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.
You can install the main TensorFlow distribution from here:
https://www.tensorflow.org/get_started/os_setup.html#download-and-setup
Some important notes on compatibility:
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TensorFlow for R requires version 0.12 or greater of TensorFlow.
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You should NOT install TensorFlow with Anaconda as there are issues with the way Anaconda builds the python shared library that prevent dynamic linking from R.
If you install TensorFlow within a Virtualenv environment you'll need to be sure to use that same environment when installing the tensorflow R package (see below for details).
If you installed TensorFlow via pip with your system default version of python then you can install the tensorflow R package as follows:
devtools::install_github("rstudio/tensorflow")If you are using a different version of python for TensorFlow, you should set the TENSORFLOW_PYTHON environment variable to the full path of the python binary before installing, for example:
Sys.setenv(TENSORFLOW_PYTHON="/usr/local/bin/python")
devtools::install_github("rstudio/tensorflow")If you only need to customize the version of python used (for example specifing python 3 on an Ubuntu system), you can set the TENSORFLOW_PYTHON_VERSION environment variable before installation:
Sys.setenv(TENSORFLOW_PYTHON_VERSION = 3)
devtools::install_github("rstudio/tensorflow")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)We recommend that users try out TensorFlow's high-level TF.Learn module which requires less use of lower-level TensorFlow APIs. Some basic examples can be found here.
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.