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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Chainer vs OpenNN

Chainer vs OpenNN

OverviewComparisonAlternatives

Overview

Chainer
Chainer
Stacks17
Followers23
Votes0
GitHub Stars5.9K
Forks1.4K
OpenNN
OpenNN
Stacks5
Followers18
Votes0

Chainer vs OpenNN: What are the differences?

What is Chainer? A Powerful, Flexible, and Intuitive Framework for Neural Networks. It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

What is OpenNN? *A neural networks C++/Python library *. It is a free neural networks library for advanced analytics. It has solved many real-world applications in energy, marketing, health and more.

Chainer and OpenNN can be primarily classified as "Machine Learning" tools.

Some of the features offered by Chainer are:

  • Supports CUDA computation
  • Runs on multiple GPUs
  • Supports various network architectures

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

  • Regression
  • Classification
  • Forecasting

Chainer is an open source tool with 5.12K GitHub stars and 1.35K GitHub forks. Here's a link to Chainer's open source repository on GitHub.

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Detailed Comparison

Chainer
Chainer
OpenNN
OpenNN

It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

It is a free neural networks library for advanced analytics. It has solved many real-world applications in energy, marketing, health and more.

Supports CUDA computation;Runs on multiple GPUs ;Supports various network architectures ;Supports per-batch architectures
Regression; Classification; Forecasting
Statistics
GitHub Stars
5.9K
GitHub Stars
-
GitHub Forks
1.4K
GitHub Forks
-
Stacks
17
Stacks
5
Followers
23
Followers
18
Votes
0
Votes
0
Integrations
Python
Python
NumPy
NumPy
CUDA
CUDA
Python
Python
C++
C++

What are some alternatives to Chainer, OpenNN?

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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