This question already has answers here:
Conda environments not showing up in Jupyter Notebook
(24 answers)
Closed 1 year ago.
I'm currently working on an hpc system where I have to need to change the default path for Jupyter that points to the correct path where my Conda environment is based out of.
The problem: When trying to run Jupyter it points to the default and I'm not able to use any of the packages set up in my own Conda environment. (e.g. pandas, sci-kit).
Question: How can I set up my Jupyter path so it points to where my Conda environment, in other words set up environment and then call the correct "/where/ever/jupyter $#" itself
Hope this was clear enough.
You need to install ipykernel:
conda install -c anaconda ipykernel
and then:
python -m ipykernel install --user --name=<YOUR_CONDA_ENV>
after that you'll be able to select your conda environment as the jupyter kernel on the dropdown (upper right corner):
Related
This question already has answers here:
How to connect R conda env to jupyter notebook
(3 answers)
Closed 2 years ago.
I'm trying to be able to code in R within a Jupyter Notebook IDE. I followed the instructions on this site very carefully: website link
But after installation, I open jupyter notebook from newly created R environment in Anaconda navigator >> go to New in Notebook >> there is no option to select R. Only Python 3.
Run these two commands in your terminal-
conda config --add channels r
conda install --yes r-irkernel
I use conda python environment. I start the Jupyter lab following the steps below:
$conda activate <env_name>
$jupyter lab --no-browser --port=8080 &
Now, from a jupyter lab notebook, when I try to import feather (import feather), it fails with Module Not Found message.
From the jupyter lab notebook, if I execute the following, it shows me that feather is present:
! conda list | grep feather
Now, if I shutdown Jupyter Lab in the same VM and start Jupyter Notebook instead, feather gets imported successfully from the notebook.
$conda activate <env_name>
$jupyter-notebook --no-browser --port=8080 &
I see this discussion, but don't see a solution there.
Alternatively, check your path from within your Jupyter notebook vs on the command line. I found that appending the module paths to sys.path solved this exact issue.
All of the ~/anaconda3/envs/[env]/lib/python3.7* paths were missing in my case.
I have found a possible work around to avoid this issue. This is based on this answer.
From the conda environment (e.g. my_env), I can create a new Python 3 kernel (say, python3_custom). Now, this kernel will be associated with all the libraries installed in that conda environment.
$ conda activate my_env
(my_env)$ conda install ipykernel
(my_env)$ ipython kernel install --user --name=python3_custom
(my_env)$ conda deactivate
I come out of my_env or base environment. Then I start JupyterLab from the command prompt:
jupyter lab --no-browser --port=8080 &
Once I open my notebook now, I can select the kernel as python3_custom. Since in the associated conda environment (my_env), feather is already installed, I don't get the error "Module Not Found" any more.
I isolate my data science projects into virtual environments using pipenv. However, running a Jupyter notebok does not access the local environment and uses the default IPyKernel. I've seen that you can register virtual environments from within the environment, but this requires installing the ipykernel package which itself requires Jupyter!
Is there anyway to avoid this and just use a single Jupyter install for all virtual environments?
Generally, you'd install jupyter once and do the following in your virtual environments:
pip install ipykernel
python -m ipykernel install --user
This isn't enough when you're running multiple Python versions.
There's a guide here that tries to address this:
https://medium.com/#henriquebastos/the-definitive-guide-to-setup-my-python-workspace-628d68552e14
It's not 100% failsafe, but it can help you avoid reinstalling jupyter notebook all the time.
I found that there are few problems when reinstall jupyter for each environment separately: i.e. pip install jupyter jupyterlab in new environments.
I had multiple issues (with and without Conda), where Jupyter would install packages to a different python environment when you use !pip install a_package_name within a cell. The shell environment still kept track of the non-environment python, and you can tell this by comparing the outputs of !which python and
import sys
sys.executable
Therefore, when you tried to import the package, it would not be available, because the cells used the environment python/ kernel (as it detected the venv directory).
I found a workaround that I'd appreciate feedback on. I changed pipenv to install virtual environments into the working directory by add to .bashrc/.bash_profile:
export PIPENV_VENV_IN_PROJECT=1
Now when opening a Jupyter notebook, I simply tack on the virtual environment's packages to the Python path:
import sys
sys.path.append('./.venv/lib/python3.7/site-packages/')
Is this a terrible idea?
Here is the big question:
Do i need to explicitly install a library, such as Plotly, in order for my locally hosted Notebook to import it?
Yes you need to have the library installed in your local environment to import it into your Jupyter Notebook.
However, you can check whether a package exists from within Jupyter Notebook and also automatically install it if it isn't already available.
you can run pip as well as shell commands from within a cell of the notebook
The syntax is as follows !pip install plotly Here the ! explicitly forces the kernel to execute the command.
If it's already installed you'll get this message Requirement already satisfied: plotly in /opt/conda/lib/python2.7/site-packages
I've been trying to set up my R using conda (eventually to use with Beaker Notebook) and I want to be able to use RStudio with my conda-installed version of R.
My method of installing R:
conda install -c r r
conda install -c r r-essentials
conda install -c r r-rserve
conda install -c r r-devtools
conda install -c r r-rcurl
conda install -c r r-RJSONIO
conda install -c r r-jpeg
conda install -c r r-png
conda install -c r r-roxygen2
conda install --channel https://conda.anaconda.org/bioconda bioconductor-edger
I ran that version of R (I only installed this version)
> version
_
platform x86_64-apple-darwin11.0.0
arch x86_64
os darwin11.0.0
system x86_64, darwin11.0.0
status
major 3
minor 3.1
year 2016
month 06
day 21
svn rev 70800
language R
version.string R version 3.3.1 (2016-06-21)
nickname Bug in Your Hair
Running R in Jupyter is kind of buggy. For example, when it outputs errors, it outputs to stdout and splits every character in the string with a linebreak. I want to use RStudio but I don't want to install another version of R.
How can I route my conda version of R into RStudio?
Here's my .bash_profile not sure if this will be useful:
$ cat ~/.bash_profile
# added by Anaconda3 4.0.0 installer
export PATH="/Users/jespinoz/anaconda/bin:$PATH"
export RSTUDIO_WHICH_R=/Users/jespinoz/anaconda/bin/R
I've been trying to follow these tutorials but I am lost. I'm really not too familiar with environment variables and such things.
(1) https://support.rstudio.com/hc/en-us/community/posts/207830688-Using-RStudio-with-conda
(2) Launch mac eclipse with environment variables set
when I looked for my R it directed me to:
$ which R
/Users/jespinoz/anaconda/bin/R
but the directions from (1) is using this path which is very confusing:
/Users/jespinoz/anaconda/lib/R/bin/R
I tried doing what this guy did and added this to my .bash_profile but it didn't work. I even made a .bashrc but it still didn't work (I sourced both after I added the lines)
export RSTUDIO_WHICH_R=/Users/jespinoz/anaconda/bin/R
How to tell RStudio to use R version from Anaconda
Unfortunately, anaconda has no tutorial for this in https://docs.continuum.io/anaconda/ide_integration
See https://anaconda.org/r/rstudio:
$ conda install -c r rstudio
Then from command line:
$ rstudio
(It is how I installed it and it works.)
So long as which R shows up a working R interpreter (which it should do if you have installed the r package from conda and activated your environment) then launching rstudio from that same environment should pick it up just fine.
For a test, on ArchLinux, I built and installed: https://aur.archlinux.org/packages/rstudio-desktop-git/
.. then force removed the R interpreter (pacman -Rdd r), then installed r from conda (conda install -c r r) and it worked fine. I then closed my terminal and opened a new one (so that the correct conda environment was not activated and successfully launched RStudio with the following command: RSTUDIO_WHICH_R=/home/ray/r_3_3_1-x64-3.5/bin/R rstudio
I think the crux is to launch RStudio from the right environment? Your ~/.bash_profile and ~/.bashrc are only sourced when you run bash. For environment variables to be set so that the your desktop environment knows about them, on Linux, you should put them in ~/.profile or else in /etc/pam.d (you may need to logout or shutdown after making those changes) and on OS X, you should check out https://apple.stackexchange.com/q/57385
Update: ADD THIS TO ~/.bash_profile !
export RSTUDIO_WHICH_R="/Users/jespinoz/anaconda/bin/R"
launchctl setenv RSTUDIO_WHICH_R $RSTUDIO_WHICH_R
Credits to #Z-Shiyi for the last line https://github.com/conda/conda/issues/3316#issuecomment-241246755
An addition to what #Ray Donnelly said above. Basically, it has to be executed from the correct environment (i.e. run it from the terminal).
You can either:
(A) Put this in your ~/.bash_profile
export RSTUDIO_WHICH_R=/Users/[yourusername]/anaconda/bin/R (if youre using conda but you could put any R path)
(B) then type this in the terminal after it's been sourced (either restart terminal or do source .bash_profile): open -a RStudio
That should work.
or you can do what I did:
(A) open up automator (sorry if you're not on a mac; this will only work on mac)
(B) use a Run Shell Script
(C) then delete cat that's already in there and put in:
export RSTUDIO_WHICH_R=/Users/[yourusername]/anaconda/bin/R
open -a RStudio
(D) Save it as something like run_rstudio.app then just run that and it should work:
Launch RStudio from Activated Conda Environment
At least for Mac OS X, I find that it is sufficient to activate the environment in a shell session, then launch RStudio.
$ conda activate my_r_env
$ /Applications/RStudio.app/Contents/MacOS/RStudio
Once in R, one can verify that values of R.home() and .libPaths() point to the environment-specific locations.
The advantage here is that you aren't fixed to whatever was last set in the environment variables, e.g., via .bash_profile. Instead, one can have many R-based environments and switch between them (or run multiple ones simultaneously) without tampering with global settings.
Suggested Alias for Convenience
Perhaps the only global setting I might recommend is to add an alias for rstudio to your .bash_profile so you don't have to type the full path every time, like
alias rstudio='/Applications/RStudio.app/Contents/MacOS/RStudio &'
which enables one to then do
$ conda activate my_r_env
$ rstudio
$
where the & enables one to continue using the shell, or close it, without affecting the RStudio instance.
Update: The Anaconda Distribution now has packages for RStudio so you should be able to use that and not have to jump through any hoops at all. You can also install it directly the Anaconda Navigator.
Making a soft link works for me:
ln -s /opt/miniconda3/envs/r-4.2/bin/R /usr/bin/R
if is up to any good (now)... conda has the package rstudioapi which brings Rstudio to your local environment, and picks up the local/default r-base installed of your active environment.
you can install it (once your environment is activated) by typing:
conda install -c conda-forge r-rstudioapi
then you just type (inside your environment): rstudio