Gluon vs Keras vs TensorFlow

Overview

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Keras

Stacks1.1K

Followers1.1K

Votes22

Gluon

Stacks29

Followers80

Votes3

GitHub Stars2.3K

Forks219

Gluon vs Keras vs TensorFlow: What are the differences?

The key differences between Gluon, Keras, and TensorFlow are as follows:

1. **Ease of use**: Gluon provides an imperative programming interface that allows for dynamic graph creation on the fly, making it beginner-friendly and easier for debugging. Keras, on the other hand, offers a higher-level, more user-friendly API for building neural networks, while TensorFlow provides both high-level APIs like Keras and low-level functionalities for more control and customization.
2. **Performance**: Gluon and Keras are often preferred for rapid prototyping and experiments due to their simplicity, while TensorFlow is known for its scalability and performance, making it suitable for large-scale production deployment and research projects.
3. **Deployment options**: TensorFlow offers more deployment options, such as TensorFlow Serving for serving models in production, TensorFlow Lite for mobile and embedded devices, and TensorFlow.js for running models in the browser, compared to Gluon and Keras which have limited deployment options.
4. **Community support**: TensorFlow has a larger and more active community compared to Gluon and Keras, providing a wider range of resources, tutorials, and pre-trained models, making it easier to find solutions to problems and stay updated on the latest advancements in the field.
5. **Flexibility and customization**: TensorFlow allows for more fine-grained control and customization of models compared to Gluon and Keras, enabling researchers and developers to experiment with different architectures, loss functions, and optimization techniques with more flexibility.
6. **Backend support**: Both Gluon and Keras support multiple backend engines such as TensorFlow, Theano, and Microsoft Cognitive Toolkit, allowing users to switch between different backends seamlessly, while TensorFlow is primarily focused on its own backend but can be integrated with other deep learning libraries.

In Summary, Gluon, Keras, and TensorFlow offer different levels of ease of use, performance, deployment options, community support, flexibility, and backend support for building and deploying neural networks.

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Advice on TensorFlow, Keras, Gluon

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

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.64k views4.64k

Comments

Detailed Comparison

TensorFlow	Keras	Gluon
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.	Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/	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.
-	neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU	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 192.3K	GitHub Stars -	GitHub Stars 2.3K
GitHub Forks 74.9K	GitHub Forks -	GitHub Forks 219
Stacks 3.9K	Stacks 1.1K	Stacks 29
Followers 3.5K	Followers 1.1K	Followers 80
Votes 106	Votes 22	Votes 3
Pros & Cons
Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	Pros 8 Quality Documentation 7 Easy and fast NN prototyping 7 Supports Tensorflow and Theano backends Cons 4 Hard to debug	Pros 3 Good learning materials
Integrations
JavaScript	scikit-learn Python	No integrations available

What are some alternatives to TensorFlow, Keras, Gluon?

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 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.

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

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

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

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Comet.ml

Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.

Related Comparisons

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Keras

Stacks1.1K

Followers1.1K

Votes22

Gluon

Stacks29

Followers80

Votes3

GitHub Stars2.3K

Forks219

Gluon vs Keras vs TensorFlow: What are the differences?

The key differences between Gluon, Keras, and TensorFlow are as follows:

1. **Ease of use**: Gluon provides an imperative programming interface that allows for dynamic graph creation on the fly, making it beginner-friendly and easier for debugging. Keras, on the other hand, offers a higher-level, more user-friendly API for building neural networks, while TensorFlow provides both high-level APIs like Keras and low-level functionalities for more control and customization.
2. **Performance**: Gluon and Keras are often preferred for rapid prototyping and experiments due to their simplicity, while TensorFlow is known for its scalability and performance, making it suitable for large-scale production deployment and research projects.
3. **Deployment options**: TensorFlow offers more deployment options, such as TensorFlow Serving for serving models in production, TensorFlow Lite for mobile and embedded devices, and TensorFlow.js for running models in the browser, compared to Gluon and Keras which have limited deployment options.
4. **Community support**: TensorFlow has a larger and more active community compared to Gluon and Keras, providing a wider range of resources, tutorials, and pre-trained models, making it easier to find solutions to problems and stay updated on the latest advancements in the field.
5. **Flexibility and customization**: TensorFlow allows for more fine-grained control and customization of models compared to Gluon and Keras, enabling researchers and developers to experiment with different architectures, loss functions, and optimization techniques with more flexibility.
6. **Backend support**: Both Gluon and Keras support multiple backend engines such as TensorFlow, Theano, and Microsoft Cognitive Toolkit, allowing users to switch between different backends seamlessly, while TensorFlow is primarily focused on its own backend but can be integrated with other deep learning libraries.

Advice on TensorFlow, Keras, Gluon

Developer at DCSIL

Oct 11, 2020

Decided

99.3k views99.3k

Comments

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Good work, Philippe

4.64k views4.64k

Comments

Detailed Comparison

TensorFlow	Keras	Gluon
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.	Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/	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.
-	neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU	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 192.3K	GitHub Stars -	GitHub Stars 2.3K
GitHub Forks 74.9K	GitHub Forks -	GitHub Forks 219
Stacks 3.9K	Stacks 1.1K	Stacks 29
Followers 3.5K	Followers 1.1K	Followers 80
Votes 106	Votes 22	Votes 3
Pros & Cons
Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	Pros 8 Quality Documentation 7 Easy and fast NN prototyping 7 Supports Tensorflow and Theano backends Cons 4 Hard to debug	Pros 3 Good learning materials
Integrations
JavaScript	scikit-learn Python	No integrations available

Gluon vs Keras vs TensorFlow

Overview