Gluon聽vs聽PyTorch聽vs聽TensorFlow

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Gluon

21
60
+ 1
2
PyTorch

827
922
+ 1
33
TensorFlow

2.4K
2.6K
+ 1
75
Decisions about Gluon, PyTorch, and TensorFlow

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Fabian Ulmer
Software Developer at Hestia | 3 upvotes 路 8.9K views

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

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Xi Huang
Developer at University of Toronto | 8 upvotes 路 38.7K views

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

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Pros of Gluon
Pros of PyTorch
Pros of TensorFlow
  • 2
    Good learning materials
  • 11
    Easy to use
  • 9
    Developer Friendly
  • 8
    Easy to debug
  • 5
    Sometimes faster than TensorFlow
  • 23
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful

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Cons of Gluon
Cons of PyTorch
Cons of TensorFlow
    Be the first to leave a con
    • 2
      Lots of code
    • 8
      Hard
    • 5
      Hard to debug
    • 1
      Documentation not very helpful

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    What is Gluon?

    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.

    What is 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.

    What is 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.

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      What are some alternatives to Gluon, PyTorch, and TensorFlow?
      Keras
      Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
      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.
      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
      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 is a mobile app SDK to help developers and designers build modern mobile apps for iOS and Android.
      See all alternatives
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