I am running Jupyter 4.1.0 on a Mac which I downloaded with Anaconda.
I have tried downloading front-end extensions to Jupyter Notebook but cannot get the packages to validate. For example after I downloaded calico-document-tools with:
jupyter nbextension install https://github.com/Calysto/notebook-extensions/archive/master.zip
When I try to validate it, I get the following.
$ jupyter nbextension enable calico-document-tools
Unrecognized JSON config file version, assuming version 1
Enabling notebook extension calico-document-tools...
- Validating: problems found:
- require? X calico-document-tools
What does this error message mean?
Related
I need to set up virtual environments for each language that I use. To do this, I'm running the Ubuntu 20.04 LTS Windows Subsystem for Linux (WSL) on Windows 10. Within WSL, I'm using Anaconda, installed in /usr/local/Anaconda, to create conda virtual environments for each language (i.e. one environment contains all my Python stuff, another contains my R stuff, etc.).
Since WSL doesn't come with a GUI, I'm using Visual Studio Code's (VSCode) Jupyter Notebook Extension to run Jupyter Notebooks to see plots/graphics. So far, I managed to easily create conda environments for Python (with ipython and ipykernel) and R (with IRkernel) and run their code in a notebook via the extension. Each time I set up an environment, the extension is easily able to find the kernel, connect to it and run the code.
However, I've not been able to set up an environment for Julia. I followed the documentation on the Julia website for installing the kernel, which is successfully found by the extension. But, when I try running a cell, the extension says it is trying to connect to the kernel, only for it to timeout and fail.
Here are the steps I have taken so far:
Create a clean conda environment (conda create -n Julia && conda activate Julia)
Install the latest version of Julia (conda install -c conda-forge julia)
Install the latest version of Jupyter (conda install -c conda-forge jupyter)
Install the Julia kernel with the built-in Julia package manager (using Pkg; Pkg.add("IJulia"))
Build the IJulia package (using Pkg; Pkg.build("IJulia"))
Confirm the presence of the Julia kernel (jupyter kernelspec list) which indeed shows the presence of a Julia kernel
Reload the VSCode connection to WSL (Ctrl + Shift + P; >Reload Window)
Shut down WSL via CMD (wsl --shutdown) for changes to take effect and reconnect
After I restart VSCode and WSL, the extension shows an option to use the Julia kernel installed in my conda environment: Julia 1.7.2 (~/.conda/envs/Julia/bin/julia). But when I create a cell and run code in a notebook, the extension creates a popup saying that it is connecting to the kernel and after some time an error message shows up:
Failed to start the Kernel.
Unable to start Kernel `Julia 1.7.2` due to connection timeout.
View Jupyter log for further details
I can also see the kernel spec JSON file in ~/.local/share/jupyter/kernels/julia-1.7/kernel.json
{
"display_name": "Julia 1.7.2",
"argv": [
"/home/USER/.conda/envs/Julia/bin/julia",
"-i",
"--color=yes",
"--project=#.",
"/home/USER/.conda/envs/Julia/share/julia/packages/IJulia/AQu2H/src/kernel.jl",
"{connection_file}"
],
"language": "julia",
"env": {},
"interrupt_mode": "signal"
}
The log file starts showing problems here:
info 17:50:48.378: Process Execution: cwd: ~
cwd: ~
warn 17:50:48.893: StdErr from Kernel Process [91m[1mERROR: [22m[39m
warn 17:50:49.138: StdErr from Kernel Process LoadError:
warn 17:50:49.795: StdErr from Kernel Process ArgumentError: Package IJulia not found in current path:
- Run `import Pkg; Pkg.add("IJulia")` to install the IJulia package.
The extension says it cannot find the IJulia kernel. This perplexes me because I can see the kernel spec in my home directory, the jupyter binary I installed from conda says that its there and the Jupyter Notebook extension can see the kernel. I have no explanation as to why the extension can see the kernel, match up the kernelspec but not be able to connect to it. Help would greatly be appreciated!
When using a Jupyter notebook file in Visual Studio code with the Jupyter extension I receive the error The kernel failed to start due to the missing module 'ipykernel_launcher'. Consider installing this module. View Jupyter [log](command:jupyter.viewOutput) for further details.
This notebook works correctly from the JupyterLab web application when I select the same conda environment that was selected in Visual Studio Code.
pip list shows that ipykernel version 5.3.4 is installed, but I don't know how to install ipykernel_launcher. I tried reinstalling pyzmq and it didn't help.
Any ideas why this isn't working?
Had the same problem. My solution is --
First uninstall all jupyter related modules:
python3 -m pip uninstall -y jupyter jupyter_core jupyter-client jupyter-console jupyterlab_pygments notebook qtconsole nbconvert nbformat jupyterlab-widgets nbclient ipykernel ipynb
(from: https://stackoverflow.com/a/52912244/1516331)
And then reinstall ipykernel. I'm using VScode so when I run a cell, VSCode asked me to installipykernel. The following should work the same alternatively:
conda install -c conda-forge --update-deps --force-reinstall ipykernel -y
I want to install new libraries in a running kernel (not bootstrapping). I'm able to create a sagemaker notebook, which is connected to a EMR cluster, but installing package is a headache.
Unable to install packages on notebook. I've tried several methods like installing packages via terminal in jupyterLab.
$ conda install numba
The installation seems to be working fine on conda_pytorch_p36 notebook, but the packages are not installed on SparkMagic (pyspark) notebook.
Error code:
An error was encountered:
No module named numba
Traceback (most recent call last):
ImportError: No module named numba
The jupyter magic command also doesn't work only in pyspark notebook
!pip install keras
Error:
An error was encountered:
invalid syntax (<stdin>, line 1)
File "<stdin>", line 1
!pip install keras
^
SyntaxError: invalid syntax
Based on answer in a github post, neither did this work
from subprocess import call
call("pip install dm-sonnet".split(" "))
when you are running $ conda install numba via the terminal in JupyterLab,
it's actually succeeding the installation on your local environment. The thing is, when you are using Sparkmagic as your kernal, the code in the cells are always running on the spark cluster, not on the local notebook environment. To run the content of a cell locally you should write %%local in the beginning of the cell. After that everything in that cell will run locally and the installed module will be available.
Otherwise you should install the module on the remote spark cluster.
Read more here:
https://github.com/jupyter-incubator/sparkmagic/blob/master/examples/Pyspark%20Kernel.ipynb
Here's a few links that I went to and did exactly what they said. I don't know what I'm doing wrong.
https://github.com/alexarchambault/jupyter-scala
https://github.com/ipython/ipython/wiki/IPython-kernels-for-other-languages
https://github.com/apache/incubator-toree
http://jcrudy.github.io/blog/html/2013/12/08/introduction_to_iscala.html
None of this is working. It may be some way that my node is configured. I just don't know. Please help.
I tried the following with Jupyterhub notebook and it works seamlessly:
# Step 1: Install spylon kernel
pip install spylon-kernel
# Step 2: create a kernel spec
python -m spylon_kernel install
# Step 3: start jupyter notebook
jupyter notebook
PS: to list all installed kernels, you can run the following command:
jupyter kernelspec list
You can use the information given here.
Ensure you have IPython 3 installed. ipython --version should return a
value >= 3.0. If it's not the case, a quick way of setting it up
consists in installing the Anaconda Python distribution, and then
running
$ pip install --upgrade "ipython[all]"
ipython --version should then return a value >= 3.0.
Download the Jupyter Scala binaries for Scala 2.10 (txz or zip) or
Scala 2.11 (txz or zip), and unpack them in a safe place. Then run
once the jupyter-scala program (or jupyter-scala.bat on Windows) it
contains. That will set-up the Jupyter Scala kernel for the current
user.
Check that Jupyter/IPython knows about Jupyter Scala by running
$ jupyter kernelspec list
This should print, among others, a line like
scala211
(or scala210 dependending on the Scala version you chose).
Then run either IPython console with
$ ipython console --kernel scala211
and start using the Jupyter Scala kernel straightaway, or run Jupyter
Notebook with
$ jupyter notebook
and create Scala 2.11 notebooks by choosing Scala 2.11 in the dropdown
in the upper right of the Jupyter Notebook start page.
Note: Since IPython has now been replaced by Jupyter, we replaced ipython in the above commands with jupyter.
I've just run:
conda create --name base2 --clone base to create an env just like base.
conda activate base2 to move to the new env.
conda install -c conda-forge spylon-kernel.
python -m spylon_kernel install --user. create a kernel spec for Jupyter notebook
jupyter-notebook
...and works just fine.
I'm using:
Anaconda 4.7.12
Jupyter-notebook 6.0.1
Ubuntu 18.04
ipykernel 5.1.3
ipython 7.9.0
ipython_genutils 0.2.0
jupyter_client 5.3.4
jupyter_core 4.6.0
traitlets 4.3.3
from def suma(a: Int) = a + 3
I can't add a comment to Heapify's answer, but his solution worked for JupyterLab on Windows without problems.
I cut and pasted his code into an Anaconda Powershell prompt
pip install spylon-kernel
python -m spylon_kernel install
jupyter notebook
And refreshed my anacopnda launcher and the spylon project option was available.
The answer for Linux can be found here.
Install Scala. Add these lines to ~/.bashrc
export SCALA_HOME=/usr/local/share/scala export
PATH=$PATH:$SCALA_HOME/bin:$PATH
Follow these instructions from the
GitHub site:
Download and unpack pre-packaged binaries Scala 2.11. Unpack each
downloaded archive(s), and, from a console, go to the bin
sub-directory of the directory it contains. Then run the following to
set-up the corresponding Scala kernel:
./jove-scala --kernel-spec
Make sure spark is installed in local along with SPARK_HOME is added or exported in .profile/environment file.
If not, you might get stuck with the following message:
"Intitializing Scala interpreter ..."
without any result.
For mac, I needed only to 3 commands to add Scala and run it with Spark (I had it already installed) on my Jupyter notebook
pip install spylon-kernel
python -m spylon_kernel install
ipython notebook
Once you run them on your terminal, you'll have spylon-kernel in your notebook, which can be used as your a Scala notebook.
spylon-kernel hasn't seen an update in years. These days its much better to use almond.
I upgraded from Ipython Notebook Server to Jupyter Server using anaconda:
(ioos)usgs#gam:~/.jupyter/custom$ conda list jupyter
jupyter 1.0.0 py27_0 defaults
jupyter-client 4.1.1 <pip>
jupyter-console 4.0.3 <pip>
jupyter-core 4.0.6 <pip>
jupyter_client 4.1.1 py27_0 defaults
jupyter_console 4.0.3 py27_0 defaults
jupyter_core 4.0.6 py27_0 defaults
and my "Share Notebook as Gist" button went away.
I imagine that the configuration of extensions changed with the new version.
I can see ~/.local/share/jupyter/nbextensions
with gist.js and a directory called mathjax in it.
Does anyone know how to properly configure the "Share Notebook as Gist" button with this Jupyter version?
You still need to activate the nbextension. The custom.js now lives now in:
$(jupyter --config-dir)/custom
I also have the file:
$HOME/.jupyter/nbconfig/notebook.json
with,
{
"load_extensions": {
"livereveal/main": true,
"gist/gist": true
}
}
that I do remember if I or if jupyter migrate created.
PS: make sure to download the latest gist.js.
I've fixed it using these steps:
Download gist.js:
$ wget https://raw.githubusercontent.com/minrk/ipython_extensions/master/nbextensions/gist.js
Install the extension as a user:
$ jupyter nbextension install --user gist.js
Note: the command accepts the file name (with .js)
To enable it, put into $(jupyter --config-dir)/nbconfig/notebook.json:
{
"load_extensions": {
"gist": true
}
}
Though the instructions here suggest that python snippets should be used instead.
To install, simply run:
pip install jupyter-notebook-gist
jupyter serverextension enable --py jupyter_notebook_gist
jupyter nbextension install --py jupyter_notebook_gist
jupyter nbextension enable --py jupyter_notebook_gist
jupyter nbextension enable --py widgetsnbextension
you also need to configure jupyter. for details visit here