it's job and gets out of the way.

    And in terms of environment nothing fancy but I took the occasion to leverage for the data processing and + to build a report on the status of the data used
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    1 reply 2 retweets 26 likes
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  5. Retweeted
    Aug 27

    Shout-out to for all the maintenance work he's been doing lately in the org! I woke up this morning to 20+ GitHub notifications for closed issues and PRs 🥰🥰🥰.

    3 replies 3 retweets 32 likes
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  6. Retweeted
    Aug 26

    CuPy v10.0.0b2/v9.4.0 has just been released. We bring support for CUDA Python in the v10 series and increased SciPy/NumPy compatibility thanks to GSoC’21! Check the release notes here and don't forget to star on GitHub!

    29 retweets 152 likes
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  7. Aug 23

    We'd like to thank for all his hard work during google summer of code

    6 likes
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  8. Aug 23

    New blogpost! Read about 's work improving visualizations here:

    2 replies 2 retweets 17 likes
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  9. Retweeted
    Aug 18

    The first release of dask-sql as a -contrib member is out! Special thanks to rajagurunath for implementing all the cool new ML features and thanks to the community for accepting dask-sql as a contrib package!

    1 reply 2 retweets 39 likes
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  10. Retweeted
    Aug 13

    looks like a biology, but no it is astrophysics 🥰🙃

    3 replies 2 retweets 12 likes
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  11. Retweeted
    Aug 11

    With 140+ responses now received for the census, I'm trying to summarise the "PyAOS stack" in one simple plot. Not an easy task!

    , , and 3 others
    3 replies 12 retweets 45 likes
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  12. Retweeted
    Jun 25

    The recording or our workshop/discussion on scaling up vector geospatial analysis with and is now online

    Videos from the 2021 Dask Summit are up online! See them here:
    1 reply 16 retweets 37 likes
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  13. Retweeted
    Aug 10

    Currently playing with 37GB (🤯) of MHD simulation files, and using + to do lazy computations on them is pure magic! 🪄

    4 replies 2 retweets 24 likes
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  14. Retweeted
    Aug 9

    Confused about choosing a good chunk size for arrays? Array chunks can't be too big (we'll run out of memory), or too small (the overhead introduced by Dask becomes overwhelming). So how to get it right? A thread...

    1 reply 37 retweets 125 likes
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  15. Retweeted
    Aug 6

    ✍️ dask-sql (a distributed SQL query engine in Python) dask-sql documentation 👤 Nils Braun ; Dask 🔗

    21 retweets 41 likes
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  16. Retweeted
    Jul 27

    What I really appreciate about : I slapped together , , , , into a module to do & & everything is compatible. Obviously a ton of effort by many open source volunteers & so awesome. And year after year it gets even better

    3 retweets 16 likes
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  17. Retweeted
    Jul 27
    Replying to and

    You are not alone! This is a common enough pain point that Dask has its own built-in PipInstall plugin

    1 reply 3 retweets 8 likes
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  18. Retweeted
    Jul 26

    Check out our recent talk on cuCIM from 2021.

    1 reply 9 retweets 22 likes
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  19. Retweeted
    Jul 22

    So BlenderNC, my other open-source project just had a major (minor) version release with NetCDF, GRIB and Zarr support. All via and . Please check it out if you are into global climate/weather dataviz :

    1 reply 13 retweets 39 likes
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  20. Retweeted
    Jul 18

    A guide of how I optimized to analyze big datasets using just a laptop: efficiently looping via vectorization, tweaking dtypes, and using . 🐍

    1 reply 18 retweets 63 likes
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