TensorFlow, a general purpose numerical computing library, was nominally developed for python and has been proving support for approximately 2 years now. This is one of the reasons why Python has always been preferred over R.
Rstudio (a free and open-source integrated development environment ) made R Interface with TensorFlow plausible. Rstudio formally announced their work on creating R interfaces to TensorFlow at rstudio::conf on Saturday. Here is JJ Allaire, the CEO of Rstudio, addressing the conference.
Interfacing R and TensorFlow has a suite of packages that provides high-level interfaces to deep learning models (Keras) and standard regression and classification models (Estimators). Here we have some interfaces to TensorFlow:
- Keras, a language for building neural networks as connections between general purpose layers. R interface to Keras focuses on enabling fast experimentation. You can have a look on the documentation for R Interface to Keras.
- tfestimators package, is an R interface to TensorFlow Estimators, with the mian aim to provide a flexible framework and implementation to different models. For a detailed information, read R Interface to TensorFlow Estimator.
- tensorflow is a low-level interface to the TensorFlow computational graph, providing access to the complete TensorFlow API from within R. Read this article on R Interface to Core TensorFlow API.
- tfdatasets package provides access to the Dataset API, including high-level convenience functions for easy integration. Read more on R interface to TensorFlow dataset API.
Considering the fact that not all users will have complete access to high-end NVIDIA GPU, using GPUs in the cloud has been made possible. Here are a few methods for the same :
- For Google’s hosted machine learning engine : cloudml package ,.
- For an Amazon EC2 image preconfigured with NVIDIA CUDA drivers, TensorFlow : RStudio Server with Tensorflow-GPU for AWS
- For Ubuntu 16.04: cloud desktop with a GPU
On the other hand, for a user having required NVIDIA GPU hardware, here are steps to set up GPU in the local workstation.
To make this simpler for the users, Rstudio has provided all the resources on TensorFlow for R website. You can also refer Deep Learning using Keras and TensorFlow in R.
Our take on this:
It has always been a major topic of discussion to choose between R and Python. Python was given the preference as it could be interfaced with TensorFlow and Keras. The creation of R interface with TensorFlow is a good news for all R users.
An avid reader and blogger who loves exploring the endless world of data science and artificial intelligence. Fascinated by the limitless applications of ML and AI; eager to learn and discover the depths of data science.
