lleaves VS zod

Compare lleaves vs zod and see what are their differences.

lleaves

Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x. (by siboehm)

zod

TypeScript-first schema validation with static type inference (by colinhacks)
Our great sponsors
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • WorkOS - The modern identity platform for B2B SaaS
  • SaaSHub - Software Alternatives and Reviews
lleaves zod
4 288
292 30,347
- -
7.0 9.1
27 days ago 3 days ago
Python TypeScript
MIT License MIT License
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.

lleaves

Posts with mentions or reviews of lleaves. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-18.
  • LLeaves: A LLVM-based compiler for LightGBM decision trees
    1 project | news.ycombinator.com | 8 Jul 2023
  • Cold Showers
    4 projects | news.ycombinator.com | 18 Jun 2022
    I built this decision tree (LightGBM) compiler last summer: https://github.com/siboehm/lleaves

    It get's you ~10x speedups for batch predictions, more if your model is big. It's not complicated, it ended up being <1K lines of Python code. I heard a couple of stories like yours, where people had multi-node spark clusters running LightGBM, and it always amused me because by if you compiled the trees instead you could get rid of the whole cluster.

  • Tree compiler that speeds up LightGBM model inference by ~30x
    2 projects | /r/dataengineering | 22 Aug 2021
    In a near-future version I'll expose some of the compilation parameters, I was somewhat afraid of having an API that's too complicated deterring people who just want a no-fuzz drop-in replacement for LightGBM. But as long as I keep sane defaults and have the parameters optional it should be fine. Relevant parameters are definitely block size (needs to adjust to L1i size and tree size) as well as the LLVM codemodel (a smaller adress space increases single-batch prediction speeds but doesn't work for large models). The thread-size specific compilation I'm still looking into, it makes the API more complicated and so might not be worth it.

zod

Posts with mentions or reviews of zod. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-24.

What are some alternatives?

When comparing lleaves and zod you can also consider the following projects:

mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

class-validator - Decorator-based property validation for classes.

ngboost - Natural Gradient Boosting for Probabilistic Prediction

joi - The most powerful data validation library for JS [Moved to: https://github.com/sideway/joi]

m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

Yup - Dead simple Object schema validation

miceforest - Multiple Imputation with LightGBM in Python

typebox - Json Schema Type Builder with Static Type Resolution for TypeScript

catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

ajv - The fastest JSON schema Validator. Supports JSON Schema draft-04/06/07/2019-09/2020-12 and JSON Type Definition (RFC8927)

io-ts - Runtime type system for IO decoding/encoding

Superstruct - A simple and composable way to validate data in JavaScript (and TypeScript).