river VS bolt

Compare river vs bolt and see what are their differences.

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river bolt
17 6
4,715 2,444
2.0% -
9.2 0.0
5 days ago over 1 year ago
Python C++
BSD 3-clause "New" or "Revised" License Mozilla Public License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

river

Posts with mentions or reviews of river. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-20.

bolt

Posts with mentions or reviews of bolt. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-04.
  • Show HN: Want something better than k-means? Try BanditPAM
    4 projects | news.ycombinator.com | 4 Apr 2023
    > frown on that sort of dataset

    That example was definitely contrived and designed to strongly illustrate the point. I'll counter slightly that non-peaky topologies aren't uncommon, but they're unlikely to look anything that would push KMedoids to a pathological state rather than just a slightly worse state ("worse" assuming that KMeans is the right choice for a given problem).

    > worth pointing out .. data reference

    Totally agreed. I hope my answer didn't come across as too negative. It's good work, and everyone else was talking about the positives, so I just didn't want to waste too much time echoing again that while getting the other points across.

    > bolt reference

    https://github.com/dblalock/bolt

    They say as much in their paper, but they aren't the first vector quantization library by any stretch. Their contributions are, roughly:

    1. If you're careful selecting the right binning strategy then you can cancel out a meaningful amount of discretization error.

    2. If you do that, you can afford to choose parameters that fit everything nicely into AVX2 machine words, turning 100s of branching instructions into 1-4 instructions.

    3. Doing some real-world tests to show that (1-2) matter.

    Last I checked their code wasn't very effective for the places I wanted to apply it, but the paper is pretty solid. I'd replace it with a faster KMeans approximation less likely to crash on big data (maybe even initializing with KMedoids :) ), and if the thing you're quantizing is trainable with some sort of gradient update step then you should do a few optimization passes in the discretized form as well.

  • 10x faster matrix and vector operations
    4 projects | news.ycombinator.com | 18 Jun 2022

What are some alternatives?

When comparing river and bolt you can also consider the following projects:

alibi-detect - Algorithms for outlier, adversarial and drift detection

python-tidal - Python API for TIDAL music streaming service

wayfire - A modular and extensible wayland compositor

PySyft - Perform data science on data that remains in someone else's server

edl - Inofficial Qualcomm Firehose / Sahara / Streaming / Diag Tools :)

makinage - Stream Processing Made Easy

frameworks - Sample code and build environments for MPC frameworks

composer - Supercharge Your Model Training

halutmatmul - Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator

AugLy - A data augmentations library for audio, image, text, and video.

rxsci - ReactiveX for data science

draco - Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.