catboost
rayon
catboost | rayon | |
---|---|---|
8 | 67 | |
7,744 | 10,242 | |
0.7% | 1.6% | |
9.9 | 9.0 | |
6 days ago | 7 days ago | |
Python | Rust | |
Apache License 2.0 | Apache License 2.0 |
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.
catboost
- CatBoost: Open-source gradient boosting library
- Boosting Algorithms
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What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.
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Writing the fastest GBDT libary in Rust
Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.
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Data Science toolset summary from 2021
Catboost - CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. Link - https://catboost.ai/
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CatBoost Quickstart — ML Classification
CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs.
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[D] What are your favorite Random Forest implementations that support categoricals
If you considering GBDT check out catboost, unfortunately RF mode is not available but library implement lots of interesting categorical encoding tricks that boost accuracy.
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CatBoost and Water Pumps
The data contains a large number of categorical features. The most suitable for obtaining a base-line model, in my opinion, is CatBoost. It is a high-performance, open-source library for gradient boosting on decision trees.
rayon
- Rayon: Data-race free parallelization of sequential computations in Rust
- Too Dangerous for C++
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Which application/problem would you choose for presenting Rust to newcomers in 1h30min?
Do some operations with .iter() then later use rayon to parallelize. So you can show how easy is to add a dependency and how easy is to parallelize.
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What Are The Rust Crates You Use In Almost Every Project That They Are Practically An Extension of The Standard Library?
rayon: Async CPU runtime for parallelism.
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Moving from Typescript and Langchain to Rust and Loops
In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.
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AreWeMegafactoryYet? I just breached simulating 1M buildings @ 60 fps (If I'm not recording, Ryzen 7 1700X 8 Core)
With a lot of rayon, blood, sweat and tears I finally managed to simulate a million buildings at 60fps :) Feel free to AMA, game is Combine And Conquer
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The Rust I Wanted Had No Future
(see https://github.com/rayon-rs/rayon/tree/master/src/iter/plumbing)
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Parallel event iterator?
I did some very basic testing with this crate : https://crates.io/crates/rayon and it seems to work :
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General Recommendations: Should I Use Tree-sitter as the AST for the LSP I am developing?
Sequentially, generating tree-sitter AST for each file and querying for the links of each file takes around 2.3 seconds. However, I randomly remembered this crate rayon, and I decided to test it. It ended up improving the performance (just by changing 2 lines of code) to 200-300ms by parallelizing the iterators and tree-sitter queries. MAJOR.
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python to rust migration
Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.
What are some alternatives?
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
crossbeam - Tools for concurrent programming in Rust
Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF)
tokio - A runtime for writing reliable asynchronous applications with Rust. Provides I/O, networking, scheduling, timers, ...
Keras - Deep Learning for humans
RxRust - The Reactive Extensions for the Rust Programming Language
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
vowpal_wabbit - Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
tokio-rayon - Mix async code with CPU-heavy thread pools using Tokio + Rayon
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
sqlx - 🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite.