Gluon vs Tensor2Tensor

Overview

Gluon

Stacks29

Followers80

Votes3

GitHub Stars2.3K

Forks219

Tensor2Tensor

Stacks4

Followers12

Votes0

GitHub Stars16.7K

Forks3.7K

Gluon vs Tensor2Tensor: What are the differences?

What is Gluon? Deep Learning API from AWS and Microsoft. 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 Tensor2Tensor? Library of deep learning models & datasets designed to make deep learning more accessible (by Google Brain). It is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. It was developed by researchers and engineers in the Google Brain team and a community of users.

Gluon and Tensor2Tensor can be primarily classified as "Machine Learning" tools.

Some of the features offered by Gluon are:

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.

On the other hand, Tensor2Tensor provides the following key features:

Many state of the art and baseline models are built-in and new models can be added easily
Many datasets across modalities - text, audio, image - available for generation and use, and new ones can be added easily
Models can be used with any dataset and input mode (or even multiple)

Tensor2Tensor is an open source tool with 9.7K GitHub stars and 2.51K GitHub forks. Here's a link to Tensor2Tensor's open source repository on GitHub.

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Detailed Comparison

Gluon	Tensor2Tensor
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.	It is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. It was developed by researchers and engineers in the Google Brain team and a community of users.
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.	Many state of the art and baseline models are built-in and new models can be added easily; Many datasets across modalities - text, audio, image - available for generation and use, and new ones can be added easily; Models can be used with any dataset and input mode (or even multiple); all modality-specific processing (e.g. embedding lookups for text tokens) is done with bottom and top transformations, which are specified per-feature in the model; Support for multi-GPU machines and synchronous (1 master, many workers) and asynchronous (independent workers synchronizing through a parameter server) distributed training; Easily swap amongst datasets and models by command-line flag with the data generation script t2t-datagen and the training script t2t-trainer; Train on Google Cloud ML and Cloud TPUs
Statistics
GitHub Stars 2.3K	GitHub Stars 16.7K
GitHub Forks 219	GitHub Forks 3.7K
Stacks 29	Stacks 4
Followers 80	Followers 12
Votes 3	Votes 0
Pros & Cons
Pros 3 Good learning materials	No community feedback yet

What are some alternatives to Gluon, Tensor2Tensor?

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.

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.

Keras

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

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.

Related Comparisons

Some of the features offered by Gluon are:

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.

On the other hand, Tensor2Tensor provides the following key features:

Many state of the art and baseline models are built-in and new models can be added easily
Many datasets across modalities - text, audio, image - available for generation and use, and new ones can be added easily
Models can be used with any dataset and input mode (or even multiple)

Tensor2Tensor is an open source tool with 9.7K GitHub stars and 2.51K GitHub forks. Here's a link to Tensor2Tensor's open source repository on GitHub.

Gluon vs Tensor2Tensor