What sets of tasks or potential code can be done to run jupyter notebook with analysis on open data to be run behind company's firewall?
I tried to search and google for appropriate solution but have not found anything
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So our team created a new Azure Machine Learning resource, but whenever I try to add a new notebook and try to edit it using "JUPYTERLAB" i get ERR_HTTP2_PROTOCOL_ERROR error, but the same notebook, when edited using EDIT IN JUPYTER works perfectly.
This is a blank and clean notebook, I also tried 2 different laptops and multiple browsers per laptop, same error. I also tried incognito and clearing cookies, but to no avail.
update: I seem to have accidentally replicated the issue and I now know what is causing it, the situation is that Im using my work laptop and constantly switching VPN connections, and some times, connecting to the AZURE PORTAl OUTSIDE the VPN. So, when you've worked on a notebook while inside a VPN, then you disconnected, and tried loading the notebook sometime later, you will encounter this
Have you tried creating a new Azure Machine Learning Compute Instance? Sometimes the VM has a fatal issue and a new one needs to be spun up.
Also try modifying the working Jupyter Notebook url by adding /lab to the end.
This problem has stomped me for hours, but I was finally able to fix it. What I did was I opened a terminal and did a Jupyter lab rebuild "jupyter lab build"
I am trying to connect from google colab to local host - it does not work,
google troubleshooting advise suggests that I should allow local Jupyter notebook to accept colab requests - how to do it ?
Screenshot from local host which confirms that requests from colab are forbidden.
There should be some config modification to allow such requests, is not it ?
Advise from colab:
How I fixed my connection issues:
If after the above commands don't work, what finally did it for me was creating a firewall rule for port 8888.
Another big one that is in the instructions in your command line but not stated on the provided Google tutorial that some people will miss is that you need to open the URL in your browser that's given to you after launching Jupyter in order for it to create the access cookie and make it valid (sort of speak).
From the screenshot you provided, make sure you have "http://localhost:8888/?token=2534..." opened in your browser that is logged in with the same account accessing colab before it will allow access to colab (a separate window or tab will usually pops up on it's own when you run the command though).
Alternatively, you can also add the --no-browser tag to prevent the need to open it in your browser
Other solutions that involve installation issues to mitigate network issues:
There were a number of other troubleshooting steps that were particular to me but dont know if will apply to you but did you have any issues when installing Jupyter?
For me, even though jupyter would still launch, there were errors when installing on Windows, and to fix those I had to replace pip with pipwin and go through the steps that way.
For example
pipwin install jupyterlabs
pipwin install jupyter_http_over_ws
And in case it helps anyone else coming across this in the future, if you're using the DOS cmd line and have issues launching jupyter, replace the "" with a "^" to indicate continuation
i.e.:
jupyter notebook --NotebookApp.allow_origin="https://colab.research.google.com" --port=8888 --NotebookApp.port_retries=0
I'm new to Jupyter notebooks/Lab and I've successfully got interaction with pop-out windows and buttons etc with ipywidgets on local instances of Jupyter Lab but not in the cloud when using notebooks.ai - the code runs without error but doesn't create the appropriate windows/button.
Is there anyway to get this working or is this an inherent restriction of using Jupyter Lab in the cloud?
I'm wondering whether there are firewall settings that need configuring to get this to work?
The X11 forwarding is disabled on the docker machines provided by notebooks.ai. Hence, any pop-up interaction is not being forwarded to your machine. However, you can see any inline plot/button in the Jupyter notebook.
If you are interested in this feature there is a GitHub repository to request features (I have never tried it) but might be interesting for you to receive a more in-depth explanation, workaround, or solution.
PROVE:
In the launcher tab you can either run a notebook, a python interpreter or a terminal on the remote docker machine. If you select the terminal and type echo $DISPLAY, you will see that the result is an empty line (if a valid display was attached you will see something like: DISPLAY=localhost:11.0).
For further information about using BASH to check if X11 forwarding is enabled from SSH check this question.
I am developping with dask distributed and this package provides a very useful debugging view as a bokeh application.
I want to have this application next to my notebook in a jupyterlab tab.
I have managed to do so by opening the jupyter lab reference (Help > Jupyter lab reference) and modifying the url with the inspect tool of firefox from it's orignial value to 127.0.0.1:8787 (the URL for the dask dashboard)
see below :
dask scheduler console in jupyter lab
Is there a less hacky way to open a website in jupyter lab?
It looks like there is an active Github issue for this. I recommend tracking https://github.com/jupyterlab/jupyterlab/issues/2369
It sounds like you may also want to look at https://github.com/dask/dask-labextension
I am trying to run a R notebook on Microsoft's Azure notebooks cloud service.
When I am trying to run all cells, it displays a Loading required package: ggplot2 in the last cell and then the Kernel systematically crashes. I get:
The kernel appears to have died. It will restart automatically.
But the Kernel does not restart automatically.
How can I get a log describing the encountered issue? Is there a way to activate a debugger?
When you're running Jupyter usually you'll see messages about kernel issues in standard I/O of the console that you launch. In Azure Notebooks this gets redirected to a file at ~/.nb.log. You can open a new terminal by clicking on the Jupyter icon, and then doing New->Terminal, and doing cat ~/.nb.log. You could also start a new Python notebook for this purpose and do "!cat ~/.nb.log" - but unfortunately you can't just do that from an R notebook, they don't support the "magic" ! commands.
Usually that gives you a good starting point. If that doesn't help much you could try invoking R directly from the terminal and trying the repro steps there and see if that's more useful.