I have installed MLFLOW for R in my ubuntu environment. When I try to execute any command for mlflow I am getting below error
mlflow_ui()
Error in rethrow_call(c_processx_exec, command, c(command, args), stdin, :
cannot start processx process (system error 2, No such file or directory) #unix/processx.c:573
You can install MLFlow separately using pip and then specify the variables in your ~/.Renviron. If you don't have a .Renviron file, create one. (Checkout out help("Startup") in R for more info).
The environment variables you need to set in the .Renviron file are:
MLFLOW_PYTHON_BIN and MLFLOW_BIN. These need to set to the location of your python executable and mlflow executable.
Simply get those by running which python and which mlflow after installing mlflow with pip.
Make sure to restart Rstudio (perhaps try removing mlflow and re-installing the package).
Then there will be no need to run mlflow::install_mlflow().
#digvijay have you installed conda & invoked the R install_mlflow function on your machine? It's necessary to do so before invoking the R APIs - install_mlflow creates a conda environment for installing the MLflow CLI, which some of the R APIs (e.g. mlflow_ui) depend on.
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I am try to install tensorflow within R and subsequently a package called "cellassign". I have installed tensorflow, reticulate, and keras with the following command and they all seem to be installed without any hiccups. I am also able to load the packages after installing.
devtools::install_github("rstudio/reticulate")
devtools::install_github("rstudio/tensorflow")
devtools::install_github("rstudio/keras")
However, I keep encountering the following issue when trying to use any command within tensorflow or keras package.
tensorflow::install_tensorflow()
Error: 'activate' is not a conda command.
Did you mean "source activate /home/users/ntu/ling0086/.conda/envs/r_env" ?
+ . /app/anaconda/3/bin/activate
+ conda activate '/home/users/ntu/ling0086/.conda/envs/r_env'
Also ran into another error if I tried using the following
> tensorflow::tf_config()
Python environments searched for 'tensorflow' package:
/home/users/ntu/ling0086/.conda/envs/r_env/bin/python3.10
/usr/bin/python
Python exception encountered:
ModuleNotFoundError: No module named 'six'
Any idea why this might be so? I am not familiar with linux-environment variables and I do not have sudo privilege. I was told that my "R" is loaded in a virtual environment and this might affect the path finding to the right packages. But I am unsure as to how I should rectify the above error. Any help would be deeply appreciated.
Not sure if this is of any help, but how I would normally run R entails:
module load anaconda/3
source activate r_env
R
I have a basic script in R 4.1 and R Studio which installs TensorFlow 2.
When I run the library installation for Tensorflow, everything works fine with no issues. My issue is that when I call the library and run the install_tensorflow command, the console is asking me to answer a "yes/no" question.
Would you like to install Miniconda? [Y/n]:
My question: How do I answer this with a Y without having to type directly into the console? I want to just run an R Script to install and provision my conda environment without manual intervention. Programmatic User Input.
Here is my full R code:
install.packages("tensorflow")
library(tensorflow)
install_tensorflow(method="auto", conda="auto", version="2.0.0", envname ="tf_test")
I used to work in R 3.4.0 version. Hovewer, this version doesn't support such packages as keras and tensorflow.
I was adviced to upgrade my R version to the newest one.
I downloaded the most recent R version 4.0.2 from the official site, then ran the following code:
install.packages("keras")
library(keras)
install_keras()
And got the following error:
Error in install_keras() :
You should call install_keras() only in a fresh R session that has not yet initialized Keras and TensorFlow (this is to avoid DLL in use errors during installation)
After this, when I tried to quit R session by q() , I faced the following error:
Error: option error has NULL value
Error: no more error handlers available (recursive errors?); invoking 'abort' restart
Error: option error has NULL value
I've never faced such an error before. When I used old R version, I typed q() and then had to choose between y and n. No errors appeared.
I'm asking you to help to to solve this problem.
You need to create a new environment and then you can install R 4.+ in Anaconda. Follow these steps.
conda create --name r4-base
After activating r4-base run these commands
conda activate r4-base
conda install -c conda-forge r-base
conda install -c conda-forge/label/gcc7 r-base
Finally, you will notice r-basa version 4 will be installed.
Thereafter, you can install any supported packages. But with this only, you won't have the ability to use it in the Jupyter notebook. You need to install install.packages('IRkernel') and Jupyter notebook as well if you want to use it. Otherwise you are good to go with R-Studio.
For Jupyter Installation and RKernel.
conda install jupyter
Then open the R console. Write in R console
install.packages('IRkernel')
IRkernel::installspec()
Congrats! You can use Notebook for Python and R.
Find the location of R.exe on your computer. In my computer, this executable is at
C:\Program Files\R\R-3.4.3\bin
Open another Anaconda Prompt as Administrator and change directories to wherever R.exe is on your computer with cd file path. On my computer, it’s cd C:\Program Files\R\R-3.4.3\bin, but it might be different for you.
Then run R from within Anaconda Prompt in Admin mode with R.exe
You’ll notice that you’re in an R session. From here, run the following three commands into the terminal.
install.packages("devtools")
devtools::install_github("IRkernel/IRkernel")
IRkernel::installspec()
In order, they (1) install the devtools package which gets you the install_github() function, (2) install the IR Kernel from GitHub, and (3) tell Jupyter where to find the IR Kernel.
Open Jupyter notebook and enjoy your new R kernel!
Get more information here
#Rheatey Bash works perfectly. but i was facing python.exe this program cant start because api-ms-win-core-path-l1-1-0.dll python system error. this is a problem running on windows 7 but i resolved this issue by installing the kernel following https://richpauloo.github.io/2018-05-16-Installing-the-R-kernel-in-Jupyter-Lab/ and it works fine
I have been unable to get tensorflow to work in my R-studio. Every time I try to use install_tensorflow() R-studio throws the below error
Error in conda_python(envpath, conda = miniconda) :
no conda environment exists at path 'C:/Users/yaswanth/AppData/Local/r-miniconda/envs/r-reticulate'
Below are the steps I followed
-I have created an environment TensorFlowEnvironment in my anaconda.
-I have installed Tensorflow in this environment
-Launched R studio from this environment
-I ran the following two lines of code
install.packages("tensorflow")
library(tensorflow)
-But when I third line of code
install_tensorflow()
I get the error that no condo environment named as r-reticulate exist. Should I create a new environment named as r-reticulate? I am a tensorflow beginner so any help would be appreciated.
Try this: In Anaconda Navigator, in the Environments pane, create a new environment (click on create+ button) and name it "rminiconda". Include both Python and R (checkboxes in the popup window. Then in RStudio, tools/Options/Python, choose the new environment for Python.
I had Tensorflow installed with Anaconda. Now I want use it in R and I need to reinstall Tensorflow, because the note here
NOTE: You should NOT install TensorFlow with Anaconda as there are
issues with the way Anaconda builds the python shared library that
prevent dynamic linking from R.
I already tried to uninstall from Anaconda and install with pip but its came to the same place in anaconda directory. Tesorflow is working from terminal but in R shows Error: Command failed (1)
Anybody can help me to how I can solve the problem? Should I uninstall anaconda and install Tensorflow using pip?
You have several options on what to do. Probably the cleanest one is to install a system-wide python (if not installed yet) and then create a virtual environment. This basically takes your system python binaries and moves them to its own compartment where everythign is isolated from the rest, incl. anaconda. Once you are inside an activated virtual environment you can install all the necessary Python appendages for TensorFlow. Once that is done, make sure you set up a correct environmental PATH for TensorFlow from where R can reach it:
Sys.setenv(TENSORFLOW_PYTHON="/path/to/virtualenv/python/binary")
devtools::install_github("rstudio/tensorflow")
Example of the path to where you installed the virtual environment project would be, I think, something like ~/minion/medvedi/venv_medvedi/bin/python.
This is no longer an issue, the documentation was updated too.
See here:
https://github.com/rstudio/tensorflow/commit/4e1e11d6ba2fe7efe1a03356f96172dbf8db365e
With the help of Keras, we can install the TensorFlow package in R.
install_keras()
library(keras)
devtools::install_github("rstudio/keras")
install_tensorflow(package_url = "https://pypi.python.org/packages/b8/d6/af3d52dd52150ec4a6ceb7788bfeb2f62ecb6aa2d1172211c4db39b349a2/tensorflow-1.3.0rc0-cp27-cp27mu-manylinux1_x86_64.whl#md5=1cf77a2360ae2e38dd3578618eacc03b")
library(tensorflow)
Keras is a high-level neural network API for deep learning from TensorFlow Google.
my suggestion is to install anaconda and create an environment called "r-reticulate".
you can do it using anaconda navigator or
reticulate::conda_create(envname = "r-reticulate")
then to check that env detected by reticulate, use reticulate::conda_python().it must return directory of python.exe for your env.
after that you can install tensorflow by install_tensorflow(). [not working in my case]
so I install the tesnorflow from CMD.
follow these steps:
open the cmd :]
activate the r-reticulate env using conda activate r-reticulate (you may need your directory to conda directory if you did not add conda to your PATH)
use : conda install -c anaconda tensorflow
now in R, you can use TensorFlow.
for installing Keras, you can use pip install Keras. [i tried install_keras() function after the installation of tensorflow, but it ruined my TensorFlow installation also]
Eventually I found the best and fast method to do it in R:
devtools::install_github("rstudio/keras")
library(keras)
install_keras(method = "conda")
install_keras(tensorflow = "gpu")
tensorflow::install_tensorflow()