Torch vs Propel

Overview

Torch

Stacks355

Followers61

Votes0

GitHub Stars9.1K

Forks2.4K

Propel

Stacks3

Followers18

Votes0

GitHub Stars2.7K

Forks73

Propel vs Torch: What are the differences?

Developers describe Propel as "Machine learning for JavaScript". Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript. On the other hand, Torch is detailed as "An open-source machine learning library and a script language based on the Lua programming language". It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Propel and Torch 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, Torch provides the following key features:

A powerful N-dimensional array
Lots of routines for indexing, slicing, transposing
Amazing interface to C, via LuaJIT

Propel is an open source tool with 2.8K GitHub stars and 80 GitHub forks. Here's a link to Propel's open source repository on GitHub.

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Detailed Comparison

Torch	Propel
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.	Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.
A powerful N-dimensional array; Lots of routines for indexing, slicing, transposing; Amazing interface to C, via LuaJIT; Linear algebra routines; Neural network, and energy-based models; Numeric optimization routines; Fast and efficient GPU support; Embeddable, with ports to iOS and Android backends	Run anywhere, in the browser or natively from Node; Target multiple GPUs and make TCP connections; PhD optional
Statistics
GitHub Stars 9.1K	GitHub Stars 2.7K
GitHub Forks 2.4K	GitHub Forks 73
Stacks 355	Stacks 3
Followers 61	Followers 18
Votes 0	Votes 0
Integrations
Python SQLFlow GraphPipe Flair Pythia Databricks Comet.ml	JavaScript Node.js TensorFlow

What are some alternatives to Torch, Propel?

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 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.