I work with Jupyter notebooks in Jetbrain's DataSpell. In two places I can configure a python interpreter. What is the difference between
Python Interpreter (lower right, in my case Conda env "mpd")
Jupyter-Server/ Managed Server (upper right, in my case WSL)
I think this should be the same, not?
In the lower right corner, you see the default Python interpreter configured in your system to run your python scrips. E.g. you have a *.py file in your DataSpell workspace.
The one shown under the Jupyter configuration is the one which will be used for running your notebooks.
If you run Jupyter locally and have just one interpreter, it will be the same for both. However, you can have multiple Python environments in your system and use one for Jupyter and another for your local scripts.
is there a way I can run python code in vscode .dev, im looking for a way to do this on a school Chromebook.
You can install the Python extension in VSCODE.dev, and then run the file with a Jupyter Server by right clicking and choosing "Run Current File in Interactive Window." HOWEVER: You must run the Jupyter Server on some computer (if you're chromebook has the Linux Development Environment enabled, you could do it there). You could run it on a remote computer, but that will cost money.
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.
Is there a way to open an IPython interactive console in pycharm that is connected to an existing running kernel (similar to "python --existing")?
btw: in case it's relevant, in my case, the running kernel is of a Jupiter notebook...
EDIT: To clarify, my question is NOT about how to open an interactive console in PyCharm. It is about how to connect that interactive console to a an existing running (Jupiter notebook) Kernel.
(tl;dr: Use jupyter console --existing in the PyCharm "Terminal" tool window (not the "Python Console" tool window) to connect to an existing iPython kernel running in a local Jupyter Notebook server.)
I can confirm that the comment by #john-moutafis suggesting ipython console --existing is the right idea. The command gives "WARNING | You likely want to use jupyter console in the future" so I tried that.
I have a project using a conda environment as its interpreter. Jupyter Notebook is installed in the conda environment.
I open the Terminal tool window. It automatically activates the conda environment.
I type jupyter notebook. The notebook server starts and a browser window opens.
I create a notebook in the browser, and execute a cell containing foo = "bar".
In PyCharm, I open another Terminal tool window by clicking the plus sign to the left of the terminal pane.
In the new terminal I type jupyter console --existing, and it starts an ipython console session.
At the prompt I type dir(), and foo is among the results, confirming that I'm attached to the same kernel as the notebook.
I don't know how it picks which kernel to connect to when there are multiple kernels running in the notebook server.
Don't type exit in the iPython session if you plan to continue using the notebook, it shuts down the kernel.
Unfortunately, tools like Debug and "Execute Line/Selection in Console", which are available for the "Python Console" tool window, are not available for the "Terminal" tool window. In fact, because the Terminal tool window is a simple tool, and that's where I've run my commands, this solution isn't very integrated with PyCharm. The terminal opens in the project directory and activates the conda environment, and it's conveniently adjacent to the editors and tools of the IDE, but otherwise there's no connection to PyCharm's tools.
If anyone can successfully attach PyCharm's integrated PyDev debugger to a running kernel, please chime in.
I'm using PyCharm 2016.3 on macOS 10.12.3.
The easiest way for me is just to type %qtconsole in a jupyter notebook cell and run it. A qt console will open already connected to the running kennel. No PyCharm involved.
I have installed Anaconda for my Machine Learning course. I'm using it as IPython (Jupyter) notebook, in which we have lessons. OS is Ubuntu 14.04 LTS. Basically, I always run it from Terminal with:
jupyter notebook
I have created new environment called su_env from root environment (exact copy) with one package added. Now, I'm wondering: how can I set environment su_env as default one? I have dozen of notebooks so it's annoying to set up each time for every notebook the environment, in "web" GUI of Jupyter.
EDIT: I'm interested in a solution where you don't have to set environment before running notebook. My logic is that, somehow, automagically, jupyter sets root environment on its own while starting up. Because of that, I'm wondering is it possible to set some config file or something so jupyter sets su_env instead of root. Also, if you know that's not possible (and why), I would like to know that.
First activate the conda environment from the command line, then launch the notebook server.
For example:
$ source activate env_name
$ jupyter notebook
Note: This might only work with environments that were created from within Jupyter Notebook, not environments that were created using conda create on the command line.
In your ~/.bashrc, include the line:
alias jupyter="source activate su_env; jupyter"
This will condense the two commands into one, and you will activate su env whenever you call jupyter notebook or lab or whatever
Edit your bashrc and add source activate su_env then that env will always be active. To switch back to to root (or any other env) source activate env_name
You can use this on conda prompt:
conda activate env_name
jupyter notebook
source activate env_name gives me an error: 'source' is not recognized as an internal or external command, operable program, or batch file.