TensorFlow vs Cortex.dev Comparison | StackShare

Overview

TensorFlow

Stacks3.8K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Cortex.dev

Stacks7

Followers19

Votes0

GitHub Stars8.0K

Forks604

TensorFlow vs Cortex.dev: What are the differences?

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; Cortex.dev: Deploy machine learning models in production. It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

TensorFlow and Cortex.dev can be categorized as "Machine Learning" tools.

TensorFlow and Cortex.dev are both open source tools. It seems that TensorFlow with 137K GitHub stars and 78.3K forks on GitHub has more adoption than Cortex.dev with 1.42K GitHub stars and 69 GitHub forks.

Advice on TensorFlow, Cortex.dev

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

TensorFlow	Cortex.dev
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.	It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.
-	Autoscaling; Supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more; CPU / GPU support; Rolling updates; Log streaming; Prediction monitoring; Minimal declarative configuration
Statistics
GitHub Stars 192.3K	GitHub Stars 8.0K
GitHub Forks 74.9K	GitHub Forks 604
Stacks 3.8K	Stacks 7
Followers 3.5K	Followers 19
Votes 106	Votes 0
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	No community feedback yet
Integrations
JavaScript	PyTorch XGBoost scikit-learn Keras

What are some alternatives to TensorFlow, Cortex.dev?

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.

PredictionIO

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