Propel vs numericaal

Overview

Propel

Stacks3

Followers18

Votes0

GitHub Stars2.7K

Forks73

numericaal

Stacks0

Followers18

Votes0

numericaal vs Propel: What are the differences?

What is numericaal? Machine learning for mobile & IoT made easy. numericaal automates model optimization and management so you can focus on data and training.

What is Propel? Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

numericaal and Propel can be primarily classified as "Machine Learning" tools.

Some of the features offered by numericaal are:

MODEL RESOURCE OPTIMIZATION - We automatically run multiple toolchains to give you the best speed, power and memory tradeoff on every model change.
CROSS-PLATFORM MODEL ANALYTICS - We measure on-device speed and power usage to help you evaluate and compare models across hardware platforms.
BOTTLENECK IDENTIFICATION - We help you pinpoint performance bottlenecks and focus your model optimization on layers that matter the most.

On the other hand, Propel provides the following key features:

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

Propel is an open source tool with 2.81K GitHub stars and 81 GitHub forks. Here's a link to Propel's open source repository on GitHub.

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Detailed Comparison

Propel	numericaal
Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.	numericaal automates model optimization and management so you can focus on data and training.
Run anywhere, in the browser or natively from Node; Target multiple GPUs and make TCP connections; PhD optional	MODEL RESOURCE OPTIMIZATION - We automatically run multiple toolchains to give you the best speed, power and memory tradeoff on every model change.; CROSS-PLATFORM MODEL ANALYTICS - We measure on-device speed and power usage to help you evaluate and compare models across hardware platforms.; BOTTLENECK IDENTIFICATION - We help you pinpoint performance bottlenecks and focus your model optimization on layers that matter the most.
Statistics
GitHub Stars 2.7K	GitHub Stars -
GitHub Forks 73	GitHub Forks -
Stacks 3	Stacks 0
Followers 18	Followers 18
Votes 0	Votes 0
Integrations
JavaScript Node.js TensorFlow	No integrations available

What are some alternatives to Propel, numericaal?

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.