R with TensorFlow 2.0 on Debian/sid

I recently posted on getting TensorFlow 2.0 with GPU support running on Debian/sid. At that time I didn’t manage to get the tensorflow package for R running properly. It didn’t need much to get it running, though.

The biggest problem I faced was that the R/TensorFlow package recommends using install_tensorflow, which can use either auto, conda, virtualenv, or system (at least according to the linked web page). I didn’t want to set up neither a conda nor virtualenv environment, since TensorFlow was already installed, so I thought system would be correct, but then, I had it already installed. Anyway, the system option is gone and not accepted, but I still got errors. In particular because the code mentioned on the installation page is incorrect for TF2.0!

It turned out to be a simple error on my side – the default is to use the program python which in Debian is still Python2, while I have TF only installed for Python3. The magic incantation to fix that is use_python("/usr/bin/python3") and one is set.

So here is a full list of commands to get R/TensorFlow running on top of an already installed TensorFlow for Python3 (as usual either as root to be installed into /usr/local or as user to have a local installation):

devtools::install_github("rstudio/tensorflow")

And if you want to run some TF program:

library(tensorflow)
use_python("/usr/bin/python3")
tf$math$cumprod(1:5)

This gives lots of output but mentioning that it is running on my GPU.

At least for the (probably very short) time being this looks like a workable system. Now off to convert my TF1.N code to TF2.0.

1 Response

  1. 2020/05/14

    […] have been using my Geforce 1060 extensively for deep learning, both with Python and R. But the always painful play with the closed source drivers and kernel updates, paired with the […]

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