Using Tensorflow as a back end

As of 2017-09-12, Nimble has experimental support for Tensorflow as a back-end.

Overview

Tensorflow is compute framework for medium sized tensor operations. Tensorflow is usually used for deep neural networks, but Nimble uses it only to accelerate tensor computations. In this sense, Nimble uses Tensorflow roughly as a GPU-compatible replacement for Eigen.

Installing Tensorflow

To use the Tensorflow backend for Nimble, you'll need to install RStudio's R wrapper for Tensorflow, and then use the R wrapper to install tensorflow

install.packages('tensorflow')    # Installs RStudio's R wrapper library.
library('tensorflow')
tensorflow::install_tensorflow()  # Installs Google's Tensorflow library.

The above lines install the latest stable CPU-only version of Tensorflow in a python virtualenv named r-tensorflow. You can replace this version with a GPU-compatible version by manually installing the Python package:

$ workon r-tensorflow  # You may need to install virtualenv before doing this.
$ pip install --ignore-installed tensorflow-gpu

To run on GPUs, you'll also need to install various CUDA libraries on your system. See the Installing Tensorflow docs for details.

Linux has first-class support; OS X and Windows may have more difficulty.

Using Tensorflow for Nimble Functions

Nimble can use Tensorflow to accelerate nimbleFunctions. Each nimbleFunction can currently use either Tensorflow or Eigen for vectorizable math, and this decision is made on a per-function basis. To enable Tensorflow, set the experimentalUseTensorflow option when compiling that function:

nimbleOptions(experimentalUseTensorflow = TRUE)
tf_fun <- compileNimble(fun)

It is generally safer to set this global variable temporarily using withNimbleOptions:

tf_fun <- withNimbleOptions(list(experimentalUseTensorflow = TRUE),
                            compileNimble(fun))