You need two things to get rTorch working:

  1. Install Python Anaconda. Preferrably, for 64-bits, and above Python 3.6+.

  2. Install R, Rtools and RStudio.

  3. Install rTorch from CRAN or GitHub.

Note. It is not mandatory to have a previously created Python environment with Anaconda, where PyTorch and TorchVision have already been installed. This step is optional. You could also get it installed directly from the R console, in very similar fashion as in R-TensorFlow using the function install_pytorch.

This book is available online via GitHub Pages, or you can also build it from source from its repository.


rTorch is available via CRAN or GitHub.

The rTorch package can be installed from CRAN or Github.

From CRAN:

From GitHub, install rTorch with:

Python Anaconda

Before start running rTorch, install a Python Anaconda environment first.


  1. Create a conda environment from the terminal with conda create -n myenv python=3.7

  2. Activate the new environment with conda activate myenv

  3. Install the PyTorch related packages with:

conda install python=3.6.6 pytorch-cpu torchvision-cpu matplotlib pandas -c pytorch

The last part -c pytorch specifies the conda channel to download the PyTorch packages. Your installation may not work if you don’t indicate the channel.

Now, you can load rTorch in R or RStudio.

Automatic installation

I use the idea from automatic installation in r-tensorflow, to create the function rTorch::install_pytorch(). This function will allow you to install a conda environment complete with all PyTorch requirements.

Note. matplotlib and pandas are not really necessary for rTorch to work, but I was asked if matplotlib or pandas would work with PyTorch. So, I decided to install them for testing and experimentation. They both work.