Conda environments not showing up in Jupyter Notebook

Question

I installed Anaconda  with Python 2 7   and installed Tensorflow in an environment called tensorflow  I can import Tensorflow successfully in that environment   The problem is that Jupyter Notebook does not recognize the new environment I just created  No matter I start Jupyter Notebook from the GUI Navigator or from the command line within the tensorflow env  there is only one kernel in the menu called Python  Root   and Tensorflow cannot be imported  Of course  I clicked on that option multiple times  saved file  re-opened  but these did not help   Strangely  I can see the two environments when I open the Conda tab on the front page of Jupyter  But when I open the Files tab  and try to new a notebook  I still end up with only one kernel   I looked at this question  Link Conda environment with Jupyter Notebook But there isn t such a directory as   Library Jupyter kernels on my computer  This Jupyter directory only has one sub-directory called runtime   I am really confused  Are Conda environments supposed to become kernels automatically   I followed https   ipython readthedocs io en stable install kernel install html to manually set up the kernels  but was told that ipykernel was not found

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This has been so frustrating  My problem was that within a newly constructed conda python36 environment  jupyter refused to load    seaborn    - even though seaborn was installed within that environment  It seemed to be able to import plenty of other files from the same environment     for example numpy and pandas but just not seaborn  I tried many of the fixes suggested here and on other threads without success  Until I realised that Jupyter was not running kernel python from within that environment but running the system python as kernel  Even though a decent looking kernel and kernel json were already present in the environment  It was only after reading this part of the ipython documentation  https   ipython readthedocs io en latest install kernel install html kernels-for-different-environments and using these commands   source activate other-env python -m ipykernel install --user --name other-env --display-name  Python  other-env     I was able to get everything going nicely   I didn   t actually use the    user variable    One thing I have not yet figured is how to set the default python to be the  Python  other-env   one  At present an existing  ipynb file opened from the Home screen will use the system python  I have to use the Kernel menu    Change kernel    to select the environment python

User · Answer

I don t think the other answers are working any more  as conda stopped automatically setting environments up as jupyter kernels  You need to manually add kernels for each environment in the following way   source activate myenv python -m ipykernel install --user --name myenv --display-name  Python  myenv     As documented here http   ipython readthedocs io en stable install kernel install html kernels-for-different-environments Also see this issue   Addendum  You should be able to install the nb conda kernels package with conda install nb conda kernels to add all environments automatically  see https   github com Anaconda-Platform nb conda kernels

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Possible Channel-Specific Issue  I had this issue  again  and it turned out I installed from the conda-forge channel  removing it and reinstalling from anaconda channel instead fixed it for me   Update  I again had the same problem with a new env  this time I did install nb conda kernels from anaconda channel  but my jupyter client was from the conda-forge channel  Uninstalling nb conda kernels and reinstalling updated that to a higher-priority channel   So make sure you ve installed from the correct channels

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Follow the instructions in the iPython documentation for adding different conda environments to the list of kernels to choose from in Jupyter Notebook  In summary  after installing ipykernel  you must activate each conda environment one by one in a terminal and run the command python -m ipykernel install --user --name myenv --display-name  Python  myenv    where myenv is the environment  kernel  you want to add

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Using only environment variables  python -m ipykernel install --user --name   basename  VIRTUAL ENV

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I had to run all the commands mentioned in the top 3 answers to get this working   conda install jupyter conda install nb conda conda install ipykernel python -m ipykernel install --user --name mykernel

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I had similar issue and I found a solution that is working for Mac  Windows and Linux  It takes few key ingredients that are in the answer above   To be able to see conda env in Jupyter notebook  you need      the following package in you base env  conda install nb conda   the following package in each env you create  conda install ipykernel   check the configurationn of jupyter notebook config py  first check if you have a jupyter notebook config py in one of the location given by  jupyter --paths  if it doesn t exist  create it by running  jupyter notebook --generate-config  add or be sure you have the following  c NotebookApp kernel spec manager class  nb conda kernels manager CondaKernelSpecManager    The env you can see in your terminal    On Jupyter Lab you can see the same env as above both the Notebook and Console    And you can choose your env when have a notebook open    The safe way is to create a specific env from which you will run your example of envjupyter lab command  Activate your env  Then add jupyter lab extension example jupyter lab extension  Then you can run  jupyter lab

User · Answer

The nb conda kernels package is the best way to use jupyter with conda  With minimal dependencies and configuration  it allows you to use other conda environments from a jupyter notebook running in a different environment  Quoting its documentation      Installation       This package is designed to be managed solely using conda  It should be installed in the environment from which you run Jupyter Notebook or JupyterLab  This might be your base conda environment  but it need not be  For instance  if the environment  notebook env contains the notebook package  then you would run      conda install -n notebook env nb conda kernels       Any other environments you wish to access in your notebooks must have an appropriate kernel package installed  For instance  to access a Python environment  it must have the ipykernel package  e g       conda install -n python env ipykernel      To utilize an R environment  it   must have the r-irkernel package  e g       conda install -n r env r-irkernel       For other languages  their corresponding kernels must be installed    Then all you need to do is start the jupyter notebook server   conda activate notebook env    only needed if you are not using the base environment for the server   conda install jupyter   in case you have not installed it already jupyter       Despite the plethora of answers and  merv s efforts to improve them  it still hard to find a good one  I made this one CW  so please vote it to the top or improve it

User · Answer

conda install nb conda kernels    in the conda environment where you run jupyter notebook  will make all conda envs available automatically   For access to other environments  the respective kernels must be installed  Here s the ref

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In my case  using Windows 10 and conda 4 6 11  by running the commands   conda install nb conda  conda install -c conda-forge nb conda kernels   from the terminal while having the environment active didn t do the job after I opened Jupyter from the same command line using conda jupyter notebook    The solution was apparently to opened Jupyter from the Anaconda Navigator by going to my environment in Environments  Open Anaconda Navigator  select the environment in Environments  press on the  play  button on the chosen environment  and select  open with Jupyter Notebook    Environments in Anaconda Navigator to run Jupyter from the selected environment

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The annoying thing is that in your tensorflow environment  you can run jupyter notebook without installing jupyter in that environment  Just run   tensorflow    conda install jupyter   and the tensorflow environment should now be visible in Jupyter Notebooks started in any of your conda environments as something like Python  conda env tensorflow

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If your environments are not showing up  make sure you have installed  nb conda kernels in the environment with Jupyter ipykernel in the Python environment you want to access  Anaconda s documentation states that  nb conda kernels should be installed in the environment from which you run Jupyter Notebook or JupyterLab  This might be your base conda environment  but it need not be  For instance  if the environment notebook env contains the notebook package  then you would run conda install -n notebook env nb conda kernels  Any other environments you wish to access in your notebooks must have an appropriate kernel package installed  For instance  to access a Python environment  it must have the ipykernel package  e g  conda install -n python env ipykernel  To utilize an R environment  it must have the r-irkernel package  e g  conda install -n r env r-irkernel  For other languages  their corresponding kernels must be installed   In addition to Python  by installing the appropriatel  kernel package  Jupyter can access kernels from a ton of other languages including R  Julia  Scala Spark  JavaScript  bash  Octave  and even MATLAB  Note that at the time originally posting this  there was a possible cause from nb conda not yet supporting Python 3 6 environments  If other solutions fail to get Jupyter to recognize other conda environments  you can always install and run jupyter from within a specific environment   You may not be able to see or switch to other environments from within Jupyter though    conda create -n py36 test -y python 3 6 jupyter   source activate py36 test  py36 test    which jupyter  home schowell anaconda3 envs py36 test bin jupyter  py36 test    jupyter notebook  Notice that I am running Python 3 6 1 in this notebook   Note that if you do this with many environments  the added storage space from installing Jupyter into every environment may be undesirable  depending on your system

User · Answer

To add your desired environment  in Anaconda Prompt   conda activate  lt env name gt   conda install -c anaconda ipykernel  python -m ipykernel install --user --name  lt env name gt  tested on conda 4 8 3

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We have struggle a lot with this issue  and here s what works for us   If you use the conda-forge channel  it s important to make sure you are using updated packages from conda-forge  even in your Miniconda root environment    So install Miniconda  and then do   conda config --add channels conda-forge --force conda update --all  -y conda install nb conda kernels -y conda env create -f custom env yml -q --force jupyter notebook   and your custom environment will show up in Jupyter as an available kernel  as long as ipykernel was listed for installation in your custom env yml file  like this example   name  bqplot channels  - conda-forge - defaults dependencies  - python gt  3 6 - bqplot - ipykernel   Just to prove it working with a bunch of custom environments  here s a screen grab from Windows

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I encountered this problem when using vscode server  In the conda environment named  quot base quot   I installed the 1 2 0 version of opennmt-py  but I want to run jupyter notebook in the conda environment  quot opennmt2 quot   which contains code using opennmt-py 2 0  I solved the problem by reinstalling jupyter in conda opennmt2   conda install jupyter  After reinstalling  executing jupyter notebook in the opennmt2 environment will execute the newly installed jupyter where jupyter   root miniconda3 envs opennmt2 bin jupyter  root miniconda3 bin jupyter

User · Answer

First you need to activate your environment   pip install ipykernel  Next you can add your virtual environment to Jupyter by typing  python -m ipykernel install --name   my env

User · Answer

Summary  tldr   If you want the  python3  kernel to always run the Python installation from the environment where it is launched  delete the User  python3  kernel  which is taking precedence over whatever the current environment is with   jupyter kernelspec remove python3   Full Solution  I am going to post an alternative and simpler solution for the following case    You have created a conda environment This environment has jupyter installed  which also installs ipykernel  When you run the command jupyter notebook and create a new notebook by clicking  python3  in the  New  dropdown menu  that notebook executes python from the base environment and not from the current environment  You would like it so that launching a new notebook with  python3  within any environment executes the Python version from that environment and NOT the base   I am going to use the name  test env  for the environment for the rest of the solution  Also  note that  python3  is the name of the kernel   The currently top-voted answer does work  but there is an alternative  It says to do the following   python -m ipykernel install --user --name test env --display-name  Python  test env     This will give you the option of using the test env environment regardless of what environment you launch jupyter notebook from  But  launching a notebook with  python3  will still use the Python installation from the base environment   What likely is happening is that there is a user python3 kernel that exists  Run the command jupyter kernelspec list to list all of your environments  For instance  if you have a mac you will be returned the following  my user name is Ted    python3        Users Ted Library Jupyter kernels python3   What Jupyter is doing here is searching through three different paths looking for kernels  It goes from User  to Env  to System  See this document for more details on the paths it searches for each operating system   The two kernels above are both in the User path  meaning they will be available regardless of the environment that you launch a jupyter notebook from  This also means that if there is another  python3  kernel at the environment level  then you will never be able to access it   To me  it makes more sense that choosing the  python3  kernel from the environment you launched the notebook from should execute Python from that environment   You can check to see if you have another  python3  environment by looking in the Env search path for your OS  see the link to the docs above   For me  on my mac   I issued the following command    ls  Users Ted anaconda3 envs test env share jupyter kernels   And I indeed had a  python3  kernel listed there    Thanks to this GitHub issue comment  look at the first response   you can remove the User  python3  environment with the following command   jupyter kernelspec remove python3   Now when you run jupyter kernelspec list  assuming the test env is still active  you will get the following   python3        Users Ted anaconda3 envs test env share jupyter kernels python3   Notice that this path is located within the test env directory  If you create a new environment  install jupyter  activate it  and list the kernels  you will get another  python3  kernel located in its environment path   The User  python3  kernel was taking precedence over any of the Env  python3  kernels  By removing it  the active environment  python3  kernel was exposed and able to be chosen every time  This eliminates the need to manually create kernels  It also makes more sense in terms of software development where one would want to isolate themselves into a single environment  Running a kernel that is different from the host environment doesn t seem natural    It also seems that this User  python3  is not installed for everyone by default  so not everyone is confronted by this issue

User · Answer

Just run conda install ipykernel in your new environment  only then you will get a kernel with this env  This works even if you have different versions installed in each envs and it doesn t install jupyter notebook again  You can start youe notebook from any env you will be able to see newly added kernels

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While  coolscitist s answer worked for me  there is also a way that does not clutter your kernel environment with the complete jupyter package deps  It is described in the ipython docs and is  I suspect  only necessary if you run the notebook server in a non-base environment   conda activate name of your kernel env conda install ipykernel python -m ipykernel install --prefix  home your username  conda envs name of your jupyter server env --name  name of your kernel env    You can check if it works using  conda activate name of your jupyter server env  jupyter kernelspec list

User · Answer

I ran into this same problem where my new conda environment  myenv  couldn t be selected as a kernel or a new notebook  And running jupter notebook from within the env gave the same result   My solution  and what I learned about how Jupyter notebooks recognizes conda-envs and kernels   Installing jupyter and ipython to myenv with conda   conda install -n myenv ipython jupyter   After that  running jupter notebook outside any env listed myenv as a kernel along with my previous environments   Python  conda env old  Python  conda env myenv    Running the notebook once I activated the environment   source activate myenv jupyter notebook   hides all my other environment-kernels and only shows my language kernels   python 2 python 3 R

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This worked for me in windows 10 and latest solution    1  Go inside that conda environment   activate your env name    2  conda install -n your env name  ipykernel  3  python -m ipykernel install --user --name build central --display-name  your env name    NOTE   Include the quotes around  your env name   in step 3

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