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  1. Stackups
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  4. Cross Platform Desktop Development
  5. Gluon vs JavaFX

Gluon vs JavaFX

OverviewComparisonAlternatives

Overview

JavaFX
JavaFX
Stacks280
Followers418
Votes11
Gluon
Gluon
Stacks29
Followers80
Votes3
GitHub Stars2.3K
Forks219

Gluon vs JavaFX: What are the differences?

Introduction

Gluon and JavaFX are both frameworks that are used for developing cross-platform applications. However, there are key differences between the two.

  1. Technology Stack: Gluon is built on top of JavaFX and provides additional features and functionality, making it a higher-level framework. JavaFX, on the other hand, is a lower-level framework that provides core functionalities for building user interfaces.

  2. Platform Compatibility: Gluon is designed to work with a wide range of platforms, including desktop, mobile, and embedded devices. This makes it suitable for developing applications that can run on multiple platforms. JavaFX, on the other hand, is primarily designed for desktop applications and has limited support for mobile and embedded platforms.

  3. Development Approach: Gluon follows a declarative programming approach, where the UI components and their properties are defined using a markup language like FXML or CSS. This allows for a separation of the UI and application logic. JavaFX, on the other hand, follows an imperative programming approach, where the UI components and their properties are defined using Java code.

  4. Tooling Support: Gluon provides additional tooling support, such as the Gluon Scene Builder, which is a visual layout tool for designing UIs. It also provides plugins for IDEs like Eclipse and IntelliJ IDEA, which make it easier to develop and debug Gluon applications. JavaFX, on the other hand, has limited tooling support, with the primary development tool being the JavaFX Scene Builder.

  5. Community and Ecosystem: Gluon has a relatively smaller community and ecosystem compared to JavaFX. This means that there may be limited resources, libraries, and third-party integrations available for Gluon. In contrast, JavaFX has a larger community and ecosystem, with a wide range of resources, libraries, and integrations readily available.

  6. Deployment and Distribution: Gluon provides tools and services for easy deployment and distribution of applications, including the ability to package applications as native executables for different platforms. JavaFX, on the other hand, requires manual setup and configuration for deployment and distribution, and does not provide native packaging options out of the box.

In summary, Gluon is a higher-level framework with additional features and support for multiple platforms, while JavaFX is a lower-level framework primarily designed for desktop applications. Gluon follows a declarative programming approach and provides additional tooling support, but has a smaller community and ecosystem compared to JavaFX. Gluon also provides easier deployment and distribution options compared to JavaFX.

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Detailed Comparison

JavaFX
JavaFX
Gluon
Gluon

It is a set of graphics and media packages that enables developers to design, create, test, debug, and deploy rich client applications that operate consistently across diverse platforms.

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

-
Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.;Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.;Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.;High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides.
Statistics
GitHub Stars
-
GitHub Stars
2.3K
GitHub Forks
-
GitHub Forks
219
Stacks
280
Stacks
29
Followers
418
Followers
80
Votes
11
Votes
3
Pros & Cons
Pros
  • 11
    Light
Cons
  • 1
    Community support less than qt
  • 1
    Complicated
Pros
  • 3
    Good learning materials

What are some alternatives to JavaFX, Gluon?

Electron

Electron

With Electron, creating a desktop application for your company or idea is easy. Initially developed for GitHub's Atom editor, Electron has since been used to create applications by companies like Microsoft, Facebook, Slack, and Docker. The Electron framework lets you write cross-platform desktop applications using JavaScript, HTML and CSS. It is based on io.js and Chromium and is used in the Atom editor.

TensorFlow

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Sciter

Sciter

It brings a stack of web technologies to desktop UI development. Web designers, and developers, can reuse their experience and expertise in creating modern looking desktop applications.

wxWidgets

wxWidgets

It is a C++ library that lets developers create applications for Windows, macOS, Linux and other platforms with a single code base. It has popular language bindings for Python, Perl, Ruby and many other languages, and unlike other cross-platform toolkits, it gives applications a truly native look and feel because it uses the platform's native API rather than emulating the GUI. It's also extensive, free, open-source and mature.

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

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