Related
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.)
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
If your environments are not showing up, make sure you have installed
nb_conda_kernels in the environment with Jupyter
ipykernel and ipywidgets in the Python environment you want to access (note that ipywidgets is to enable some Juptyer functionality, not environment visibility, see related docs).
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).
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].
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
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.
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.
To add a conda environment to Jupyter:
In Anaconda Prompt :
run conda activate <env name>
run conda install -c anaconda ipykernel
run python -m ipykernel install --user --name=<env name>
** tested on conda 4.8.3 4.11.0
$ 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.
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)
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!
This is an old thread, but running this in Anaconda prompt, in my environment of interest, worked for me:
ipython kernel install --name "myenvname" --user
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>=3.6
- bqplot
- ipykernel
Just to prove it working with a bunch of custom environments, here's a screen grab from Windows:
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
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.
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
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
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
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.
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 :)
I encountered this problem when using vscode server.
In the conda environment named "base", I installed the 1.2.0 version of opennmt-py, but I want to run jupyter notebook in the conda environment "opennmt2", 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
For conda 4.5.12, what works for me is (my virtual env is called nwt)
conda create --name nwt python=3
after that I need to activate the virtual environment and install the ipykernel
activate nwt
pip install ipykernel
then what works for me is:
python -m ipykernel install --user --name env_name --display-name "name of your choosing."
As an example, I am using 'nwt' as the display name for the virtual env. And after running the commands above. Run 'jupyter notebook" in Anaconda Prompt again. What I get is:
Using only environment variables:
python -m ipykernel install --user --name $(basename $VIRTUAL_ENV)
I just wanted to add to the previous answers: in case installing nb_conda_kernels, ipywidgets and ipekernel dosen't work, make sure your version of Jupyter is up to date. My envs suddenly stopped showing up after a period of everything working fine, and it resumed working after I simply updated jupyter through the anaconda navigator.
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
I have been using Spyder installed with with Anaconda distribution which uses Python 2.7 as default. Currently I need to set up a development virtual environment with Python 3.4.
Top two suggestions after research online are:
to set up virtual environment first and to point change the preferences of Spyder , e.g here;
to install all Spyder dependencies, like PyQt4, in the virtual environment itself, e. g. here ;
Both recommendations are cumbersome and do not look like smart options for development.
Is there a solution that would allow to run Spyder with required Python version automatically after activating the required virtual environment?
Here is a quick way to do it in 2021 using the Anaconda Navigator. This is the most reliable way to do it, unless you want to create environments programmatically which I don't think is the case for most users:
Open Anaconda Navigator.
Click on Environments > Create and give a name to your environment. Be sure to change Python/R Kernel version if needed.
Go "Home" and click on "Install" under the Spyder box.
Click "Launch/Run"
There are still a few minor bugs when setting up your environment, most of them should be solved by restarting the Navigator.
If you find a bug, please help us posting it in the Anaconda Issues bug-tracker too! If you run into trouble creating the environment or if the environment was not correctly created you can double check what got installed: Clicking the "Environments" opens a management window showing installed packages. Search and select Spyder-related packages and then click on "Apply" to install them.
There is an option to create virtual environments in Anaconda with required Python version.
conda create -n myenv python=3.4
To activate it :
source activate myenv # (in linux, you can use . as a shortcut for "source")
activate myenv # (in windows - note that you should be in your c:\anaconda2 directory)
UPDATE. I have tested it with Ubuntu 18.04. Now you have to install spyder additionally for the new environment with this command (after the activation of the environment with the command above):
conda install spyder
(I have also tested the installation with pip, but for Python 3.4 or older versions, it breaks with the library dependencies error that requires manual installation.)
And now to run Spyder with Python 3.4 just type:
spyder
EDIT from a reader:
For a normal opening, use "Anaconda Prompt" > activate myenv > spyder (then the "Anaconda Prompt" must stay open, you cannot use it for other commands, and a force-close will shut down Spyder). This is of course faster than the long load of "Anaconda Navigator" > switch environment > launch Spyder (#adelriosantiago's answer).
What worked for me :
run spyder from the environment (after source activate)
go to Tools --> preferences --> python Interpreter and select the python file from the env you want to link to spyder
ex : /home/you/anaconda3/envs/your_env/bin/python
Worked on ubuntu 16, spyder3, python3.6.
Additional to tomaskazemekas's answer: you should install spyder in that virtual environment by:
conda install -n myenv spyder
(on Windows, for Linux or MacOS, you can search for similar commands)
To do without reinstalling spyder in all environments follow official reference here.
In summary (tested with conda):
Spyder should be installed in the base environment
From the system prompt:
Create an new environment. Note that depending on how you create it (conda, virtualenv) the environment folder will be located at different place on your system)
Activate the environment (e.g., conda activate [yourEnvName])
Install spyder-kernels inside the environment (e.g., conda install spyder-kernels)
Find and copy the path for the python executable inside the environment. Finding this path can be done using from the prompt this command python -c "import sys; print(sys.executable)"
Deactivate the environment (i.e., return to base conda deactivate)
run spyder (spyder3)
Finally in spyder Tool menu go to
Preferences > Python Interpreter > Use the following interpreter and paste the environment python executable path
Restart the ipython console
PS: in spyder you should see at the bottom something like this
Voila
I just had the same problem trying to get Spyder to run in Virtual Environment.
The solution is simple:
Activate your virtual environment.
Then pip install Spyder and its dependencies (PyQt5) in your virtual environment.
Then launch Spyder3 from your virtual environment CLI.
It works fine for me now.
The above answers are correct but I calling spyder within my virtualenv would still use my PATH to look up the version of spyder in my default anaconda env. I found this answer which gave the following workaround:
source activate my_env # activate your target env with spyder installed
conda info -e # look up the directory of your conda env
find /path/to/my/env -name spyder # search for the spyder executable in your env
/path/to/my/env/then/to/spyder # run that executable directly
I chose this over modifying PATH or adding a link to the executable at a higher priority in PATH since I felt this was less likely to break other programs. However, I did add an alias to the executable in ~/.bash_aliases.
From Spyder official page on Github:
The naive approach
To use Spyder with another environment, the simplest way is to just
install it directly into the environment from which you'd like to use
the packages in, and run it from there. This works with all Spyder
versions and should require no extra configuration once the IDE is
installed; however, it results in multiple installations to manage and
isn't as flexible or configurable as the alternative. Therefore, when
dealing with multiple environments, we recommend the modular
approach.
The modular approach
Starting with Spyder 3.3.1, you can install the modular
spyder-kernels package into any Python environment (conda
environment, virtualenv/venv, system Python, WinPython, etc) in
which you wish to work, and then change the Python interpreter used by
Spyder on its IPython consoles to point to the Python executable of
that environment.
This takes a small amount of preparation and configuration, but is
much "lighter" and quicker than a full Spyder installation into that
environment, avoids dependency conflicts, and opens up new workflow
possibilities.
To achieve this, follow these steps:
1- Activate the environment (e.g. myenv) in which you'd like to work (e.g. with conda activate myenv for conda, source myenv/bin/activate or workon myenv for virtualenv/venv, etc)
2- Install the spyder-kernels package there, with the command:
3- conda install spyder-kernels if using conda/Anaconda,
4- pip install spyder-kernels if using pip/virtualenv.
5- After installing via either method, run the following command inside the same environment:
python -c "import sys; print(sys.executable)"
and copy the path returned by that command (it should end in
python, pythonw, python.exe or pythonw.exe, depending on your
operating system).
6- Deactivate that environment, activate the one in which Spyder is installed (if you've installed it in its own environment) and start
Spyder as you normally would.
7- After Spyder has started, navigate to Preferences > Python Interpreter > Use the following interpreter and paste the path from
Step 3 into the text box.
8- Start a new IPython console. All packages installed in your myenv environment should be available there. If conda is used, the
name of the current environment and its Python version should be
displayed in Spyder's status bar, and hovering over it should display
the path of the selected interpreter.
On Windows:
You can create a shortcut executing
Anaconda3\pythonw.exe Anaconda3\cwp.py Anaconda3\envs\<your_env> Anaconda3\envs\<your env>\pythonw.exe Anaconda3\envs\<your_env>\Scripts\spyder-script.py
However, if you started spyder from your venv inside Anaconda shell, it creates this shortcut for you automatically in the Windows menu. The steps:
install spyder in your venv using the methods mentioned in the other answers here.
(in anaconda:) activate testenv; though in my case, this step was not needed.
Look up the windows menu "recently added" or just search for "spyder" in the windows menu, find spyder (testenv) and
[add that to taskbar] and / or
[look up the file source location] and copy that to your desktop, e.g. from C:\Users\USER\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Anaconda3 (64-bit), where the spyder links for any of my environments can be found.
Now you can directly start spyder from a shortcut without the need to open anaconda prompt.
For me below worked:
Open Anaconda, setup new environment, then select the env and click on play icon as shown below:
Then click on Open Terminal, and type "spyder" in terminal, it will open the spyder with selected env.
Note: For me directly opening Sypder from Home page was always opening with base env.
I follow one of the advice above and indeed it works. In summary while you download Anaconda on Ubuntu using the advice given above can help you to 'create' environments. The default when you download Spyder in my case is: (base) smith#ubuntu ~$. After you create the environment, i.e. fenics and activate it with $ conda activate fenics the prompt change to (fenics) smith#ubuntu ~$. Then you launch Spyder from this prompt, i.e $ spyder and your system open the Spyder IDE, and you can write fenics code on it. Remember every time you open a terminal your system open the default prompt. You have to activate your environment where your package is and the prompt change to it i.e. (fenics).
Apologies first-hand if I've done some silly mistake in the below raised issue. I have been stuck on this since quite some time, trying to successfully install multiple Python versions (via separate virtual environments) and run Jupyter notebook with all three versions in Change kernel switch.
AIM:
Setup Anaconda with Python 3.5.6 as default and create two virtual environments with Python 2.7.13 and Python 3.7.3 version and to be able to switch between these three Python versions on a Jupyter Notebook on Windows.
Process Followed: What I did (and ended in mess):
I first successfully installed Anaconda3 with Python 3.5.6 as default (installed in C:\ProgramData\Anaconda3) and set the PATH variables. Jupyter Notebook was up and running with an ipython kernel running from 'base' (or root) from
(base) jupyter kernelspec list
Available kernels:
python_3.5.6 C:\Users\username\AppData\Roaming\jupyter\kernels\python_3.5.6
and kernel.json file was also mapped to the correct python version.
.
Then I created my first virtual environment (Python_2.7.13_ENV):
(base) conda create --p C:\ProgramData\Anaconda3\envs\Python_2.7.13_ENV python=2.7.13
and installed jupyter on it
(base) activate Python_2.7.13_ENV
(Python_2.7.13_ENV) conda install notebook ipykernel
(Python_2.7.13_ENV) python -m ipykernel install --p C:\Users\username\AppData\Roaming\jupyter\kernels\ --name Python_2.7.13_ENV --display-name "python_2.7.13"
I used the prefix notation as the default installation syntax was installing it for the root user and I wanted it to install it only for a specific user.
And this worked like a charm. The updated jupyter kernelspec read:
(base) jupyter kernelspec list
Available kernels:
python_3.5.6 C:\Users\username\AppData\Roaming\jupyter\kernels\python_3.5.6
python_2.7.13 C:\Users\username\AppData\Roaming\jupyter\kernels\python_2.7.13
and kernel.json file was also mapped to the correct python version ("C:\\ProgramData\\Anaconda3\\envs\\Python_2.7.13\\python.exe")
This was also working fine. I could open a file in jupyter and succesfully switch between the two kernels.
.
Than I followed the same steps for creating my second virtual environment (Python_3.7.3_ENV):
Now, the updated kernelspec read:
(base) jupyter kernelspec list
Available kernels:
python_3.5.6 C:\Users\username\AppData\Roaming\jupyter\kernels\python_3.5.6
python_2.7.13 C:\Users\username\AppData\Roaming\jupyter\kernels\python_2.7.13
python_3.7.3 C:\Users\username\AppData\Roaming\jupyter\kernels\python_3.7.3
and the kernel.json was also mapped to the correct python version.
Problem:
Both the virtual envs were created successfully.
Now when I run a jupyter notebook and try to switch to Python 2.7.13 kernel, it works fine, but shows a ImportError: DLL load failed (due to some import issue in zmq) on switching to Python_3.7.3 kernel.
However, when I first activate the Python_3.7.3_ENV virtual env and then load the jupyter notebook, I am able to switch between all three Python versions.
Can anybody provide a solution on how to toggle between all three versions without activating the virtual env beforehand if it's possible as I am able to do it with Py 2.7 & Py 3.5 versions.
PS. I have set the 'open with' default on right-click on a ipynb file to jupyter-notebook.exe.
I have similar setting: default python 3.7, env: python 3.6 and python 2.7.
set multiple versions of python/ipykernels running on jupyter notebook
Check you Anaconda version, if it is '>= 4.1.0', it is easier since after 4.1.0, anaconda includes a special package nb_conda_kernels that detects conda environments with notebook kernels and automatically registers them. If the Anaconda version is lower than 4.1.0 or just want to manually do that, you can reference here.
For python2.7 and adding the ipykernel to jupyter notebook, shown as 'Python (py27)'
conda create -n py27 python=2.7 ipykernel
conda activate py27
python -m ipykernel install --user --name=py27 --display-name "Python (py27)"
For python3.6 and adding the ipykernel to jupyter notebook, shown as 'Python (py36)'
conda create -n py36 python=3.6 ipykernel
conda activate py36
python -m ipykernel install --user --name=py36 --display-name "Python (py36)"
Then we can check if they works well not. You can deactivate from specific env and go back to the base env, and type jupyter notebook (or use graphic shortcut for jupyter notebook anaconda provides)and then you can create 'new' notebook, you can find apart from default Python 3, there also shows Python (py27) and Python (py36).
I was making mistake at the beginning to check which python was running:
Do not just use !python --version in jupyter notebook no matter which version kernel you are using.Because this is just like you running command in base env, it will always show the default env python version, which in my case is python 3.7.x.
What you can do to:
from platform import python_version
print(python_version())
## or this one, which ever you like.
import sys
print(sys.executable)
print(sys.version)
print(sys.version_info)
You will get right python version accordingly.
Show list of jupyter notebook kernels
jupyter kernelspec list (in base env, it will show all kernels name)
If you want to remove specific kernel, you can use:
jupyter kernelspec uninstall <kernel name>
Another way to confirm the python version is right, you can check the ipykernel json file, if the path to start the python is right or not.
To do this: use jupyter kernelspec list, so you can get to know the path of json file. (for example, in my case I can get the path for py27: C:\Users\username\AppData\Roaming\jupyter\kernels\py27). Then, you can go to the dir/path, and you can check what is shown in kernel.json
for the argv, it should show the path to access related python version. For example,
for py27, it will show like "C:\\Users\\username\\Anaconda3\\envs\\py27\\python.exe",
for py36, it will show like "C:\\Users\\username\\Anaconda3\\envs\\py36\\python.exe"
Last thing about setting PATH env variables: do not add Anaconda to the Windows PATH because this can interfere with other software. Instead, open Anaconda with the Start Menu and select Anaconda Prompt, or use Anaconda Navigator. More information can check the Anaconda official docs.
Hope this may be helpful if you meet similar issues.
PS: I was always using Unix like system, not so used to Windows env setting. That is what I met for setting different kernels for different anaconda envs and summarized it. Hope it helps.
After here and there, the only to make this work is having that virtual environment activated.
Following the above steps, I have installed Python 2.7.13, 3.5.6 and 3.7.3 version. My default python is Python 3.5.6 while the other two versions are installed in two virtual environments - Python_2.7.13_ENV and Python_3.7.3_ENV respectively.
How to use them ?
For Python 2.7 and 3.5, just use the Jupyter notebooks normally as you would. Since the default python is set to Python 3.5.6 there is no problem in switching between two versions using Change Kernel option in Jupyter Notebook Toolbar.
For Python 3.7 we first need to activate Python_3.7.3_ENV virtual environemnt and then we can switch between all three versions succesfully using Change Kernel option in Jupyter Notebook.
I'm trying to run the anaconda distribution of python libraries in a Jupyter Notebook, but when I run the notebook I keep getting ImportErrors because the python path is set to the default distribution from Mac OS X 10.11
When I print out the sys.path and sys.executable, they differ when running python vs running jupyter notebook. For example,
from pprint import pprint as p
import sys
p(sys.path)
After doing this in python I get the correct output:
['',
'/Users/glennraskovich/anaconda2/lib/python27.zip',
'/Users/glennraskovich/anaconda2/lib/python2.7',
'/Users/glennraskovich/anaconda2/lib/python2.7/plat-darwin',
'/Users/glennraskovich/anaconda2/lib/python2.7/plat-mac',
'/Users/glennraskovich/anaconda2/lib/python2.7/plat-mac/lib-scriptpackages',
'/Users/glennraskovich/anaconda2/lib/python2.7/lib-tk',
'/Users/glennraskovich/anaconda2/lib/python2.7/lib-old',
'/Users/glennraskovich/anaconda2/lib/python2.7/lib-dynload',
'/Users/glennraskovich/anaconda2/lib/python2.7/site-packages',
'/Users/glennraskovich/anaconda2/lib/python2.7/site-packages/aeosa']
But when running this in jupyter notebook I get:
['',
'/usr/local/lib/python2.7/site-packages/dask-0.11.0-py2.7.egg',
'/usr/local/lib/python2.7/site-packages/networkx-1.11-py2.7.egg',
'/usr/local/lib/python2.7/site-packages/six-1.10.0-py2.7.egg',
'/usr/local/lib/python2.7/site-packages/Pillow-3.3.1-py2.7-macosx-10.11-x86_64.egg',
'/usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/lib/python27.zip',
'/usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/lib/python2.7',
'/usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-darwin',
'/usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac',
'/usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac/lib-scriptpackages',
'/usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-tk',
'/usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-old',
'/usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload',
'/usr/local/lib/python2.7/site-packages',
'/Library/Python/2.7/site-packages',
'/usr/local/lib/python2.7/site-packages/IPython/extensions',
'/Users/glennraskovich/.ipython']
For the sys.executable,
p(sys.executable)
In python, correct output:
/Users/glennraskovich/anaconda2/bin/python
But in jupyter notebook, sys.executable is not set to the anaconda version
/usr/local/opt/python/bin/python2.7
I've tried setting PATH in my .bashrc and .bash_profile, and using the commands which python, which jupyter and such show anaconda paths but jupyter notebook is not using the anaconda paths. What could be the issue here?
I figured out the solution, since the kernel was set to use the default mac os x's python I fixed it by using the commands
python2 -m pip install ipykernel
python2 -m ipykernel install --user
For me, I installed Jupyter after creating an environment, but then was trying to run a module installed from the base env. I found by "jupyter kernelspec list" (https://github.com/jupyter/notebook/issues/2563), my kernel.json at C:\Users\username\Anaconda37\share\jupyter\kernels\python3\kernel.json was pointing to the python.exe in my working env. Changed the path and solved it.
This was an exhaustive description of python path setting.
I had this problem when I used Anaconda Navigator and the command line. I typed 'source activate ' into the console and then used Anaconda Navigator to open Jupyter. In Anaconda Navigator, however, I wasn't in the right environment which caused the problem. This is because Anaconda Navigator comes with its own activation for virtual environments (when you click on them). So you either need to activate the virtual environment from the console and then start Jupyter from the console or you need to activate the virtual environment in Anaconda Navigator and start Jupyter from the Navigator. Both ways work but not in combination. :-)
My experience:
I had a conda virtual environment in Windows, and I had linked it as a kernel in Jupyter Notebook. However, the sys.executable was pointing at the global Python installation executable.
Performing the steps in #Glenn's answer didn't help. To solve it:
Find the executable python.exe for your environment. Mine was inside C:\Users\youruser\miniconda3\envs\my_conda_environment\python.exe.
Find the kernel.json configuration file for your environment inside C:\Users\youruser\AppData\Roaming\jupyter\kernels\yourkernel\kernel.json
Modifiy the kernel.json configuration path, replacing the existing executable path by the one from step 1.
Restart jupyter and it's done!
I want to keep the Mac Python as my main 'python'. The reason for that is the recommendation in Python website here. I also want to add a separate environment for my Python3 (Anaconda).
For doing that I installed the Anaconda Python to get access to Conda and then I made an environment for my Python3 using the following command:
conda create -n py36 python=3.6 anaconda
When I installed the Anaconda python it added this to my .bash_profile file to get access to all conda commands:
# added by Anaconda3 4.4.0 installer
# export PATH="/Users/omidb/anaconda/bin:$PATH"
Now my default python is anaconda python which I don't want to.
How can I have default Mac python as my main python and then when I needed Anaconda, just use source activate py36 ?
UPDATED ANSWER
After testing this, I feel it's appropriate to offer this as a simple solution for using Mac Python as the default and only using Conda Python when desired.
You need to add/move the conda path to the end of your PATH environment via export command. This should allow you to use the Mac Python as the default and only use Anaconda Python after calling source activate py36.
export PATH="$PATH:/Users/omidb/anaconda/bin"
Path Resolution
This solution assumes you have /usr/bin/ (where Mac Python is) already in your PATH. Resolution order should check that directory first assuming it's first in the PATH. Also, this setup does not require symlinks in /usr/local/bin. I am not a fan of manipulating system-level resources for solutions that can be done with user resources (directories).
Default Python Setup
After moving the Anaconda path to the end of your PATH environment variable, you can validate that which python references /usr/bin/python, the location for Mac Python. You will run Mac python by default at the command line.
Running Conda Python
As previously noted, you have to call source activate py36 when you want to use the conda virtual environment. There is no need for adding symlinks to /usr/local/bin as they are already available through ~/anaconda/bin/.
Furthermore, source activate py36 (or any other Anaconda environment), it will add the appropriate environment path for Anaconda python to the beginning of your PATH environment variable, which (referring back to Path Resolution) would be executed when run as python on the command line. You can validate this with which python after running source activate py36. conda also stores the previous path as the environment variable CONDA_PATH_BACKUP.
Deactivating Conda
After running source deactivate, the original path is restored, so you will then be back to running Mac python.
Faced the same problem and question is too old but the simplest of doing this which I found is:
1. Let's check if the default python version is pointing to Conda python
which python - If Conda installation updated to your bashrc or zshrc, it will show that path
Running Command:
conda config --set auto_activate_base false
It's will make sure that when you start the terminal, Conda doesn't get activated as base
Now if you check python --version or which python - It should be pointing to mac default python version
Now whenever you want to use conda, all conda commands are available with conda <command>
Create the virtual env using conda create --name venv and activate it using conda activate <venv_name>
Now, I am able to use different python versions I required with conda and default python version as system default