rubixml / server Goto Github PK

View Code? Open in Web Editor NEW

61.0 8.0 12.0 18.75 MB

A standalone inference server for trained Rubix ML estimators.

Home Page: https://rubixml.com

License: MIT License

PHP 73.78% HTML 0.84% JavaScript 2.67% Vue 19.33% SCSS 0.89% TypeScript 2.50%

machine-learning http-server model-server infrastructure api model-deployment microservice json-api php rest-api

server's Introduction

Rubix ML

A high-level machine learning and deep learning library for the PHP language.

Developer-friendly API is delightful to use
40+ supervised and unsupervised learning algorithms
Support for ETL, preprocessing, and cross-validation
Open source and free to use commercially

Installation

Install Rubix ML into your project using Composer:

$ composer require rubix/ml

Requirements

PHP 7.4 or above

Optional

GD extension for image support
Mbstring extension for fast multibyte string manipulation
SVM extension for Support Vector Machine engine (libsvm)
PDO extension for relational database support
GraphViz for graph visualization

Documentation

Read the latest docs here.

What is Rubix ML?

Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.

Getting Started

If you are new to machine learning, we recommend taking a look at the What is Machine Learning? section to get started. If you are already familiar with basic ML concepts, you can browse the basic introduction for a brief look at a typical Rubix ML project. From there, you can browse the official tutorials below which range from beginner to advanced skill level.

Tutorials & Example Projects

Check out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.

Interact With The Community

Join Our Telegram Channel

Contributing

See CONTRIBUTING.md for guidelines.

License

The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.

server's People

Contributors

Stargazers

Watchers

Forkers

dovidezra anhhai986 muhammadzakirramadhan andrewdalpino torchello marciopocebon mellorison mtsakharov danilvaliev boorinio mcharytoniuk gogomann

server's Issues

Project Borg (Kubernetes)

This ticket is to determine if Rubix Server can be used with Kubernetes to deploy and autoscale instances.

In the process we should ...

A. Determine if we need to make any changes to the library to either make it work or make it easier to work
B. Create a container for other developers to use
C. Produce documentation for the procedure to be added to the README

Retraining or partially training the served learner?

Hi @andrewdalpino! I am exploring the RubixML Server currently and finding it really practical.I am curios about one aspect though. Here is the example case:

Let us say I got a REST server that accepts requests from client applications (non-php) and returns predictions.

$server = new RESTServer(‘localhost’, 8080);
$server->serve($estimator);

Suppose, I also have a Tester client (php) that might sit within the same environment (same machine server ) or remote location. This Tester client’s job is to periodically send samples to the REST server, get back predictions, check the accuracy and if the accuracy is below the certain % retrain the estimator instance. My question is how would you go about doing it with a current implementation considering the fact that you need to reach to estimator instance?
I could extend the REST server class, add a new route to a new, lets say TrainerController and retrain the model. That’s done once the request is in. But how do you deliver that request?
I see three ways: a) In Tester client create completely new Guzzle client, package the data in as json in body, set up headers and send to REST server
b) a middleware with a conditional check if RPC or REST request etc
c) Implement the existing Client interface with a slightly customized implementation logic and let this RESTclient handle all the future request to REST Server.

I think c option looks way cleaner and much reusable. So now the library will have two clients RPCClient and RESTclient. I am just curios if you are considering to add anything like this in future updates? Or would you rather leave it up to an individual developer to come up with ways of figuring this out?

thanks

Backpressure

Currently, the RPC client supports the Retry-After header when accompanied by a Too Many Requests (429) and SERVICE Unavailable (503) response code. This allows the server to determine how much time it needs the client to backoff before it can handle another request. This 'backpressure' mechanism allows server and client to work together to stabilize the flow of requests coming in to the server under heavy load. This enhancement is to add a new RateLimiter middleware that limits the number of requests per minute and estimates the wait time (the amount of backpressure) for a client when the threshold has been reached.