TensorFlow vs TensorFlow.js

Overview

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

TensorFlow.js

Stacks184

Followers378

Votes18

GitHub Stars19.0K

Forks2.0K

TensorFlow vs TensorFlow.js: What are the differences?

TensorFlow vs TensorFlow.js

TensorFlow is a popular open-source machine learning framework developed by Google, while TensorFlow.js is a JavaScript library that allows developers to run TensorFlow models directly in the browser.

Architecture: The main difference between TensorFlow and TensorFlow.js lies in their architecture. TensorFlow is designed to run on CPUs, GPUs, and TPUs, while TensorFlow.js is specifically designed to run in the browser using WebGL, which enables high-performance GPU-accelerated computations.
Deployment: TensorFlow models are typically deployed on remote servers or local machines, making them accessible through APIs or command-line interfaces. On the other hand, TensorFlow.js allows models to be deployed and run directly in the browser without the need for a server, enabling client-side machine learning applications.
Language Support: TensorFlow supports multiple programming languages, including Python, C++, and JavaScript. TensorFlow.js, as its name suggests, is focused on JavaScript and allows developers to build and deploy machine learning models using JavaScript code.
Model Size: TensorFlow.js models tend to have smaller sizes compared to traditional TensorFlow models. This is important for browser-based applications where minimizing the model size is crucial for faster loading times and reduced bandwidth consumption.
Training Capability: TensorFlow provides a comprehensive set of tools and APIs for model training, including distributed training across multiple devices and servers. While TensorFlow.js also supports training, it is primarily used for deploying pre-trained models and making predictions in the browser.
Community and Ecosystem: TensorFlow has a large and active community of developers and researchers, with extensive libraries, pre-trained models, and frameworks built around it. TensorFlow.js, being a relative newcomer, has a smaller but growing community, with a more focused ecosystem around browser-based machine learning.

In summary, TensorFlow and TensorFlow.js differ in their architecture, deployment options, language support, model size, training capability, and community ecosystem. TensorFlow is a versatile machine learning framework that can run on different hardware devices, while TensorFlow.js is specifically designed for running models in the browser using JavaScript, making it accessible to a wider range of developers and enabling client-side machine learning applications.

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Advice on TensorFlow, TensorFlow.js

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	TensorFlow.js
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.	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
Statistics
GitHub Stars 192.3K	GitHub Stars 19.0K
GitHub Forks 74.9K	GitHub Forks 2.0K
Stacks 3.9K	Stacks 184
Followers 3.5K	Followers 378
Votes 106	Votes 18
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 6 Open Source 5 NodeJS Powered 2 Deploy python ML model directly into javascript 1 Cost - no server needed for inference 1 Easy to share and use - get more eyes on your research
Integrations
JavaScript	JavaScript

What are some alternatives to TensorFlow, TensorFlow.js?

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.

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.

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.

Related Comparisons

TensorFlow vs TensorFlow.js: What are the differences?

TensorFlow vs TensorFlow.js

Architecture: The main difference between TensorFlow and TensorFlow.js lies in their architecture. TensorFlow is designed to run on CPUs, GPUs, and TPUs, while TensorFlow.js is specifically designed to run in the browser using WebGL, which enables high-performance GPU-accelerated computations.
Deployment: TensorFlow models are typically deployed on remote servers or local machines, making them accessible through APIs or command-line interfaces. On the other hand, TensorFlow.js allows models to be deployed and run directly in the browser without the need for a server, enabling client-side machine learning applications.
Language Support: TensorFlow supports multiple programming languages, including Python, C++, and JavaScript. TensorFlow.js, as its name suggests, is focused on JavaScript and allows developers to build and deploy machine learning models using JavaScript code.
Model Size: TensorFlow.js models tend to have smaller sizes compared to traditional TensorFlow models. This is important for browser-based applications where minimizing the model size is crucial for faster loading times and reduced bandwidth consumption.
Training Capability: TensorFlow provides a comprehensive set of tools and APIs for model training, including distributed training across multiple devices and servers. While TensorFlow.js also supports training, it is primarily used for deploying pre-trained models and making predictions in the browser.
Community and Ecosystem: TensorFlow has a large and active community of developers and researchers, with extensive libraries, pre-trained models, and frameworks built around it. TensorFlow.js, being a relative newcomer, has a smaller but growing community, with a more focused ecosystem around browser-based machine learning.

TensorFlow vs TensorFlow.js

Overview

TensorFlow vs TensorFlow.js: What are the differences?