I've been following https://queirozf.com/entries/jupyter-kernels-how-to-add-change-remove to create a jupyter kernel.
This procedure worked when I was on Ubuntu or WSL1.
However, I'm unable to change kernel on WSL2. Whatever custom kernel I select, the python and pip-packages seem to be pointing to the version where the jupyter notebook is launched and not the virtualenv associated to the kernel.
Does anyone know how to setup custom jupyter kernels on WSL2 ?
The recent update on my computer seemed to have resolved the problem
Related
To date I have been using Jupyter Notebook to run R and sometimes Python code. I have also been using RStudio at times. Recently, while using RStudio, I was prompted to install some package (cannot exactly remember). At any rate, I installed this package. Dont know if only coincidence, but trying to run R in the notebook resulted in kernel not connecting. I found the same issue with Python, the Python kernel is also not connecting anymore.
Executing the below, I get;
(base) C:\WINDOWS\system32>jupyter kernelspec list
Available kernels:
ir C:\Users\Admin\AppData\Roaming\jupyter\kernels\ir
python3 C:\Users\Admin\anaconda3\share\jupyter\kernels\python3
How do I get Jupyter Notebook's kernels to work again.
Executing IRkernel::installspec() in R via Anaconda CMD prompt have resolved the issue. Note for others with this issue, you may be prompted to run install.packages(“rlang”) before.
I have multiple people working on the same AWS EMR cluster to run some Spark jobs. This is being done through Jupyter Notebooks which are created/modified using the Jupyter extension installed on a SSH Target through vscode. The modules are installed on the base conda environment that is included with the /emr/notebook-env/. Some people can see the correct kernel that is associated with the base conda environment in their vscode window when working on notebooks. However, some don't see this kernel as an option. How do I make sure that everyone's vscode lists the appropriate kernel when they are creating new notebooks or modifying existing notebooks?
Another potential reason this could happen is that the Jupyter exentsion of VSCode is not installed.
To add the Jupyter extension for VSCode, search for it by clicking extensions icon in lefthand toolbar, then searching for Jupyter and installing.
The user having the issue had to update their vscode and that fixed the issue
I apologize if it is a trivial question, but I searched and I could not find the answer. My situation is the following:
I have access to a server through ssh tunnelling. The server has Julia installed but not jupyter.
My machine has both, and my code is mostly in jupyter notebooks and I wish to keep it that way.
I wish to be able to run my code on a julia kernel on the server, but editing from a Jupyter interface/ IJulia locally.
I have found several guides on how to launch jupyter from remote, but they do not apply since the server does not have jupyter installed. Similarly, I am aware I could convert my code in a julia script and run it, but I would rather keep the convenience of a notebook for editing.
Is this even possible to do?
Any help is very appreciated!
I am trying to run some notebooks in my virtual environment in the VSCode (remotely connected). I install the venv as usual via python3 -m venv <venv-name>, activate it and install all the needed modules. When I run which ipython I get the one from the venv so I install the kernel via ipython kernel install --name "<name>" --user and it is successfully created in ~/.local/share/jupyter/kernels/ directory and the kernel.json points to the venv python. Then I open the VSCode and select both the Python: Select Interpreter and Jupyter: Select Interpreter to start Jupyter server to point to the virtual environment's python, sth. like .../<venv-name>/bin/python3.
However, when I try to run the cell it wants me to select kernel (I can also do it myself in the upper right corner of the VSCode), but my newly created kernel is not there. There are only two (same) ones from usr/bin/python.
It is really strange since twice in two days my kernel magically appeared for one notebook and worked as desired, but when I opened a new notebook, my kernel was gone again. I tried to remove/reinstall kernels, venvs, VSCode's Python and Jupyter extensions but nothing helped. Any suggestions?
For now, I start the kernel in remote command-line via jupyter notebook --no-browser --ip=<ip> and then insert the connection link to Jupyter Server in the bottom right corner of the VSCode status bar but am wondering if there is an easier way since all the stuff (except VSCode) is on a remote machine?
This way is not easy. You can set up Jupyter Kernel easily.
Firstly, using ssh to connect to the remote server.
Secondly, open Command Palette (⇧⌘P) and enter Python: Select Interpreter, you can directly connecting to remote kernel.
resource: https://code.visualstudio.com/docs/datascience/jupyter-notebooks
I had started with udacity deep learning course and was setting up environments. I think the kernel notebook uses does not use python from conda environment. Following are some of the results of things I have tried.
Started conda environment
source activate tensorflow
With python terminal inside conda environment from linux terminal:
import sys
sys.executable
>>> '/home/username/anaconda2/envs/tensorflow/bin/python'
Also tensorflow gets imported with python shell
With ipython terminal inside conda environment, it shows same executable path. and tensorflow gets imported inside ipython shell.
However with jupyter notebook when I execute a cell in notebook, tensorflow module cannot be found. Also terminal spawned from notebook shows executable path of global python installation which is in anaconda/bin directoty, not of environment I had created from which I started the notebook
'/home/username/anaconda2/bin/python'
However conda environment of shell is still tensorflow
conda info --envs
# conda environments:
#
tensorflow * /home/username/anaconda2/envs/tensorflow
root /home/username/anaconda2
Does that mean kernel is linked to python installation in this location and not in conda env? How to link the same?
There is some more nuance to this question that is good to clarify. Each notebook is bound to a particular kernel. With the latest 4.0 release of Anaconda we (Continuum) have bundled a Conda-environment-aware extension that will try to associate a Notebook with a particular Conda environment. If that cannot be found then the "default" environment (or "root" environment) will be used. In your case you have a Notebook that is, I am guessing, asking for the default (or "root") environment, and so Jupyter starts a kernel in that environment, and not in the environment from which the Jupyter server was started. You can change the associated kernel by going to the Kernel->Change kernel menu and picking your tensorflow environment's kernel, along the lines of this:
Or when you create a new Notebook you can pick at that time which Conda environment's kernel should back the Notebook (note that one Conda environment can have multiple kernels available, e.g. Python and R):
We appreciate that this can be a common cause of confusion, especially when sharing notebooks, since the person who shared it either used the "default" kernel (probably called just "Python"), or they were using a Conda environment with a different name. We are working on ways to make this smoother and less confusing, but if you have suggestions for expected/desired behavior, please let us know (GitHub issue to https://github.com/ContinuumIO/anaconda-issues/issues/new is the best way to do this)