Sphinx vs TensorFlow

Overview

Sphinx

Stacks1.1K

Followers300

Votes32

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Sphinx vs TensorFlow: What are the differences?

What is Sphinx? Open source full text search server, designed from the ground up with performance, relevance (aka search quality), and integration simplicity in mind. Sphinx lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with Sphinx pretty much as with a database server. A variety of text processing features enable fine-tuning Sphinx for your particular application requirements, and a number of relevance functions ensures you can tweak search quality as well.

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.

Sphinx belongs to "Search Engines" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".

"Fast" is the top reason why over 12 developers like Sphinx, while over 16 developers mention "High Performance" as the leading cause for choosing TensorFlow.

Uber Technologies, 9GAG, and Postmates are some of the popular companies that use TensorFlow, whereas Sphinx is used by Webedia, Grooveshark, and Ansible. TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to Sphinx, which is listed in 38 company stacks and 14 developer stacks.

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Advice on Sphinx, TensorFlow

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.

99.4k views99.4k

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

4.65k views4.65k

Comments

Detailed Comparison

Sphinx	TensorFlow
It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.	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.
Output formats: HTML (including Windows HTML Help), LaTeX (for printable PDF versions), ePub, Texinfo, manual pages, plain text;Extensive cross-references: semantic markup and automatic links for functions, classes, citations, glossary terms and similar pieces of information;Hierarchical structure: easy definition of a document tree, with automatic links to siblings, parents and children;Automatic indices: general index as well as a language-specific module indices;Code handling: automatic highlighting using the Pygments highlighter;Extensions: automatic testing of code snippets, inclusion of docstrings from Python modules (API docs), and more	-
Statistics
GitHub Stars -	GitHub Stars 192.3K
GitHub Forks -	GitHub Forks 74.9K
Stacks 1.1K	Stacks 3.9K
Followers 300	Followers 3.5K
Votes 32	Votes 106
Pros & Cons
Pros 16 Fast 9 Simple deployment 6 Open source 1 Lots of extentions	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
DevDocs Zapier Google Drive Google Chrome Dropbox	JavaScript

What are some alternatives to Sphinx, TensorFlow?

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

MkDocs

It builds completely static HTML sites that you can host on GitHub pages, Amazon S3, or anywhere else you choose. There's a stack of good looking themes available. The built-in dev-server allows you to preview your documentation as you're writing it. It will even auto-reload and refresh your browser whenever you save your changes.

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

"Fast" is the top reason why over 12 developers like Sphinx, while over 16 developers mention "High Performance" as the leading cause for choosing TensorFlow.

Sphinx vs TensorFlow