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.
The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs. The tensorflow package provides access to the complete TensorFlow API from within R.
First, install the main TensorFlow distribution from here:
https://www.tensorflow.org/get_started/os_setup.html#download-and-setup
If you install TensorFlow within a virtualenv environment you'll need to be sure to use that same environment when loading the tensorflow R package (see below for details on how to do this).
Next, install the tensorflow R package from GitHub as follows:
devtools::install_github("rstudio/tensorflow")Note that the tensorflow package depends on other packages which include native C/C++ code so it's installation requires R Tools on Windows and Command Line Tools on OS X. If the package installation fails because of inability to compile then install the appropriate tools for your platform based on the links above and try again.
When it is loaded the tensorflow R package scans the system for the version of python where TensorFlow is installed. If automatic detection doesn't work or if you want to exercise more control over which version(s) of python and TensorFlow are used you can specify an explicit TENSORFLOW_PYTHON environment variable to force probing for TensorFlow within a specific version of python, for example:
Sys.setenv(TENSORFLOW_PYTHON="/usr/local/bin/python")
library(tensorflow)You can also specify Python virtualenvs or Conda envs via the use_python functions documented here.
You can verify that your installation is working correctly by running this script:
library(tensorflow)
sess = tf$Session()
hello <- tf$constant('Hello, TensorFlow!')
sess$run(hello)See the package website for additional details on using the TensorFlow API from R: https://rstudio.github.io/tensorflow
See the TensorFlow API reference for details on all of the modules, classes, and functions within the API: https://www.tensorflow.org/api_docs/python/index.html
The tensorflow package provides code completion and inline help for the TensorFlow API when running within the RStudio IDE. In order to take advantage of these features you should also install the current Preview Release of RStudio.