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Gluon vs PyTorch: What are the differences?

Introduction

Gluon and PyTorch are both popular deep learning libraries used for building and training neural networks. While they have similar goals and functionalities, there are important differences between the two.

  1. Computation Graph: In PyTorch, the computation graph is defined dynamically during runtime, allowing for more flexibility in modifying the graph. On the other hand, Gluon uses a static computation graph, which is defined upfront and cannot be modified. This difference makes PyTorch more suitable for dynamic architectures and Gluon more suitable for static architectures.

  2. Model Creation: PyTorch follows an imperative programming style, where developers can define and modify models on-the-fly. This makes it easier to experiment and debug models. Gluon, on the other hand, follows a declarative programming style, where models need to be defined using predefined building blocks. This declarative approach provides better abstraction and is useful for large-scale and production-level models.

  3. Hybrid Frontend: One unique feature of Gluon is its hybrid frontend, which allows users to switch seamlessly between imperative and declarative programming. This makes it possible to combine the benefits of both styles, enabling efficient prototyping and deployment. PyTorch, on the other hand, does not have a built-in hybrid frontend, although it provides flexibility through its dynamic graph.

  4. Ease of Use: Gluon is designed to be easy to use and beginner-friendly. It provides a higher-level API that simplifies the process of building neural networks. PyTorch, although powerful, has a steeper learning curve and requires more familiarity with programming concepts.

  5. Community Support: PyTorch has gained significant popularity in the research community, leading to a large and active community of users, contributors, and libraries. Gluon, while growing in popularity, does not have the same level of community support as PyTorch. This means that there may be fewer resources and libraries available for Gluon compared to PyTorch.

  6. Backend Support: Gluon supports both Apache MXNet and Apache TensorFlow as backend engines, giving users the flexibility to choose the underlying framework. PyTorch, on the other hand, is built on its own backend engine, making it more tightly integrated but less flexible in terms of backend options.

In summary, Gluon and PyTorch differ in terms of their computation graph, programming style, hybrid frontend, ease of use, community support, and backend support. Both libraries have their own strengths and weaknesses, and the choice between them depends on the specific requirements and preferences of the user.

Decisions about Gluon and PyTorch

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 · 50.2K 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 · 92.3K 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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A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

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Pros of Gluon
Pros of PyTorch
  • 3
    Good learning materials
  • 15
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow

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Cons of Gluon
Cons of PyTorch
    Be the first to leave a con
    • 3
      Lots of code
    • 1
      It eats poop

    Sign up to add or upvote consMake informed product decisions

    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.

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