---
title: "Why Use Keras?"
output: 
  rmarkdown::html_vignette: default
vignette: >
  %\VignetteIndexEntry{Why Use Keras?} 
  %\VignetteEngine{knitr::rmarkdown} 
  %\VignetteEncoding{UTF-8}
type: docs
repo: https://github.com/rstudio/keras
menu:
  main:
    name: "Why Use Keras?"
    identifier: "keras-why-use-keras"
    parent: "keras-using-keras"
    weight: 15
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


There are countless deep learning frameworks available today. Why use Keras rather than any other? Here are some of the areas in which Keras compares favorably to existing alternatives.

---

## Keras prioritizes developer experience
    
- Keras is an API designed for human beings, not machines. [Keras follows best practices for reducing cognitive load](https://blog.keras.io/user-experience-design-for-apis.html): it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.

- This makes Keras easy to learn and easy to use. As a Keras user, you are more productive, allowing you to try more ideas than your competition, faster -- which in turn [helps you win machine learning competitions](https://www.quora.com/Why-has-Keras-been-so-successful-lately-at-Kaggle-competitions).

- This ease of use does not come at the cost of reduced flexibility: because Keras integrates with lower-level deep learning languages (in particular TensorFlow), it enables you to implement anything you could have built in the base language. The Keras API integrates seamlessly with your TensorFlow workflows.

---

## Keras has broad adoption in the industry and the research community

With over 200,000 individual users as of November 2017, Keras has stronger adoption in both the industry and the research community than any other deep learning framework except TensorFlow itself (and Keras is commonly used in conjunction with TensorFlow).

You are already constantly interacting with features built with Keras -- it is in use at Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and many others. It is especially popular among startups that place deep learning at the core of their products.

Keras is also a favorite among deep learning researchers, coming in #2 in terms of mentions in scientific papers uploaded to the preprint server [arXiv.org](https://arxiv.org/archive/cs):

<img src='images/arxiv-mentions.png' style='width:500px; display: block; margin: 0 auto;'/>

Keras has also been adopted by researchers at large scientific organizations, in particular CERN and NASA.

---

## Keras makes it easy to turn models into products

Your Keras models can be easily deployed across a greater range of platforms than any other deep learning framework:

- On iOS, via [Apple’s CoreML](https://developer.apple.com/documentation/coreml) (Keras support officially provided by Apple)
- On Android, via the TensorFlow Android runtime. Example: [Not Hotdog app](https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3)
- In the browser, via GPU-accelerated JavaScript runtimes such as [Keras.js](https://transcranial.github.io/keras-js/#/) and [WebDNN](https://mil-tokyo.github.io/webdnn/)
- On Google Cloud, via [TensorFlow-Serving](https://www.tensorflow.org/serving/)
- In an R or Python webapp backend (such as a Shiny or Flask app)
- On the JVM, via [DL4J model import provided by SkyMind](https://deeplearning4j.org/model-import-keras)
- On Raspberry Pi

---

## Keras supports multiple backend engines and does not lock you into one ecosystem

Your Keras models can be developed with a range of different [deep learning backends](https://keras.rstudio.com/articles/backend.html). Importantly, any Keras model that only leverages built-in layers will be portable across all these backends: you can train a model with one backend, and load it with another (e.g. for deployment). Available backends include:

- The TensorFlow backend (from Google)
- The CNTK backend (from Microsoft)
- The Theano backend

Amazon is also currently working on developing a MXNet backend for Keras.

As such, your Keras model can be trained on a number of different hardware platforms beyond CPUs:

- [NVIDIA GPUs](https://developer.nvidia.com/deep-learning)
- [Google TPUs](https://cloud.google.com/tpu/), via the TensorFlow backend and Google Cloud
- OpenCL-enabled GPUs, such as those from AMD, via [the PlaidML Keras backend](https://github.com/plaidml/plaidml)

---

## Keras has strong multi-GPU support and distributed training support

- Keras has [built-in support for multi-GPU data parallelism](https://keras.rstudio.com/reference/multi_gpu_model.html)
- [Horovod](https://github.com/uber/horovod), from Uber, has first-class support for Keras models
- Keras models [can be turned into TensorFlow Estimators](https://www.tensorflow.org/versions/master/api_docs/python/tf/keras/estimator/model_to_estimator) and trained on [clusters of GPUs on Google Cloud](https://cloud.google.com/solutions/running-distributed-tensorflow-on-compute-engine)
- Keras can be run on Spark via [Dist-Keras](https://github.com/cerndb/dist-keras) (from CERN) and [Elephas](https://github.com/maxpumperla/elephas)

---

## Keras development is backed by key companies in the deep learning ecosystem

Keras development is backed primarily by Google, and the Keras API comes packaged in TensorFlow as `tf.keras`. Additionally, Microsoft maintains the CNTK Keras backend. Amazon AWS is developing MXNet support. Other contributing companies include NVIDIA, Uber, and Apple (with CoreML).

<img src='images/google-logo.png' style='width:200px; margin-right:15px;'/>
<img src='images/microsoft-logo.png' style='width:200px; margin-right:15px;'/>
<img src='images/nvidia-logo.png' style='width:200px; margin-right:15px;'/>
<img src='images/aws-logo.png' style='width:110px; margin-right:15px;'/>

