Surge vs TensorFlow

Overview

Surge

Stacks91

Followers176

Votes60

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Surge vs TensorFlow: What are the differences?

What is Surge? Static web publishing for Front-End Developers. Surge makes it easy for developers to deploy projects to a production-quality CDN through Grunt, Gulp, npm.

What is TensorFlow? Open Source Software Library for Machine Intelligence. 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.

Surge belongs to "Static Web Hosting" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".

"Free plan" is the primary reason why developers consider Surge over the competitors, whereas "High Performance" was stated as the key factor in picking TensorFlow.

According to the StackShare community, TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to Surge, which is listed in 9 company stacks and 13 developer stacks.

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Advice on Surge, TensorFlow

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

Detailed Comparison

Surge	TensorFlow
Surge makes it easy for developers to deploy projects to a production-quality CDN through Grunt, Gulp, npm.	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.
Statistics
GitHub Stars -	GitHub Stars 192.3K
GitHub Forks -	GitHub Forks 74.9K
Stacks 91	Stacks 3.9K
Followers 176	Followers 3.5K
Votes 60	Votes 106
Pros & Cons
Pros 18 Free plan 13 Simple 11 Free custom domain support 10 Deployment via command line 3 Smart about urls Cons 1 No free redirects	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
Integrations
No integrations available	JavaScript

What are some alternatives to Surge, TensorFlow?

GitHub Pages

Public webpages hosted directly from your GitHub repository. Just edit, push, and your changes are live.

DomainRacer

It is a blazing fast hosting solution that provides Customer Satisfaction driven Web Hosting services since 2016.

Netlify

Netlify is smart enough to process your site and make sure all assets gets optimized and served with perfect caching-headers from a cookie-less domain. We make sure your HTML is served straight from our CDN edge nodes without any round-trip to our backend servers and are the only ones to give you instant cache invalidation when you push a new deploy. Netlify is also the only static hosting service with integrated continuous deployment.

Vercel

A cloud platform for serverless deployment. It enables developers to host websites and web services that deploy instantly, scale automatically, and require no supervision, all with minimal configuration.

Webflow

Webflow is a responsive design tool that lets you design, build, and publish websites in an intuitive interface. Clean code included!

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.

Divshot

Divshot makes building and hosting front-end web applications simple. Build locally and deploy using a simple command-line interface. Divshot supports multiple environments, pushState routing, atomic deploys, and more.

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.

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Surge vs TensorFlow: What are the differences?

What is Surge? Static web publishing for Front-End Developers. Surge makes it easy for developers to deploy projects to a production-quality CDN through Grunt, Gulp, npm.

Surge belongs to "Static Web Hosting" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".

"Free plan" is the primary reason why developers consider Surge over the competitors, whereas "High Performance" was stated as the key factor in picking TensorFlow.