Overview

Gluon

Stacks29

Followers80

Votes3

GitHub Stars2.3K

Forks219

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

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.

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.
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.
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.
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.
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.
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.

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Advice on Gluon, PyTorch

Developer at DCSIL

Oct 11, 2020

Decided

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.

99.3k views99.3k

Comments

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

cfvedova

Oct 10, 2020

Decided

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.

72.8k views72.8k

Comments

Detailed Comparison

Gluon	PyTorch
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.	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.
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.	Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system
Statistics
GitHub Stars 2.3K	GitHub Stars 94.7K
GitHub Forks 219	GitHub Forks 25.8K
Stacks 29	Stacks 1.6K
Followers 80	Followers 1.5K
Votes 3	Votes 43
Pros & Cons
Pros 3 Good learning materials	Pros 15 Easy to use 11 Developer Friendly 10 Easy to debug 7 Sometimes faster than TensorFlow Cons 3 Lots of code 1 It eats poop
Integrations
No integrations available	Python

What are some alternatives to Gluon, PyTorch?

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

Keras

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

Kubeflow

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TensorFlow.js

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Polyaxon

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Streamlit

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MLflow

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H2O

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PredictionIO

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