How to add conda environment to jupyter lab

python anaconda jupyter-notebook jupyter-lab

I'm using Jupyter Lab and I'm having trouble to add conda environment. The idea is to launch Jupyter Lab from my base environment, and then to be able to choose my other conda envs as kernels.

I installed the package nb_conda_kernels which is supposed to do just that, but it's not working as I want. Indeed, let's assume I create a new Conda Environment, then I launch jupyter lab from base, I can't see the new environment as an available kernel.

I have found a "fix", which works everytime but is not convenient at all. If I install Jupyter Notebook in my new environment, then launch a jupyter notebook from this new environment, close it, go back to base environment, and then launch Jupyter Lab from base environment, my new environment is available as a kernel in Jupyter Lab.

If you know how to make it work without this "fix", I would be very grateful.

Nihal Sangeeth

Assuming your conda-env is named cenv, it is as simple as :

$ conda activate cenv           # . ./cenv/bin/activate in case of virtualenv
(cenv)$ conda install ipykernel
(cenv)$ ipython kernel install --user --name=<any_name_for_kernel>
(cenv)$ conda deactivate

If you restart your jupyter notebook/lab you will be able to see the new kernel available. For newer versions of jupyter kernel will appear without restarting the instance. Just refresh by pressing F5.

PS: If you are using virtualenv etc. the above steps hold good.

I direct people having difficulties getting a tensorflow environment to work in jupyter lab/notebook to this answer. It worked for me. Thanks.

I would only add that once you have the new kernel, go to your jupyter notebook and, under "kernel", select "change kernel" to your newly created kernel. Once there you can use things like import tensorflow as tf if your environment was a tensorflow environment. I also recommend this for people getting to a tensorflow environment form jupyter. I redirected multiple questions on that to here.

why doesn't jupyter lab just inherit the environment as jupyter notebook does for me? Anyway this fixed my problem so thanks for that.

It helps to name the kernel so that it specifies which environment/use it is tied to.

While is an effective answer, I'd still recommend the nb_conda_kernels approach instead, since it avoids the manual registration step, which can be prone to mistakes.

Statistic Dean

A solution using nb_conda_kernels. First, install it in your base environment :

(base)$ conda install -c conda-forge nb_conda_kernels

Then in order to get a kernel for the conda_env cenv :

$ conda activate cenv
(cenv)$ conda install ipykernel
(cenv)$ conda deactivate

You will get a new kernel named Python [conda env:cenv] in your next run of jupyter lab / jupyter notebook

Note : If you have installed nb_conda_kernels, and want to create a new conda environment and have it accessible right away then

conda create -n new_env_name ipykernel

will do the job.

Sadly this doesn't seem to work (jupyter lab version 1.1.4 with python 3.7.4) - use the accepted answer above to install the kernel.

Works for me with Jupyter Lab 1.1.4, Python 3.7.3, and nb_conda_kernels 2.2.2. No need to "install" the kernel, except in the environment that you want to access in your notebook.

@sappjw The accepted answer works but this one lacks $ ipython kernel install --user --name=<any_name_for_kernel> and did not see the new kernel until I did this

@Pherdindy the difference is that this answer relies on nb-conda_kernels to detect the conda environment rendering the ipython kernel install line unnecessary

I prefer this method as you can be running a notebook, install a new package and have it immediately reflected in the notebook

Daniel Firebanks-Quevedo

I tried both of the above solutions and they didn't quite work for me. Then I encountered this medium article which solved it: https://medium.com/@jeremy.from.earth/multiple-python-kernels-for-jupyter-lab-with-conda-c67e50de3aa3

Essentially, after running conda install ipykernel inside of your cenv environment, it is also good to run python -m ipykernel install --user --name cenv within the cenv environment - that way, we make sure that the version of python that is used within the jupyter environment is the one in cenv. Cheers!

This worked for me when all else failed. Thanks! Still not clear on why nb_conda_kernels doesn't seem to automatically do the job for me anymore. Note that, in my experience, if you have ipykernel, jupyterlab, and nb_conda_kernels installed in your base environment and launch JupyterLab from within the base environment, it is more likely to see all available conda kernels, weirdly.

Did this too for a new conda env that wasn't showing up, further adjusted the generated kernel.json by referring to other existing conda envs in ~/.local/share/jupyter/kernels/

I agree this is a slightly less error-prone version of stackoverflow.com/a/53546634/570918, but more robust still is the nb_conda_kernels approach, which will automatically detect any environments with ipykernel (or other language-specific kernel packages) installed.

Ryan M

The following worked for me

pip install nb_conda

https://github.com/Anaconda-Platform/nb_conda

github.com/Anaconda-Platform/nb_conda_kernels

The proposed command gave me the results: ERROR: No matching distribution found for nb_conda

install it through conda install nb_conda

How to add conda environment to jupyter lab

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