Tensorflow setup on RStudio/ R | CentOS
Question:
For the last 5 days, I am trying to make Keras/Tensorflow packages work in R. I am using RStudio for installation and have used conda, miniconda, virtualenv but it crashes each time in the end. Installing a library should not be a nightmare especially when we are talking about R (one of the best statistical languages) and TensorFlow (one of the best deep learning libraries). Can someone share a reliable way to install Keras/Tensorflow on CentOS 7?
Following are the steps I am using to install tensorflow in RStudio.
Since RStudio simply crashes each time I run tensorflow::tf_config() I have no way to check what is going wrong.
devtools::install_github("rstudio/reticulate")
devtools::install_github("rstudio/keras") # This package also installs tensorflow
library(reticulate)
reticulate::install_miniconda()
reticulate::use_miniconda("r-reticulate")
library(tensorflow)
tensorflow::tf_config() **# Crashes at this point**
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tensorflow_2.7.0.9000 keras_2.7.0.9000 reticulate_1.22-9000
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 lattice_0.20-45 png_0.1-7 zeallot_0.1.0
[5] rappdirs_0.3.3 grid_3.6.0 R6_2.5.1 jsonlite_1.7.2
[9] magrittr_2.0.1 tfruns_1.5.0 rlang_0.4.12 whisker_0.4
[13] Matrix_1.3-4 generics_0.1.1 tools_3.6.0 compiler_3.6.0
[17] base64enc_0.1-3
Update 1
The only way RStudio does not crash while installing tensorflow is by executing following steps –
First, I created a new virtual environment using conda
conda create --name py38 python=3.8.0
conda activate py38
conda install tensorflow=2.4
Then from within RStudio, I installed reticulate and activated the virtual environment which I earlier created using conda
devtools::install_github("rstudio/reticulate")
library(reticulate)
reticulate::use_condaenv("/root/.conda/envs/py38", required = TRUE)
reticulate::use_python("/root/.conda/envs/py38/bin/python3.8", required = TRUE)
reticulate::py_available(initialize = TRUE)
ts <- reticulate::import("tensorflow")
As soon as I try to import tensorflow in RStudio, it loads the library /lib64/libstdc++.so.6 instead of /root/.conda/envs/py38/lib/libstdc++.so.6 and I get the following error –
Error in py_module_import(module, convert = convert) :
ImportError: Traceback (most recent call last):
File "/root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
File "/home/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/python/rpytools/loader.py", line 39, in _import_hook
module = _import(
ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so)
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors
for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
Here is what inside /lib64/libstdc++.so.6
> strings /lib64/libstdc++.so.6 | grep GLIBC
GLIBCXX_3.4
GLIBCXX_3.4.1
GLIBCXX_3.4.2
GLIBCXX_3.4.3
GLIBCXX_3.4.4
GLIBCXX_3.4.5
GLIBCXX_3.4.6
GLIBCXX_3.4.7
GLIBCXX_3.4.8
GLIBCXX_3.4.9
GLIBCXX_3.4.10
GLIBCXX_3.4.11
GLIBCXX_3.4.12
GLIBCXX_3.4.13
GLIBCXX_3.4.14
GLIBCXX_3.4.15
GLIBCXX_3.4.16
GLIBCXX_3.4.17
GLIBCXX_3.4.18
GLIBCXX_3.4.19
GLIBC_2.3
GLIBC_2.2.5
GLIBC_2.14
GLIBC_2.4
GLIBC_2.3.2
GLIBCXX_DEBUG_MESSAGE_LENGTH
To resolve the library issue, I added the path of the correct libstdc++.so.6 library having GLIBCXX_3.4.20 in RStudio.
system('export LD_LIBRARY_PATH=/root/.conda/envs/py38/lib/:$LD_LIBRARY_PATH')
and, also
Sys.setenv("LD_LIBRARY_PATH" = "/root/.conda/envs/py38/lib")
But still I get the same error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20'. Somehow RStudio still loads /lib64/libstdc++.so.6 first instead of /root/.conda/envs/py38/lib/libstdc++.so.6
Instead of RStudio, if I execute the above steps in the R console, then also I get the exact same error.
Update 2:
A solution is posted here
Answers:
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
Update on 29 July, 2022
After months of solving this problem, I feel so stupid to have wasted time coding R on CentOS. The most popular and stable OS to code R is Ubuntu. By default, CentOS supports only the 3.6 version of R while the most stable current version of R is 4.2. With the default 3.6 version of R on CentOS, most of the libraries are outdated and they conflict with other libraries which are updated for R 4.2+. From my experience, you are going to avoid a lot of misery and frustration if you start coding R on Ubuntu. I am not sponsoring Ubuntu, the above statement is just from my experience and others might have different experiences.
Original Answer
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")
For the last 5 days, I am trying to make Keras/Tensorflow packages work in R. I am using RStudio for installation and have used conda, miniconda, virtualenv but it crashes each time in the end. Installing a library should not be a nightmare especially when we are talking about R (one of the best statistical languages) and TensorFlow (one of the best deep learning libraries). Can someone share a reliable way to install Keras/Tensorflow on CentOS 7?
Following are the steps I am using to install tensorflow in RStudio.
Since RStudio simply crashes each time I run tensorflow::tf_config() I have no way to check what is going wrong.
devtools::install_github("rstudio/reticulate")
devtools::install_github("rstudio/keras") # This package also installs tensorflow
library(reticulate)
reticulate::install_miniconda()
reticulate::use_miniconda("r-reticulate")
library(tensorflow)
tensorflow::tf_config() **# Crashes at this point**
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tensorflow_2.7.0.9000 keras_2.7.0.9000 reticulate_1.22-9000
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 lattice_0.20-45 png_0.1-7 zeallot_0.1.0
[5] rappdirs_0.3.3 grid_3.6.0 R6_2.5.1 jsonlite_1.7.2
[9] magrittr_2.0.1 tfruns_1.5.0 rlang_0.4.12 whisker_0.4
[13] Matrix_1.3-4 generics_0.1.1 tools_3.6.0 compiler_3.6.0
[17] base64enc_0.1-3
Update 1
The only way RStudio does not crash while installing tensorflow is by executing following steps –
First, I created a new virtual environment using conda
conda create --name py38 python=3.8.0
conda activate py38
conda install tensorflow=2.4
Then from within RStudio, I installed reticulate and activated the virtual environment which I earlier created using conda
devtools::install_github("rstudio/reticulate")
library(reticulate)
reticulate::use_condaenv("/root/.conda/envs/py38", required = TRUE)
reticulate::use_python("/root/.conda/envs/py38/bin/python3.8", required = TRUE)
reticulate::py_available(initialize = TRUE)
ts <- reticulate::import("tensorflow")
As soon as I try to import tensorflow in RStudio, it loads the library /lib64/libstdc++.so.6 instead of /root/.conda/envs/py38/lib/libstdc++.so.6 and I get the following error –
Error in py_module_import(module, convert = convert) :
ImportError: Traceback (most recent call last):
File "/root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
File "/home/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/python/rpytools/loader.py", line 39, in _import_hook
module = _import(
ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so)
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors
for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
Here is what inside /lib64/libstdc++.so.6
> strings /lib64/libstdc++.so.6 | grep GLIBC
GLIBCXX_3.4
GLIBCXX_3.4.1
GLIBCXX_3.4.2
GLIBCXX_3.4.3
GLIBCXX_3.4.4
GLIBCXX_3.4.5
GLIBCXX_3.4.6
GLIBCXX_3.4.7
GLIBCXX_3.4.8
GLIBCXX_3.4.9
GLIBCXX_3.4.10
GLIBCXX_3.4.11
GLIBCXX_3.4.12
GLIBCXX_3.4.13
GLIBCXX_3.4.14
GLIBCXX_3.4.15
GLIBCXX_3.4.16
GLIBCXX_3.4.17
GLIBCXX_3.4.18
GLIBCXX_3.4.19
GLIBC_2.3
GLIBC_2.2.5
GLIBC_2.14
GLIBC_2.4
GLIBC_2.3.2
GLIBCXX_DEBUG_MESSAGE_LENGTH
To resolve the library issue, I added the path of the correct libstdc++.so.6 library having GLIBCXX_3.4.20 in RStudio.
system('export LD_LIBRARY_PATH=/root/.conda/envs/py38/lib/:$LD_LIBRARY_PATH')
and, also
Sys.setenv("LD_LIBRARY_PATH" = "/root/.conda/envs/py38/lib")
But still I get the same error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20'. Somehow RStudio still loads /lib64/libstdc++.so.6 first instead of /root/.conda/envs/py38/lib/libstdc++.so.6
Instead of RStudio, if I execute the above steps in the R console, then also I get the exact same error.
Update 2:
A solution is posted here
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
Update on 29 July, 2022
After months of solving this problem, I feel so stupid to have wasted time coding R on CentOS. The most popular and stable OS to code R is Ubuntu. By default, CentOS supports only the 3.6 version of R while the most stable current version of R is 4.2. With the default 3.6 version of R on CentOS, most of the libraries are outdated and they conflict with other libraries which are updated for R 4.2+. From my experience, you are going to avoid a lot of misery and frustration if you start coding R on Ubuntu. I am not sponsoring Ubuntu, the above statement is just from my experience and others might have different experiences.
Original Answer
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")