Keras vs Chainer: What are the differences?
Developers describe Keras as "Deep Learning library for Theano and TensorFlow". Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/. On the other hand, Chainer is detailed as "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.
Keras and Chainer belong to "Machine Learning Tools" category of the tech stack.
Some of the features offered by Keras are:
- neural networks API
- Allows for easy and fast prototyping
- Convolutional networks support
On the other hand, Chainer provides the following key features:
- Supports CUDA computation
- Runs on multiple GPUs
- Supports various network architectures
Keras and Chainer are both open source tools. It seems that Keras with 43.5K GitHub stars and 16.5K forks on GitHub has more adoption than Chainer with 4.98K GitHub stars and 1.32K GitHub forks.
What is Chainer?
What is Keras?
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Why do developers choose Chainer?
What are the cons of using Chainer?
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Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)