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  1. Stackups
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  3. Development & Training Tools
  4. Machine Learning Tools
  5. Cortex.dev vs Propel

Cortex.dev vs Propel

OverviewComparisonAlternatives

Overview

Propel
Propel
Stacks3
Followers18
Votes0
GitHub Stars2.7K
Forks73
Cortex.dev
Cortex.dev
Stacks7
Followers19
Votes0
GitHub Stars8.0K
Forks604

Propel vs Cortex.dev: What are the differences?

Propel: Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript; 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.

Propel and Cortex.dev belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Propel are:

  • Run anywhere, in the browser or natively from Node
  • Target multiple GPUs and make TCP connections
  • PhD optional

On the other hand, Cortex.dev provides the following key features:

  • Autoscaling
  • Supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more
  • CPU / GPU support

Propel and Cortex.dev are both open source tools. Propel with 2.8K GitHub stars and 80 forks on GitHub appears to be more popular than Cortex.dev with 1.42K GitHub stars and 69 GitHub forks.

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Detailed Comparison

Propel
Propel
Cortex.dev
Cortex.dev

Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

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.

Run anywhere, in the browser or natively from Node; Target multiple GPUs and make TCP connections; PhD optional
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
2.7K
GitHub Stars
8.0K
GitHub Forks
73
GitHub Forks
604
Stacks
3
Stacks
7
Followers
18
Followers
19
Votes
0
Votes
0
Integrations
JavaScript
JavaScript
Node.js
Node.js
TensorFlow
TensorFlow
TensorFlow
TensorFlow
PyTorch
PyTorch
XGBoost
XGBoost
scikit-learn
scikit-learn
Keras
Keras

What are some alternatives to Propel, Cortex.dev?

TensorFlow

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

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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

Keras

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

Kubeflow

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

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

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

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

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

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.

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