I have created a new Anaconda environnement for Python. I managed to add it has an optional environnement you can choose when you create a new Notebook. Hovewer, I'd like to know how can I change the environnement of an already existing Notebook.
open your .ipynb file on your browser. On top, there is Kernel tab. You can find your environments under Change Kernel part.
you can change the kernel from Kernel option of top menu-bar of jupyter notebook
In addition, for different environment the best practice is to use ipykernel
in your conda environment install ipykernel by following command:
conda install ipykernel
name the kernel using:
python -m ipykernel install --user --name other-env --display-name "Python (other-env)"
to know more you can follow the link:
https://ipython.readthedocs.io/en/stable/install/kernel_install.html
Typically one runs jupyter notebook or jupyter-notebook or ipython notebook in a terminal to
start a Jupyter notebook webserver locally (and open the URL in the browser). When using conda
and conda environments, what is the best way to run a Jupyter notebook which allows to
import Python modules installed in the conda environment?
As it seems, this is not quite straight forward and many
users have similar troubles.
Most common error message seems to be: after installing a package XYZ in a conda environment
my-env one can run import XYZ in a python console started in my-env, but running the same
code in the Jupyter notebook will lead to an ImportError.
This question has been asked many times, but there is no good place to answer it, most Q&A's and
Github tickets are quite messy so let's start a new Q&A here.
Disclaimer: ATM tested only in Ubuntu and Windows (see comments to this answer).
Jupyter runs the user's code in a separate process called kernel. The kernel can be a different Python installation (in a different conda environment or virtualenv or Python 2 instead of Python 3) or even an interpreter for a different language (e.g. Julia or R). Kernels are configured by specifying the interpreter and a name and some other parameters (see Jupyter documentation) and configuration can be stored system-wide, for the active environment (or virtualenv) or per user. If nb_conda_kernels is used, additional to statically configured kernels, a separate kernel for each conda environment with ipykernel installed will be available in Jupyter notebooks.
In short, there are three options how to use a conda environment and Jupyter:
Option 1: Run Jupyter server and kernel inside the conda environment
Do something like:
conda create -n my-conda-env # creates new virtual env
conda activate my-conda-env # activate environment in terminal
conda install jupyter # install jupyter + notebook
jupyter notebook # start server + kernel inside my-conda-env
Jupyter will be completely installed in the conda environment. Different versions of Jupyter can be used
for different conda environments, but this option might be a bit of overkill. It is enough to
include the kernel in the environment, which is the component wrapping Python which runs the code.
The rest of Jupyter notebook can be considered as editor or viewer and it is not necessary to
install this separately for every environment and include it in every env.yml file. Therefore one
of the next two options might be preferable, but this one is the simplest one and definitely fine.
Option 2: Create special kernel for the conda environment
Do something like:
conda create -n my-conda-env # creates new virtual env
conda activate my-conda-env # activate environment in terminal
conda install ipykernel # install Python kernel in new conda env
ipython kernel install --user --name=my-conda-env-kernel # configure Jupyter to use Python kernel
Then run jupyter from the system installation or a different conda environment:
conda deactivate # this step can be omitted by using a different terminal window than before
conda install jupyter # optional, might be installed already in system e.g. by 'apt install jupyter' on debian-based systems
jupyter notebook # run jupyter from system
Name of the kernel and the conda environment are independent from each other, but it might make sense to use a similar name.
Only the Python kernel will be run inside the conda environment, Jupyter from system or a different conda environment will be used - it is not installed in the conda environment. By calling ipython kernel install the jupyter is configured to use the conda environment as kernel, see Jupyter documentation and IPython documentation for more information. In most Linux installations this configuration is a *.json file in ~/.local/share/jupyter/kernels/my-conda-env-kernel/kernel.json:
{
"argv": [
"/opt/miniconda3/envs/my-conda-env/bin/python",
"-m",
"ipykernel_launcher",
"-f",
"{connection_file}"
],
"display_name": "my-conda-env-kernel",
"language": "python"
}
Option 3: Use nb_conda_kernels to use a kernel in the conda environment
When the package nb_conda_kernels is installed, a separate kernel is available automatically for each
conda environment containing the conda package ipykernel or a different kernel (R, Julia, ...).
conda activate my-conda-env # this is the environment for your project and code
conda install ipykernel
conda deactivate
conda activate base # could be also some other environment
conda install nb_conda_kernels
jupyter notebook
You should be able to choose the Kernel Python [conda env:my-conda-env]. Note that nb_conda_kernels seems to be available only via conda and not via pip or other package managers like apt.
Troubleshooting
Using Linux/Mac the command which on the command line will tell you which jupyter is used, if you
are using option 1 (running Jupyter from inside the conda environment), it should be an executable
from your conda environment:
$ which jupyter
/opt/miniconda3/envs/my-conda-env/bin/jupyter
$ which jupyter-notebook # this might be different than 'which jupyter'! (see below)
/opt/miniconda3/envs/my-conda-env/bin/jupyter-notebook
Inside the notebook you should see that Python uses Python paths from the conda environment:
[1] !which python
/opt/miniconda3/envs/my-conda-env/bin/python
[2] import sys; sys.executable
'/opt/miniconda3/envs/my-conda-env/bin/python'
['/home/my_user',
'/opt/miniconda3/envs/my-conda-env/lib/python37.zip',
'/opt/miniconda3/envs/my-conda-env/lib/python3.7',
'/opt/miniconda3/envs/my-conda-env/lib/python3.7/lib-dynload',
'',
'/opt/miniconda3/envs/my-conda-env/lib/python3.7/site-packages',
'/opt/miniconda3/envs/my-conda-env/lib/python3.7/site-packages/IPython/extensions',
'/home/my_user/.ipython']
Jupyter provides the command jupyter-troubleshoot or in a Jupyter notebook:
!jupyter-troubleshoot
This will print a lot of helpful information about including the outputs mentioned above as well as installed libraries and others. When
asking for help regarding Jupyter installations questions, it might be good idea to provide this information in bug reports or questions.
To list all configured Jupyter kernels run:
jupyter kernelspec list
Common errors and traps
Jupyter notebook not installed in conda environment
Note: symptoms are not unique to the issue described here.
Symptoms: ImportError in Jupyter notebooks for modules installed in the conda environment (but
not installed system wide), but no error when importing in a Python terminal
Explaination: You tried to run jupyter notebook from inside your conda environment
(option 1, see above), there is no configuration for a kernel for this conda environment (this
would be option 2) and nb_conda_kernels is not installed (option 3), but jupyter notebook is not (fully)
installed in the conda environment, even if which jupyter might make you believe it was.
In GNU/Linux you can type which jupyter to check which executable of Jupyter is run.
This means that system's Jupyter is used, probably because Jupyter is not installed:
(my-conda-env) $ which jupyter-notebook
/usr/bin/jupyter
If the path points to a file in your conda environment, Jupyter is run from inside Jupyter:
(my-conda-env) $ which jupyter-notebook
/opt/miniconda3/envs/my-conda-env/bin/jupyter-notebook
Note that when the conda package ipykernel is installed, an executable jupyter is shipped, but
no executable jupyter-notebook. This means that which jupyter will return a path to the conda
environment but jupyter notebook will start system's jupyter-nootebook (see also here):
$ conda create -n my-conda-env
$ conda activate my-conda-env
$ conda install ipykernel
$ which jupyter # this looks good, but is misleading!
/opt/miniconda3/envs/my-conda-env/bin/jupyter
$ which jupyter-notebook # jupyter simply runs jupyter-notebook from system...
/usr/bin/jupyter-notebook
This happens because jupyter notebook searches for jupyter-notebook, finds
/usr/bin/jupyter-notebook and
calls it
starting a new Python process. The shebang in /usr/bin/jupyter-notebook is #!/usr/bin/python3
and not a dynamic
#!/usr/bin/env python.
Therefore Python manages to break out of the conda environment. I guess jupyter could call
python /usr/bin/jupyter-notebook instead to overrule the shebang, but mixing
system's bin files and the environment's python path can't work well anyway.
Solution: Install jupyter notebook inside the conda environment:
conda activate my-conda-env
conda install jupyter
jupyter notebook
Wrong kernel configuration: Kernel is configured to use system Python
Note: symptoms are not unique to the issue described here.
Symptoms: ImportError in Jupyter notebooks for modules installed in the conda environment (but
not installed system wide), but no error when importing in a Python terminal
Explanation: Typically the system provides a kernel called python3 (display name "Python 3")
configured to use /usr/bin/python3, see e.g. /usr/share/jupyter/kernels/python3/kernel.json.
This is usually overridden by a kernel in the conda environment, which points to the environments
python binary /opt/miniconda3/envs/my-conda-env/bin/python. Both are generated by the package
ipykernel (see here
and here).
A user kernel specification in ~/.local/share/jupyter/kernels/python3/kernel.json might override
the system-wide and environment kernel. If the environment kernel is missing or the user kernel
points to a python installation outside the environment option 1 (installation of jupyter in the
environment) will fail.
For occurrences and discussions of this problem and variants see here,
here,
here
and also here,
here and
here.
Solution: Use jupyter kernelspec list to list the location active kernel locations.
$ conda activate my-conda-env
$ jupyter kernelspec list
Available kernels:
python3 /opt/miniconda3/envs/my-conda-env/share/jupyter/kernels/python3
If the kernel in the environment is missing, you can try creating it manually using
ipython kernel install --sys-prefix in the activated environment, but it is probably better to
check your installation, because conda install ipykernel should have created the environment
(maybe try re-crate the environment and re-install all packages?).
If a user kernel specification is blocking the environment kernel specification, you can either
remove it or use a relative python path which will use $PATH to figure out which python to use.
So something like this, should be totally fine:
$ cat ~/.local/share/jupyter/kernels/python3/kernel.json
{
"argv": [
"python",
"-m",
"ipykernel_launcher",
"-f",
"{connection_file}"
],
"display_name": "Python 3",
"language": "python"
}
Correct conda environment not activated
Symptoms: ImportError for modules installed in the conda environment (but not installed system
wide) in Jupyter notebooks and Python terminals
Explanation: Each terminal has a set of environment variables, which are lost when the terminal
is closed. In order to use a conda environment certain environment variables need to be set, which
is done by activating it using conda activate my-conda-env. If you attempted to run Jupyter
notebook from inside the conda environment (option 1), but did not activate the conda environment
before running it, it might run the system's jupyter.
Solution: Activate conda environment before running Jupyter.
conda activate my-conda-env
jupyter notebook
Broken kernel configuration
Symptoms: Strange things happening. Maybe similar symptoms as above, e.g. ImportError
Explanation: If you attempted to use option 2, i.e. running Jupyter from system and the Jupyter
kernel inside the conda environment by using an explicit configuration for the kernel, but it does
not behave as you expect, the configuration might be corrupted in some way.
Solution: Check configuration in ~/.local/share/jupyter/kernels/my-kernel-name/kernel.json
and fix mistakes manually or remove the entire directory and re-create it using the command
provided above for option 2. If you can't find the kernel configuration there run
jupyter kernelspec list.
Python 2 vs 3
Symptoms: ImportError due to wrong Python version of the Jupyter kernel or other problems
with Python 2/3
Explanation: The kernel configuration can have all sorts of confusing and misleading effects.
For example the default Python 3 kernel configuration will allow me to launch a Jupyter notebook
running on Python 2:
conda create -n my-conda-env
conda activate my-conda-env
conda install python=2
conda install jupyter
jupyter notebook
The default Python 3 kernel:
$ cat ~/.local/share/jupyter/kernels/python3/kernel.json
{
"argv": [
"python",
"-m",
"ipykernel_launcher",
"-f",
"{connection_file}"
],
"display_name": "Python 3",
"language": "python"
}
After creating a new Jupyter Notebook with the Python 3 kernel, Python 2 from the conda
environment will be used even if "Python 3" is displayed by Jupyter.
Solution: Don't use Python 2 ;-)
The following command can also be used as a one liner to create your Conda environment running the latest version of Python and the latest version of Jupyter Notebooks,
conda create -n <env-name> python jupyter
If you want to install specific versions of Python or Jupyter, you can do,
conda create -n <env-name> python=<version> jupyter=<version>
For instance,
conda create -n <env-name> python=3.10.4 jupyter=1.0.0
If there are other packages that you want to use in this environment with your notebooks you can do the following,
conda create -n <env-name> python jupyter <another-package> <another-package> ...
For instance,
conda create -n <env-name> python jupyter scikit-learn
Note that similar to before, these commands will install the latest versions of Python and the relevant packages. If you want specific versions, you can use the =<version> syntax.
Also, you can still install any of the packages that you need using either pip install or conda install once the environment has been created.
After you have created your environment (using any of the methods given above), you can simply run the following commands to activate your environment and run Jupyter Notebooks,
conda activate <env-name>
jupyter notebook
Following worked for me :
Activate the environment that you want to use : conda activate
<env_name>
pip install ipykernel (if you don't already have it)
python -m ipykernel install --user --name=<env_name>
I'm trying to write some R code in Jupyter notebooks (I use python too and would like to be able to use the same app for everything), but I'm having some problems...
I'm on a Mac (Yosemite). I installed Anaconda 3, updated Jupyter, then installed RStudio (which includes R essentials). r-irkernel is installed (I assume also through RStudio), version 0.8.11.
When I try to start an R notebook in Jupyter, it starts a kernel and then immediately it dies. The error given is:
Kernel started: 4275a83e-b2b6-40ef-b161-3a7b2ac57c43
Error: .onLoad failed in loadNamespace() for 'pbdZMQ', details:
call: dyn.load(file, DLLpath = DLLpath, ...)
error: unable to load shared object '/Applications/anaconda3/lib/R/library/pbdZMQ/libs/pbdZMQ.so':
dlopen(/Applications/anaconda3/lib/R/library/pbdZMQ/libs/pbdZMQ.so, 6): Library not loaded: #rpath/libzmq.5.dylib
Referenced from: /Applications/anaconda3/lib/R/library/pbdZMQ/libs/pbdZMQ.so
Reason: image not found
Execution halted
What's the best way forwards, please? What am I missing?
Thanks! :)
Alex Mikhalev's answer partially worked for me. However, it also removed notebook and r-essentials.
Their answer was:
conda remove zeromq
conda install zeromq
But, I also needed to install the following as well:
conda install notebook
conda install r-essentials
Try
conda remove zeromq
conda install zeromq
worked for me.
Alas! Other answers have addressed the Anaconda methods so I address the title Jupyter notebooks for R? more generally. Anaconda is nice but always little bit behind. There is also other option to run your Jupyter R notebooks and that is Docker.
Jupyter Docker Stacks
The Jupyter Docker Stacks contains Jupyter R Notebook here available in Github and Docker hub.
You could try it with
$ docker run --rm -it -p 6780:8888 -v "$PWD":/home/jovyan/ jupyter/r-notebook
as in OS X or
$ docker run --rm -it -p 6780:8888 -v "$PWD":/tmp jupyter/r-notebook
as in some other distros or as path required to be set up by your os.
You may find the following threads useful
Show volume files in the GUI of Docker Jupyter notebook
Kernel Error in R Jupyter Notebook due to Anaconda?
I'm trying to get a basic ipyparallel environment working using mpi4py as described in the ipyparallel documentation. After starting the ipcluster, I load ipython and try to create a client but it has no IDs and accessing the directview returns a NoEnginesRegistered exception.
Steps I take to get to this point:
Create a new environment: conda create --name=ipyparallel and source activate ipyparallel
Install ipyparallel and mpi4py: conda install ipyparallel mpi4py
Create a new ipython profile: ipython profile create --parallel --profile=mpi
Edit ~/.ipython/profile_mpi/ipcluster_config.py and add c.IPClusterEngines.engine_launcher_class = 'MPIEngineSetLauncher'
Launch cluster with ipcluster start --profile=mpi
Then I launch ipython and run the following:
import ipyparallel as ipp
c = ipp.client(profile="mpi")
c[:] # <-- NoEnginesRegistered exception
Step 5 reports that "Engines appear to have started successfully" and I can see that a process named "mpiexec" is running. Strangely, I tried these same steps on another machine with the same OS and there it worked with no problems. What am I missing?
I solved the problem for anyone coming here with a similar problem. During installation I had added the notebook extension to jupyter's global config. Not sure why that caused this problem but it's fixed now. Outside of a conda environment, I ran:
sudo pip install ipyparallel
sudo jupyter nbextension disable --py ipyparallel
sudo jupyter nbextension uninstall --py ipyparallel
sudo pip uninstall ipyparallel
and then inside the conda environment I can connect to the ipyparallel engines.
I am new to jupyter/ipython. I usually start the notebook on a remote machine and create a ssh tunnel. Details of how the tunnel is set up can be found here: http://www.hydro.washington.edu/~jhamman/hydro-logic/blog/2013/10/04/pybook-remote/
However, many times when I start a notebook, it reports back with 0 active kernels (not all the times). Following is the screenshot of that:
-bash-4.1$ jupyter notebook --no-browser --port=7777
[NotebookApp] Serving notebooks from local directory: /x/y/z
[NotebookApp] 0 active kernels
[NotebookApp] The Jupyter Notebook is running at: http://localhost:7777/
[NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
I obviously have python.
python --version
Python 2.7.11 :: Anaconda 2.0.1 (64-bit)
Can someone explain to me this erratic behavior? and how it can be fixed?
Any help would be appreciated.
Thank you
Attached is the error I get:
I also use jupyter using a ssh tunnel and was getting 0 active kernels message and I could not run any notebook. My error snapshot is here. The ipython kernel was not installed properly in the direct install of jupyter using sudo -H pip install jupyter. I resolved it by updating the ipython kernel using:
sudo apt-get -y install ipython ipython-notebook
sudo -H pip install jupyter
If it doesn't work, try uninstalling jupyter and then install packages in sequence as explained in this link.