I have opened my .ipynb file with vscode (Python 3.9 Interpreter - installed jupyterlab), it's connected locally, how can I open it through the browser? what is the default port?
Type:
jupyter notebook in vscode terminal (Ctrl + Shift + ~) and it will start Jupyter in browser of your preference.
If jupyter not installed type:
pip install jupyter in elevated powershell or however you prefer to do installs.
If pip not installed:
python -m pip install -U pip
if python not installed I prefer to install with chocolatey and walkthrough is on their site. https://docs.python-guide.org/starting/install3/win/#install3-windows
I do not have a solution, but only hints. You can let Code start a local server via Ctrl + Shift + P, then Jupyter connect , then select default.
Unfortunately, it's not documented how you can access the server. I gave that feedback on the documentation page.
Source of the image: https://code.visualstudio.com/docs/python/jupyter-support#_connect-to-a-remote-jupyter-server
You can list all running notebooks and their tokens by typing jupyter notebook list in the terminal.
It will output something like this:
Currently running servers:
http://localhost:8889/?token=your_token :: /directory
You can click on the link to open it in the browser.
A solution for CORS-related issues:
In my case, the problem was in my CORS configuration. What fixed it for me was launching the notebook from the terminal with the allowed origin specified:
jupyter notebook --no-browser --NotebookApp.allow_origin_pat=https://.*vscode-cdn\.net
If, for some reason, that doesn't work, you could try the following as a last resort (bearing in mind this will allow ANY origin):
jupyter notebook --no-browser --NotebookApp.allow_origin='*'
Source:
https://github.com/microsoft/vscode-jupyter/wiki/Connecting-to-a-remote-Jupyter-server-from-vscode.dev
Related
Whenever I try to run a jupyter notebook via WSL2 , I seem to be unable to connect to a kernel when running a VSCODE. The Vscode is running in Ubuntu, but I cannot connect to the remote jupyter server. I was able to do so for the last couple of months but suddenly, without me recalling channging anything, this process stopped working.
After typing 'jupyter notebook' in the terminal, I geth the following error in red, which (I think?) I received before and the jupyter was working nonetheless. However what's different is the 'forbidden' message. (see picture below)
My usual process was in WSL: conda activate env -> jupyter notebook > open another terminal -> conda activate env -> code . -> (vscode opens up running in wsl) -> copy paste one of the URLS to jupyter server -> viola it's working.
Thank you
It seems simply restarting the computer somehow solved the issue, interesting.
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 write Python code in Visual Studio Code and run the program from a terminal in which I have activated a virtual environment, and it works fine.
However, if I create notebook cells using #%% and run those interactively, the virtual environment is not used. How can I fix this?
It's because there is an extra step needed - you need to explicitly install a Jupyter kernel that points to your new Python virtual environment. You can't simply activate Jupyter-lab or Notebook from the virtual environment. This has tripped me up before, too.
Follow the advice here: Using Jupyter notebooks with a virtual environment
And, in fact, there can be an issue where your kernel still doesn't point to the correct Python binary, in which case you need to change one suggestion in the above advice process:
From: ipython kernel install --user --name=projectname
To: python3 -m ipykernel install --user --name=projectname
(This correction comes from a comment to Jupyter Notebook is loading incorrect Python kernel #2563.)
*and don't forget to restart VSCode
All you need is to edit Vscode settings following these steps:
Open Open User settings using shortcut Ctrl + Shift + P
Type in search space "env"
Under Extentions -> Python , you will find Python: Venv Path
Type the absolute path to your enviroment "path/to/myenv/bin" in linux or "path/to/myenv/Script/"
Restart vsCode
Select the desired kernel using Notebook : Select Notebook kernel using shortcut Ctrl + Shift + P
Read more here: https://techinscribed.com/python-virtual-environment-in-vscode/
For VSCode, your Jupyter kernel is not necessarily using the same python interpreter you're using at the command line.
Use Ctrl + Shift + P to open the Command Palette, and select "Notebook: Select Notebook Kernel"
Then choose the interpreter you're using at the terminal
I find it easy to use pipenv install ipykernel to set up the virtual environment with the Jupyter kernel in one go (comment on rocksteady's answer).
Encounter the same behaviour. Python code works perfectly fine, but Jupyter refuses to pick up the local .venv.
The local venv is in Python: Select Interpreter but not in Jupyter's Select kernel list.
The problem is there're too many venv in the system!
If you encounter the same behaviour,
Press F1, then Jupyter: Filter kernels, uncheck everything, except the local env.
Then F1 -> Developer: Reload Window.
Jupyter will automatically use the default local venv.
for me solved by adding the path of my venv to the settings.json,
now the kernel is detected automatically
"python.pythonPath": "P:\Miniconda3_64bit\venv\Scripts\python.exe",
Make sure you have installed jupyter, notebook, ipykernel libraries in your virtual environment.
Then hit Ctrl + Shift +P , press >Python: Select Interpreter and choose your path of the venv.
After that, hit Ctrl + Shift +P again, run >Notebook: Select Notebook kernel.
If you have already opened the jupyter notebook window , reload it again and you can find your path of the venv in Jupyter's Select kernel list.
On macOS I have .venv/ in the same folder as my .ipynb
. .venv/bin/activate
pip install ipykernel
Then I restart VSCode in the project folder, open the notebook, and in the Select Kernel dropdown I see .venv/bin/python
Selecting that, now it works.
Here is how to do for venv with Jupyter Notebook on VSCode in Windows:
Create a venv and get the path to this venv in Windows. As an example, with Anaconda, I get: C:\Users\rascoussier\Anaconda3\envs\research310.
Now, we need to tell VSCode to use it. In VSCode, go to the Python Extension > Extension Settings. Search for Python: Venv Path. Add the path where the venvs are located. Here we added C:\Users\rascoussier\Anaconda3\envs\research310.
Restart VSCode.
Now launch command pallet(ctrl+shift+P) and run >Notebook: Select Notebook kernel. Normally the venv python should be available and it should then works.
Try a few things:
Make sure you can run the code from a Visual Studio Code terminal using the "ipython" prompt with the same Conda environment.
If it works then sometimes it is a caching issue, so close your file and open a new one.
Now let me show you a scenario. You select the interpreter in Visual Studio Code, and then you write codes below '# %%'. The moment you hit Ctrl + Enter, you are guessing that the IPython kernel that Visual Studio Code is using is not of the interpreters that you have selected. In this case you could write the following code to conform which interpreter is used for IPython kernel.
import sys
print(sys.executable)
This shows the executable path that the IPython kernel is using. If you see that it's not taking the correct interpreter then here's something that worked for me.
Just restart your computer. Then reopen Visual Studio Code and reselect the interpreter and again hit Ctrl + Enter. Now this time Visual Studio Code should take the correct interpreter and its IPython kernel.
See the final output image
If this happens while using WSL server, don't forget to install Python in the WSL as well, because it doesn't come automatically from the local installation to the server.
https://code.visualstudio.com/docs/remote/wsl-tutorial
Another alternative is to specify the folders where the environmental variables should be sought for.
Create your virtual environment using conda create --name ENV_NAME e.g conda create --name pwd
Then, conda activate pwd
It Should print out details like this:
Use that environment location
Edit Vscode settings following these steps:
Open Open User settings using shortcut Ctrl + Shift + P
Type in search space "env"
Under Extentions -> Python , you will find Python: Venv Folders
(See the image below)
Try installing the Anaconda Extension pack.
When I code in Visual Studio Code with this extension in the bottom left corner, I can select the virtual environment I want to execute my code in. Hence installing this package should make the trick.
I am trying to use the hidecode tag as shown here, but for some reason, it's not working on my local Jupyter Notebook server. The code remains visible, and no button on the right of the cell is displayed. I even downloaded the same notebook used in the page above, no dice.
I am running Ubuntu 18.04.2 LTS, Jupyter Notebook Server 5.2.2, Python 3.6.7, and IPython 5.5.0. The command I ran to start the server is sudo jupyter notebook --allow-root.
If you need any more info, please let me know. I appreciate any help I can get in figuring this out.
If you want to hide the code, the output or the prompt of your cells you can install the hide_code extension:
https://pypi.org/project/hide_code/0.2.0/
Stop jupyter
Run these commands
pip install hide_code
jupyter nbextension install --py hide_code
jupyter nbextension enable --py hide_code
jupyter serverextension enable --py hide_code
NOTE: I run the first two commands as root and the last two with the same user used to launch jupyter.
Restart jupyter
I found this extension very useful because I needed to print the notebook to a pdf file and all the tools like nbpublish, nbconvert was failing. In this way I can print the notebook directly from the "print" feature of the browser.
I have jupyter/anaconda/python3.5.
How can I know which conda environment is my jupyter notebook running on?
How can I launch jupyter from a new conda environment?
As mentioned in the comments, conda support for jupyter notebooks is needed to switch kernels. Seems like this support is now available through conda itself (rather than relying on pip).
http://docs.continuum.io/anaconda/user-guide/tasks/use-jupyter-notebook-extensions/
conda install nb_conda
which brings three other handy extensions in addition to Notebook Conda Kernels.
Question 1: Find the current notebook's conda environment
Open the notebook in Jupyter Notebooks and look in the upper right corner of the screen.
It should say, for example, "Python [env_name]" if the language is Python and it's using an environment called env_name.
Question 2: Start Jupyter Notebook from within a different conda environment
Activate a conda environment in your terminal using source activate <environment name> before you run jupyter notebook. This sets the default environment for Jupyter Notebooks. Otherwise, the [Root] environment is the default.
You can also create new environments from within Jupyter Notebook (home screen, Conda tab, and then click the plus sign).
And you can create a notebook in any environment you want. Select the "Files" tab on the home screen and click the "New" dropdown menu, and in that menu select a Python environment from the list.
which environment is jupyter executing:
import sys
print(sys.executable)
create kernel for jupyter notebook
source activate myenv
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"
source activate other-env
python -m ipykernel install --user --name other-env --display-name "Python (other-env)"
http://ipython.readthedocs.io/en/stable/install/kernel_install.html#kernel-install
If the above ans doesn't work then try running conda install ipykernel in new env and then run jupyter notebook from any env, you will be able to see or switch between those kernels.
to show which conda env a notebook is using just type in a cell:
!conda info
if you have grep, a more direct way:
!conda info | grep 'active env'
You can also switch environments in Anaconda Navigator, install Jupiter and run it.
Because none of the answers above worked for me, I write here the solution that finally solved my problem on Ubuntu. My problem was:
I did the following steps:
Activate my environment: conda activate MyEnv
Start jupyter notebook:jupyter notebook
Although MyEnv was active in the terminal and had an asterix when writing conda env list, but jupyter notebook was started with the base environment.
Installing nb_conda and ipykernel didn't solve the problem for me either. Additionally, the conda tab wasn't appearing in jupyter notebook and also clicking on the kernels or going to the menu Kernel->Change Kernel didn't show the kernel MyEnv.
Solution was: install the jupyter_environment_kernel in MyEnv environment:
pip install environment_kernels
After that when starting jupyter notebook, it is started with the right environment. You can also switch between environments without stopping the kernel, by going to the menu Kernel->Change Kernel and selecting the desired kernel.
Question 1: How can I know which conda environment is my jupyter notebook running on?
Launch your Anaconda Prompt and run the command conda env list to list all the available conda environments.
You can clearly see that I've two different conda environments installed on my PC, with my currently active environment being root(Python 2.7), indicated by the asterisk(*) symbol ahead of the path.
Question 2: How can I launch jupyter from a new conda environment?
Now, to launch the desired conda environment, simply run activate <environment name>. In this case, activate py36
For more info, check out this link and this previous Stack Overflow question..
The following commands will add the env in the jupyter notebook directly.
conda create --name test_env
conda activate test_env
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=test_env
Now It should say, "Python [test_env]" if the language is Python and it's using an environment called test_env.
To check on which environment your notebook is running type the following commands in the notebook shell
import sys
print(sys.executable)
To launch the notebook in a new environment deactivate that environment first. Create a conda environment and then install the ipykernel. Activate that environment. Install jupyter on that environment.
conda create --name {envname}
conda install ipykernel --name {envname}
python -m ipykernel install --prefix=C:/anaconda/envs/{envname} --name {envname}
activate envname
pip install jupyter
In your case path "C:/anaconda/envs/{envname}" could be different, check accordingly.
After following all steps, launch notebook and do step 1
run the following in shell.
sys.executable
This should show: Anaconda/envs/envname
On Ubuntu 20.04, none of the suggestions above worked.
I.e. I activated an existing environment. I discovered (using sys.executable and sys.path) that my jupyter notebook kernel was running the DEFAULT Anaconda python, and NOT the python I had installed in my activated environment. The consequence of this was that my notebook was unable to import packages that I had installed into this particular Anaconda environment.
Following instructions above (and a slew of other URLs), I installed ipykernel, nb_conda, and nb_conda_kernels, and ran: python -m ipykernel --user --name myenv.
Using the Kernels|Change Kernel... menu in my Jupyter notebook, I selected myenv, the one I had specified in my python -m ipykernel command.
However, sys.executable showed that this did not "stick".
I tried shutting down and restarting, but nothing resulted in my getting the environment I had selected.
Finally, I simply edited file kernel.json in folder:
~/.local/share/jupyter/kernels/myenv
Sure enough, despite my having performed all the steps suggested above, the first argument in this JSON file was still showing the default python location:
$Anaconda/bin/python (where $Anaconda is the location where I installed anaconda)
I edited file kernel.json with a text editor so that this was changed to:
$Anaconda/envs/myenv/bin/python
Hopefully, my use of myenv is understood to mean that you should replace this with the name of YOUR environment.
Having edited this file, my Jupyter notebooks started working properly - namely, they used the python specified for my activated environment, and I was able to import packages that were installed in this environment, but not the base Anaconda environment.
Clearly, something is messed up in how the set of packages ipykernel, nb_conda, and nb_conda_kernels are configuring Anaconda environments for jupyter.
I have tried every method mentioned above and nothing worked, except installing jupyter in the new environment.
to activate the new environment
conda activate new_env
replace 'new_env' with your environment name.
next install jupyter
'pip install jupyter'
you can also install jupyter by going to anaconda navigator and selecting the right environment, and installing jupyter notebook from Home tab
Adding to the above answers, you can also use
!which python
Type this in a cell and this will show the path of the environment. I'm not sure of the reason, but in my installation, there is no segregation of environments in the notebook, but on activating the environment and launching jupyter notebook, the path used is the python installed in the environment.
For windows 10,
Go into Anaconda Launcher
In the 'Applications on' dropdown menu, select the required conda environment.
Install Jupyter notebook in the Anaconda Launcher
Launch Jupyter notebook from the Anaconda Launcher
The Conda tab is visible in the Jupyter notebook where you can see your active conda env.
For checking on Which Python your Jupyter Notebook is running try executig this code.
from platform import python_version
print(python_version())
In order to run jupyter notebook from your environment
activate MYenv
and install jupyter notebook using command
pip install jupyter notebook
then just
jupyter notebook
What solved the issue for me was that I had to run the following command:
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"
The issue was that I opened a jupyter notebook made with/for a kernel for another conda python environment. That was visible from the output on the terminal; it is handy op run jupyter notebook from a terminal on the conda environment so that you can easily see what error messages are shown. Then it became clear that the notebook tried to run python from another environment.
Inspecting the folder/files:
C:\Users\<username>\AppData\Roaming\jupyter\kernels\<env name>\kernel.json
"argv": [
"D:\\Users\\<username..path>\\envs\\<env name>\\python.exe",
"-m",
"ipykernel_launcher",
"-f",
"{connection_file}"
],
"display_name": "Python (env name)",
"language": "python",
"metadata": {
"debugger": true
}
}
So you can check and correct if necessary.
Second, when using anaconda/conda, make sure you have a healthy channel policy and execute the following commands when creating a new environment:
conda config --add channels conda-forge
conda config --set channel_priority strict
It solved this problem for me, I hope it helps you too.