Tensorflow setup in R | Error: Failed to load the native TensorFlow runtime

Perhaps my failed attempts will help someone else solve this problem; my approach:

• boot up a clean CentOS 7 vm
• install R and some dependencies

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

• Download and install Anaconda via linux installer script
• Create a new conda env

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

**From within this conda env you can import tensorflow in python without error; now to access tf via R

• install an updated gcc via devtoolset

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

• attempt to use tensorflow in R via the reticulate package

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

• try older versions of tensorflow/keras

devtools::install_github('rstudio/[email protected]')
devtools::install_github('rstudio/[email protected]')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

• Try an updated version of R (v4.0)

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

I guess the issue is with R/CentOS, as you can import and use tensorflow via python normally, but I'm not sure what else to try.

I would also like to say that I had no issues with Ubuntu (which is specifically supported by tensorflow, along with macOS and Windows), and I came across these docs that might be some help: https://wiki.hpcc.msu.edu/display/ITH/Installing+TensorFlow+using+anaconda / https://wiki.hpcc.msu.edu/pages/viewpage.action?pageId=22709999

Took me more than 15 days and I finally solved this problem.

Boot up a clean CentOS 7 VM, install R and dependencies (taken from Jared's answer) -

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

Now, create a conda environment

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

Open a new port (7878 or choose any port number you want) on the server to access RStudio with new conda environment libraries

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

then launch RStudio as follows -

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

You will have your earlier environment intact on default port 8787 and a new environment with Tensorflow and Keras on 7878.

The following code now works fine in RStudio

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")