Custom Installation

The install_tensorflow() function is provided as a convenient way to get started, but is not required. If you have an existing installation of TensorFlow or just prefer your own custom installation that’s fine too.

The full instructions for installing TensorFlow on various platforms are here: https://www.tensorflow.org/install/. After installing, please refer to the sections below on locating TensorFlow and meeting additional dependencies to ensure that the tensorflow for R package functions correctly with your installation.

Locating TensorFlow

Once you’ve installed TensorFlow you need to ensure that the tensorflow for R package can find your installation. The package scans the system for various versions of Python, and also scans available virtual environments and conda environments, so in many cases things will just work without additional effort.

If the version of TensorFlow you installed is not found automatically, then you can use the following techniques to ensure that TensorFlow is located.

Specify the RETICULATE_PYTHON environment variable to force probing within a specific Python installation. For example:

library(tensorflow)
Sys.setenv(RETICULATE_PYTHON="/usr/local/bin/python")

You could also add the RETICULATE_PYTHON environment variable to your .RProfile.

Alternatively, call the use_python family of configuration functions:

Function	Description
`use_python()`	Specify the path a specific Python binary.
`use_virtualenv()`	Specify the directory containing a Python virtualenv.
`use_condaenv()`	Specify the name of a conda environment.

For example:

library(tensorflow)
use_python("/usr/local/bin/python")
use_virtualenv("~/myenv")
use_condaenv("myenv")

Note that you can include multiple calls to the use_ functions and all provided locations will be tried in the order they were specified.

You can also use the required argument from the use_* functions. In this case TensorFlow will only be searched at the specified location.

library(tensorflow)
use_virtualenv("my_env", required = TRUE)

Additional Dependencies

There are some components of TensorFlow (e.g. the Keras library) which have dependencies on additional Python packages. The install_tensorflow() function installs these dependencies automatically, however if you do a custom installation you should be sure to install them manually.

You can install the additional dependencies with the following command:

pip install h5py pyyaml requests Pillow scipy

Supported Platforms

Note that binary installations of TensorFlow are provided for Windows, OS X, and Ubuntu 16.04 or higher. It’s possible that binary installations will work on other Linux variants but Ubuntu is the only platform tested and supported.

In particular, if you are running on RedHat or CentOS you will need to install from source then follow the instructions in the [Custom Installation] section to ensure that your installation of TensorFlow can be used with the tensorflow R package.