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  1. proslijedio/la je Tweet
    prije 7 sati

    Test pool performance for yourself w/ this self-contained Gist! git clone and run no_pool.py & yes_pool.py (in any order) then open up pool_vs_no_pool.ipynb and run all to see the speedup of a memory pool on 2.54GB of CSV!

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  2. proslijedio/la je Tweet
    3. velj

    Session 02 is Tuesday! Join for cancer genomics live-coding as Dr. (Stanford Medicine) and ( ) experiment with visual GPU UMAP for longitudinal analysis over thousands of cancer patients in GENIE / !

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  3. proslijedio/la je Tweet
    prije 14 sati

    Happening tomorrow. Cool way to learn about precision medicine / genetics / cancer, GPU data frames, and UMAP clustering ("uniform manifold approximation & projection"), chat w/ folks in the middle of it, and post-show, even try for yourself!

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  4. proslijedio/la je Tweet
    prije 13 sati

    Reduce overhead & further improve your accelerated with Memory Manager in BlazingSQL! Check out our new blog post for more: ?

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  5. proslijedio/la je Tweet
    31. sij

    We are very happy to announce the release of Panel 0.8! This release brings huge performance improvements, support, improved JS linking, easier debugging, support for responsive & plots. All powered by

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  6. proslijedio/la je Tweet
    2. velj
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  7. proslijedio/la je Tweet
    prije 16 sati

    Turbo-telegram update: Made Taxi Dashboard SQL based & added a Notebook to download & pre-process Jan - Dec 2015 yellow cab data w/ ! >20GB CSV downloaded, transformed, rewritten, read back & visualized in 7min 52sec w/ a single T4 on AWS.

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  8. proslijedio/la je Tweet
    29. sij

    The next Numba release (0.49) will be March 2020 as we do some some serious internal refactoring and cleanup. We've already deleted more than 7000 lines of code!

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  9. proslijedio/la je Tweet
    31. sij
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  10. proslijedio/la je Tweet
    30. sij

    40x faster predictions for even the deepest random forests with FIL’s new sparse forest support -

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  11. proslijedio/la je Tweet
    30. sij

    We're standardizing OpenAI's deep learning framework on PyTorch to increase our research productivity at scale on GPUs (and have just released a PyTorch version of Spinning Up in Deep RL):

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  12. proslijedio/la je Tweet
    28. sij

    Dask 2.10.0 is now available. This release includes bug fixes and compatibility with the imminent 1.0 release! Support for pandas' new nullable boolean and string data types has already been added. Read the release notes at

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  13. proslijedio/la je Tweet
    29. sij

    Numba 0.48.0 has been released! This one comes shortly after 0.47.0 as we had many PRs that didn't make it in time. Quite a few bug fixes (including many CUDA fixes thanks to NVIDIA) and many new string methods contributed by Intel. More details:

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  14. proslijedio/la je Tweet
    29. sij

    If you’d like to use RAPIDS in Kaggle competitions, now it can be easily installed with the dataset below. It takes ~40 seconds to install, compared to ~6 minutes when using conda install.

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  15. proslijedio/la je Tweet
    30. sij

    Pandas 1.0 is here! * Read the release notes: * Read the blogpost reflecting on what 1.0 means to our project: * Install with conda / PyPI: Thanks to our 300+ contributors to this release.

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  16. proslijedio/la je Tweet
    29. sij

    We’re excited to announce a new Anaconda product! Manage and secure open-source and in your organization with Anaconda Team Edition.

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  17. proslijedio/la je Tweet
    28. sij

    Multi-node distribution across multiple w/ BlazingSQL is made simple thru Dask! Just start up dask-scheduler & worker, then pass your Client into BlazingContext upon initialization and you're good to go! See more in docs:

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  18. proslijedio/la je Tweet
    28. sij

    Using OpenAI Gym and PyBullet to train an open-source 3D printed robot

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  19. proslijedio/la je Tweet
    27. sij

    So excited to share the latest blog on alert analysis from our awesome team!

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  20. proslijedio/la je Tweet
    21. sij

    Facebook AI has effectively solved the task of point-goal navigation by AI agents in simulated environments, using only a camera, GPS, and compass data. Agents achieve 99.9% success in a variety of virtual settings, such as houses and offices.

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