I am using vscode with the Python extension and Jupyter extension. For the Python extension, I have several interpreters available, installed by anaconda directly or different virtual environments I have created using conda. Thus, with the Python extension I can run code using different Python versions and packages. However, only one kernel is available for running the jupyter notebooks within vscode:
C:\ProgramData\Anaconda3\python.exe -m ipykernel_launcher
The "jupyter mode" in vscode does not recognize packages I have installed in my virtual environments. Is there a way to start the Python kernel for "jupyter mode" from one of my virtual environments? Or is there another way to solve this problem so that I can use a specific virtual environment in "jupyter mode" within vscode?
Thanks for all help.
The solution was to install the ipykernel for the different conda environments following instructions such as these. These kernels are then automatically identified by vscode.
Related
I just downloaded vscode using homebrew and install python#3.11 using homebrew as well. I used pip3 install jupyter as I saw other recommend to fix this issue and still no luck. I have downloaded all the extensions on vscode and I have set the kernal to homebrew version of python and I still recieve this issue. Note that I have used notebooks in the past and have never gotten this issue so I am not really sure what it is this time. I have not installed anaconda.
I have tried uninstalling and installing the extensions but no luck. I have tried to pip3 uninstall jupyter, pip3 uninstall notebook and reinstall them and still no luck. I have tried to change the kernal to the default python3 on the mac and still nothing. My mac is m1 running on Ventura 13.2. I am able to run python code on regular python files just not using jupyter notebook. I followed the instructions how to set it up on vscode website but I got this error. Any help would be appreciated thank you.
I have same issue using VSCode on Mac OS.
VSCode's 'Jupyter' plugin is broken, causing VSCode unable to bind with python interpreter. Downgrading from v2023.1.2000312134 to v2022.11.1003412109 fixed my issue.
I had the same issue, running on m1 mac (Ventura 13.2). If you aren't already, make sure you are utilising a python virtual environment:
# Create a python virtual environment
$ python -m venv venv
# Activate your python virtual environment
$ source venv/bin/activate
Form your VSCode Command Palette (Shift+Command+P), search and then choose
Jupyter: Select Interpreter to Start Jupyter Server
You should then select the python version that is associated to your virtual environment (venv).
In tutorial requirement is install vscode in windows and install Remote Development extension pack. Why not just install in wsl?
I've install vscode to wsl. When I run code in wsl I get message:
To use Visual Studio Code with the Windows Subsystem for Linux, please install Visual Studio Code in Windows and uninstall the Linux version in WSL. You can then use the `code` command in a WSL terminal just as you would in a normal command prompt.
Do you want to continue anyway? [y/N]
To no longer see this prompt, start Visual Studio Code with the environment variable DONT_PROMPT_WSL_INSTALL defined.
Which cons of run vscode in wsl as opposed to run it in Windows?
The WSL extension splits VS Code into a “client-server” architecture, with the client (the user interface) running on your Windows machine and the server (your code, Git, plugins, etc) running "remotely" in your WSL distribution.
When VS Code is started in WSL, no shell startup scripts are run.
The extension runs commands and other extensions directly in WSL so you can edit files located in WSL or the mounted Windows filesystem (for example /mnt/c) without worrying about pathing issues, binary compatibility, or other cross-OS challenges.
(source: MSFT DOCUMENTATION)
This is the architectural choice of Windows and - personally speaking - I feel like it's a choice to avoid conflicts and redundancies.
When running the WSL extension, selecting the 'Extensions' tab will display a list of extensions split between your local machine and your WSL distribution.
Installing a local extension, like a theme, only needs to be installed once.
Some extensions, like the Python extension or anything that handles things like linting or debugging, must be installed separately on each WSL distribution. VS Code will display a warning icon ⚠, along with a green "Install in WSL" button, if you have an extension locally installed that is not installed on your WSL distribution.
I'm using wsl and it runs codes in vscode pretty fine and I have different libraries which I installed through pip and conda in wsl but when I run that code using vscode itself it doesn't recognize the libraries or even pip itself.
I don't have any other environment.
I should add that I installed the packages globally using conda install ... or pip install ... in base environment and I only have base environment and I run my code through code . and I also have python and remote wsl extensions installed in my vscode.
what can be the problem?
I don't have much personal experience with this, but I found some useful information in this Stack Overflow question (even though it doesn't utilize conda), along with https://code.visualstudio.com/docs/remote/wsl-tutorial#_python-development.
I also found this blog post useful, even if it doesn't cover WSL.
In short, make sure you:
Have installed the Python extension (by Microsoft) in VSCode. This is critical for being able to detect and select the Python interpreter. You don't mention having this in place, so I believe this is your likely problem.
You have done this already, but including it for others who might read this later -- Install the Remote - WSL extension (or the Remote Development extension pack) in VSCode.
You are also doing this already -- Start VSCode from inside your WSL distribution. Alternatively, you can start VSCode from Windows and then select the Remote WSL - Reopen Folder in WSL from the Command Palette (also accessible from the "Remote" Status Bar).
In VSCode, open the Command Palette with Shift+Ctrl+P, search for the Python: Select Interpreter command, and you should find your Conda environment in the list.
After selecting this, you should find that your project is using the interpreter and modules that you have installed via conda.
One thing I did to overcome this issue is go to Extensions -> Local (You should have two tabs there, Local and WSL:DISTRO) DISTRO refers to whatever DISTRO you're using, you will see that some of the local extensions are disabled in the current workspace (WSL) and there is a little cloud icon in the WSL:DISTRO tab that says install Local Extensions in WSL:DISTRO once you click that it will let you choose which extensions to install and you should be good to go!
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 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)