Alternatives to Gluon logo

Alternatives to Gluon

TensorFlow, Keras, Photon, PyTorch, and JavaFX are the most popular alternatives and competitors to Gluon.
27
72
+ 1
2

What is Gluon and what are its top alternatives?

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.
Gluon is a tool in the Machine Learning Tools category of a tech stack.
Gluon is an open source tool with 2.3K GitHub stars and 229 GitHub forks. Here’s a link to Gluon's open source repository on GitHub

Top Alternatives to Gluon

  • 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. ...

  • Keras
    Keras

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

  • Photon
    Photon

    The fastest way to build beautiful Electron apps using simple HTML and CSS. Underneath it all is Electron. Originally built for GitHub's Atom text editor, Electron is the easiest way to build cross-platform desktop applications. ...

  • 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. ...

  • JavaFX
    JavaFX

    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. ...

  • MXNet
    MXNet

    A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. ...

  • Flutter
    Flutter

    Flutter is a mobile app SDK to help developers and designers build modern mobile apps for iOS and Android. ...

  • 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. ...

Gluon alternatives & related posts

TensorFlow logo

TensorFlow

2.9K
3.1K
80
Open Source Software Library for Machine Intelligence
2.9K
3.1K
+ 1
80
PROS OF TENSORFLOW
  • 26
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 3
    High level abstraction
  • 2
    Powerful
  • 2
    Easy to use
CONS OF TENSORFLOW
  • 9
    Hard
  • 6
    Hard to debug
  • 1
    Documentation not very helpful

related TensorFlow posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.4M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

See more
Keras logo

Keras

960
1K
17
Deep Learning library for Theano and TensorFlow
960
1K
+ 1
17
PROS OF KERAS
  • 6
    Quality Documentation
  • 6
    Easy and fast NN prototyping
  • 5
    Supports Tensorflow and Theano backends
CONS OF KERAS
  • 3
    Hard to debug

related Keras posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.4M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

See more
Photon logo

Photon

15
72
0
Framework for Electron apps
15
72
+ 1
0
PROS OF PHOTON
    Be the first to leave a pro
    CONS OF PHOTON
      Be the first to leave a con

      related Photon posts

      PyTorch logo

      PyTorch

      1.1K
      1.2K
      42
      A deep learning framework that puts Python first
      1.1K
      1.2K
      + 1
      42
      PROS OF PYTORCH
      • 14
        Easy to use
      • 11
        Developer Friendly
      • 10
        Easy to debug
      • 7
        Sometimes faster than TensorFlow
      CONS OF PYTORCH
      • 3
        Lots of code

      related PyTorch posts

      Server side

      We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

      • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

      • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

      • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

      Client side

      • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

      • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

      • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

      Cache

      • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

      Database

      • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

      Infrastructure

      • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

      Other Tools

      • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

      • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

      See more
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.4M views

      Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

      At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

      TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

      Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

      https://eng.uber.com/horovod/

      (Direct GitHub repo: https://github.com/uber/horovod)

      See more
      JavaFX logo

      JavaFX

      235
      322
      9
      A Java library for building Rich Internet Applications
      235
      322
      + 1
      9
      PROS OF JAVAFX
      • 9
        Light
      CONS OF JAVAFX
      • 1
        Community support less than qt
      • 1
        Complicated

      related JavaFX posts

      MXNet logo

      MXNet

      43
      74
      1
      A flexible and efficient library for deep learning
      43
      74
      + 1
      1
      PROS OF MXNET
      • 1
        User friendly
      CONS OF MXNET
        Be the first to leave a con

        related MXNet posts

        Flutter logo

        Flutter

        10.2K
        10.6K
        1K
        Cross-platform mobile framework from Google
        10.2K
        10.6K
        + 1
        1K
        PROS OF FLUTTER
        • 124
          Hot Reload
        • 104
          Cross platform
        • 97
          Performance
        • 80
          Backed by Google
        • 66
          Compiled into Native Code
        • 52
          Fast Development
        • 51
          Open Source
        • 46
          Fast Prototyping
        • 43
          Expressive and Flexible UI
        • 40
          Single Codebase
        • 35
          Reactive Programming
        • 30
          Material Design
        • 24
          Widget-based
        • 23
          Target to Fuchsia
        • 23
          Dart
        • 17
          IOS + Android
        • 14
          Easy to learn
        • 13
          Tooling
        • 13
          You can use it as mobile, web, Server development
        • 13
          Great CLI Support
        • 11
          Good docs & sample code
        • 11
          Debugging quickly
        • 11
          Have built-in Material theme
        • 10
          Target to Android
        • 10
          Support by multiple IDE: Android Studio, VS Code, XCode
        • 10
          Community
        • 9
          Easy Testing Support
        • 9
          Written by Dart, which is easy to read code
        • 8
          Have built-in Cupertino theme
        • 8
          Target to iOS
        • 7
          Easy to Widget Test
        • 7
          Easy to Unit Test
        • 7
          Real platform free framework of the future
        • 7
          Flutter is awesome
        • 1
          F
        CONS OF FLUTTER
        • 28
          Need to learn Dart
        • 10
          No 3D Graphics Engine Support
        • 9
          Lack of community support
        • 7
          Graphics programming
        • 6
          Lack of friendly documentation
        • 2
          Lack of promotion
        • 1
          Https://iphtechnologies.com/difference-between-flutter

        related Flutter posts

        Vaibhav Taunk
        Team Lead at Technovert · | 31 upvotes · 1.9M views

        I am starting to become a full-stack developer, by choosing and learning .NET Core for API Development, Angular CLI / React for UI Development, MongoDB for database, as it a NoSQL DB and Flutter / React Native for Mobile App Development. Using Postman, Markdown and Visual Studio Code for development.

        See more
        Shared insights
        on
        DartDartFlutterFlutter

        Hi, I'm considering building a social marketplace app on android, ios and web, Flutter seems to be a good UI framework for cross-platform apps, it's safe type, hot reload, and native compiling on native machine code (thanks to Dart). My question is, for an MVP product is it a good choice? if yes, will it be on the mid-term, long term? Or will I have to change as the users grow?

        thank you

        See more
        scikit-learn logo

        scikit-learn

        979
        990
        36
        Easy-to-use and general-purpose machine learning in Python
        979
        990
        + 1
        36
        PROS OF SCIKIT-LEARN
        • 20
          Scientific computing
        • 16
          Easy
        CONS OF SCIKIT-LEARN
        • 1
          Limited

        related scikit-learn posts