tangram
linfa
Our great sponsors
tangram | linfa | |
---|---|---|
22 | 14 | |
1,310 | 3,381 | |
- | 3.5% | |
9.6 | 6.3 | |
almost 2 years ago | 26 days ago | |
Rust | Rust | |
GNU General Public License v3.0 or later | 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.
tangram
- Tangram open sourced their package manager code
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Train a Machine Learning Model to Predict the Programming Language in a Code Snippet
Head over to https://www.tangram.dev and give it a try!
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Writing the fastest GBDT libary in Rust
In this post, we will go over how we optimized our Gradient Boosted Decision Tree library. This is based on a talk that we gave at RustConf 2021: Writing the Fastest Gradient Boosted Decision Tree Library in Rust. The code is available on GitHub.
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What machine learning can learn from Ruby on Rails
You can check out the Tangram Ruby Gem. We built it using Ruby FFI and the source is available on our GitHub repo.
- Any role that Rust could have in the Data world (Big Data, Data Science, Machine learning, etc.)?
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Examples of Rust front-end web works
We are using Rust for our web application and website at Tangram. You can view the source here: https://github.com/tangramdotdev/tangram/tree/main/crates/www. The website is at https://www.tangram.dev. We decided to write our own web framework because we needed server rendering and we wanted to use the builder pattern in creating components. Here is an example component for our Logo to get a sense of what this looks like: https://github.com/tangramdotdev/tangram/blob/main/crates/www/ui/logo.rs
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Multi-language library support: Is it possible?
Check out https://github.com/tangramdotdev/tangram.
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Ask HN: Who is hiring? (October 2021)
Tangram | Senior Rust Programmer | Remote | https://www.tangram.dev
Tangram is an all in one machine learning framework designed for programmers. With Tangram, developers can train models and make predictions on the command line or with libraries for languages including Elixir, Go, JS, Python, Ruby, and Rust, and learn about their models and monitor them in production from a web application. To learn more about what the product does, watch the demo on the homepage at https://www.tangram.dev or check it out on GitHub at https://www.github.com/tangramdotdev/tangram.
We are looking to grow our engineering team with senior Rust programmers. We are currently based in Boston, MA but are looking to build a remote team. At Tangram, you'll get to work on everything from our core machine learning algorithms to writing front-end code in Rust! We are looking for developers with experience in Rust and familiarity with or willingness to learn machine learning concepts. If this sounds exciting, email me (Isabella, cofounder) at [email protected].
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Seed β A Rust front-end framework for creating fast and reliable web apps
We chose to use Rust instead of TypeScript for the front end of https://github.com/tangramdotdev/tangram.
This allows us to:
* Share code with our server written in Rust.
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How far along is the ML ecosystem with Rust?
I'm working on machine learning in Rust at Tangram. We currently only provide an implementation of linear models and gradient boosted decision trees but will move into exposing training of deep models in the future. You can check out Tangram here: https://github.com/tangramdotdev/tangram. You may also be interested in checking out Linfa https://github.com/rust-ml/linfa. If you're interested in the future of machine learning in Rust, check out Luca Palmieri's blog post: https://www.lpalmieri.com/posts/2019-12-01-taking-ml-to-production-with-rust-a-25x-speedup/
linfa
- Why is Rust not more popular in ML and secure edge computing?
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Polars vs ndarray performance
I've been playing with data analytics and ml in rust for the last couple of weeks. A typical ML job requires transforming some data to feed the ml model to the then train the model. For ML I've been using linfa (https://github.com/rust-ml/linfa) which is surprisingly nice. I've been experimenting with ndarray and polars for data transformation (linfa uses ndarray) - from a UX standpoint. I'm pretty surprised by polars' performance (https://h2oai.github.io/db-benchmark/), which sits on top of arrow2, and it's definitely a great candidate for OLAP tasks. But I couldn't find any comparison between ndarray and polars, has anyone had any meaningful experience with the two or/and can point me to a benchmark comparison?
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Ask HN: What is the job market like, for niche languages (Nim, crystal)?
The most comprehensive current view of the Rust machine learning ecosystem at the moment is probably at https://www.arewelearningyet.com/ (I sometimes help maintain this site)
Rust has a weird mix at the moment, and not one that's likely to significantly change within the next 12 months, at least. Certain tools are genuinely best-in-class, especially around simple operations on insane amounts of data. Rust kills it in that space due to its native speed and focus on concurrency.
There's also growing projects like Linfa [1]. that while not at the level of scikit-learn, have significantly increased their coverage on common data science/classical ML problems in the past couple years, along with improved tooling. The space does have a few pure-Rust projects coming down the pipeline around autodifferentiation, GPU compute, etc. that are likely to yield some really valuable results in deep learning, but that aren't quite available and will take some time to pick up some traction even once they're released. At the same time, areas like data visualization are unlikely to reach parity with something like matplotlib/pyplot in the near future.
Python is the de-facto standard, and will be for some time, but Rust's ability to build accessible high-level APIs on top of performant, language-native libraries is attracting some attention and I wouldn't be surprised to start seeing ingress in the certain areas over the next few years, where instead of the Python/C++ combination, it's just Rust all the way down.
[1] https://github.com/rust-ml/linfa
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Is RUST aiming to build an ecosystem on scientific computing?
take a look at https://github.com/rust-ml/linfa for machine learning related crates
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What is a FOSS which is needed but doesn't exist yet/needs contributers?
Check out smartcore and linfa. At work I was badly in need of an NMF function similar to MATLAB's one these days but not enough time to write one myself. If you're good at math and machine learning, this sounds like a task you could try tackling.
- Any role that Rust could have in the Data world (Big Data, Data Science, Machine learning, etc.)?
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How far along is the ML ecosystem with Rust?
For other algorithms, there is not yet a single library to rule them all (linfa might become that at some point) but searching for the algorithm you need on crate.io is likely to give you some results (obligatory plug to Friedrich, my gaussian process implementation).
- Linfa: A Rust machine learning framework
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AII4DEVS #10: Diverse knowledge is the key to grow the next generation of ML practitioners into AI engineers.
To all folks in love with Rust programming language, **linfa** is a promising library to check out: a complete porting of the well known scikit-learn library, which enables common preprocessing tasks and classical ML algorithms such as clustering, linear learners, logistic regression, and decision trees as well as support vector machines and Bayesian algorithms such as Naive Bayes. We all know that Python has the 98% of the machine learning languages market share, but if I looked to something else, a super-fast Rust implementation would be my first stop.
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Linfa has a website now!
for a start I will implement the TryFrom for Dataset under a feature flag. But to be really useful some of the algorithms have to start using something like DatasetBase here Records are currently bounded by an associated type for the element type, we would have to relax that too. Just read your blogpost on polars π
What are some alternatives?
wtpsplit - Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
smartcore - A comprehensive library for machine learning and numerical computing. The library provides a set of tools for linear algebra, numerical computing, optimization, and enables a generic, powerful yet still efficient approach to machine learning.
daisyui - πΌ πΌ πΌ πΌ πΌ βThe most popular, free and open-source Tailwind CSS component library
Awesome-Rust-MachineLearning - This repository is a list of machine learning libraries written in Rust. It's a compilation of GitHub repositories, blogs, books, movies, discussions, papers, etc. π¦
rust-plus-golang - Rust + Go β Call Rust code from Go using FFI
rust-ndarray - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
openidconnect-rs - OpenID Connect Library for Rust
rusty-machine - Machine Learning library for Rust
code - Source code for the book Rust in Action
Enzyme - High-performance automatic differentiation of LLVM and MLIR.
tangram - WebGL map rendering engine for creative cartography
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference