Rezultati pretraživanja
  1. 7. lip 2019.

    It works with Series, too.

    Prikaži ovu nit
  2. 29. kol 2019.

    We did a thing and made bigger, better, faster, stronger 💪. Super proud of the team for this release and it just seems to be getting better and better from here. 0.10 will be even more fun! 😀

  3. 3. lis 2019.

    I've never previously considered using a to unpack variable structure binary data. I'm seeing crazy speedups with . Now I'm getting a 258x speedup unpacking float64 (c/w np.frombuffer) ESRI shapefile points into a .

  4. 3. lis 2019.
    Odgovor korisnicima i sljedećem broju korisnika:

    For reference, in we had 64 threadsperblock for our `apply_rows` UDFs and in changing it to use the `.forall(...)(...)` syntax we saw a ~10x speedup from getting much better occupancy on the GPU in 0.10 😀

  5. 16. stu 2019.

    I'm giving a talk with my colleague Ashwin Srinath today at at 11:15 in Sky Room. If you want to hear about and come check it out! If anyone wants to have a chat about GPU-Accelerated data science at the conference, shoot me a message and we'll find time

  6. 24. ožu 2019.

    Matt Rocklin explains how uses to scale computing across multiple and nodes. This includes computing across datasets much larger than total available GPU memory.

  7. 8. velj 2019.

    Pearu created a nice small package to create copy-free views of arrays in Python on the GPU and on the host. Supports , , , , , , and ()!

  8. 2. lis 2019.

    I tried a larger shapefile California blocks from TIGER/LINE. ~1 sec to unravel 40m points in 750K blocks & similar to read dbf from string into . Huge potential with here and I believe that further speedup is possible!

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  9. 13. svi 2019.

    Second big news of the day, is on ! If you want to try multi-node multi-GPU on or you now can, easier than ever, on nodes. We started with , now on Dataproc for scale. Try it today!

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  10. 28. lis 2018.

    WOOT! data frame library now installable! conda install -c numba -c conda-forge -c rapidsai -c defaults cudf=0.2.0 Even more to come this week! So proud of everyone on the team!

  11. 18. sij

    Another bonus article, want to accelerate , add a and use plus this scales with & integrates with , , , , and more just like Pandas.

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  12. 28. kol 2019.

    Been using nightly for the last few weeks to enable our Multi-Node demos. Onwards and upwards with 0.9!

  13. 21. kol 2019.

    Interested in seeing how can help accelerate your network security research and use cases? Check out our latest notebook from . It uses and to investigate a network graph for incidents.

  14. 2. stu 2018.

    Woot! ML library is now installable! conda install -c numba -c conda-forge -c rapidsai -c defaults cuml=0.2.0 K-means and K-NN ( ) added w/ integration! I know I said this before, but more to come next week!

  15. 23. lip 2019.

    With the addition of Java bindings in the upcoming 0.8 release, is becoming a colorful project! , , ,

  16. I just spent an entire afternoon trying to install 's to run Python code on a GPU, circumventing the lack of Windows support by hosting a notebook from subsystem shell (Ubuntu), only to realize that my GPU is not supported 🤦‍♂️

  17. 28. kol 2019.

    Built to scale, 0.9 is here! : more robust, up to 10x faster; : PageRank 300GB in 30 secs; : multi-node kmeans/random forest & the new Forest Inference Library that accelerates / inference over 30x!

  18. 19. ožu 2019.

    Fantastic work by doing tree detection In LiDAR using libraries and DBSCAN. Awesome work!

  19. 4. tra 2019.

    Check @blazingdb out if you're looking for on . They are heavy contributors to and an integral part of the analytics ecosystem. Super happy that I get to consistently work with them to help build the future!

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