I have access to a google cloud compute instance where I run jupyterlab.
I updated jupyterlab to 2.1.4 but then I can't build with the extensions.
When I build in the console (The one built in jupyter lab and the one of the instance), it tells me that the build went fine but when I reload jupyterlab tells me it needs to build again.
I had some other issues such as updating a package with pip and when trying to use it, but the version remaining the same.
How can I fix this issue?
Thank you
Related
I am running a Windows 10 machine, where I have a Python installation installed from one of the programs I am working with. This leads to dependencies of this program to specific versions of Python packages, including Jupyter and Jupyterlab, and I cannot update/upgrade them without breaking the functionality in the original program.
Hence, I decided to install a more recent version of Python in addition to the one I already have on my machine. That was also not the issue, and installing all the packages I was after went fine so far.
However, even though I installed nodejs and npm within the new version of Python, when attempting to install a widget in Jupyterlab, it still does not recognize the packages.
In addition, wen running jupyter-lab.exe --generate-config, I am getting asked, if I want to override the existing configuration file.
I have no intention to do so, but would like to be able to configure the different jupyter notebook environments separate from each other.
Is there a possibility to do so?
I am trying to install mplcursors or mpldatacursors in python 3.10.0, and it keeps showing packages not found. I have it installed in a Pycharm virtual environment, and it works over there (Python 3.9).
Does anyone have an idea for the support of these APIs?
Can I copy an installed API into another environment?
I am new to jupyter and right from start I am trying to run it in VSCode and that too with a virtual environment. I hope that is not too much for you guys.
So here are steps I did;
I installed python and vscode
Added Paths in windows and all virtual environment things work fine
For data processing I created new virtual environment 'DataProcessingVenv'
I opened this venv in Terminal in VSCode and installed Pandas
I did pip install Jupyter
I did pip install ipykernel
next did ipython kernel install --user --name=DataProcessingVenv
In VSCode I created new notebook
pressed Ctrl+Shift+P and selected interpreter as /DataProcessingVenv/Scripts/Python.exe
Now I am trying to read a csv present in same fodler where notebook is present but somehow it is not working. I really don't know what is wrong and where, even don't know if I have provided all required info to solve the issue. Please guide me it some more info is required or if I can do something to solve the issue. I am attaching the current image in VSCode with error at the end.
So at the end the solution was simple. But it took time to learn. I just had to run all previous cells in Jupyter to make the current cell work.
Idea is to build the distroless docker image and available python3 google distorless image version is 3.7 - gcr.io/distroless/python3. Our code is already compiled and running with python3.5 version and required to upgrade the version into 3.7 so that we can get rid of the library, compactability issues and can make use of the distroless image with verison 3.7. Some questions are,
Will version upgrade cause any issues to the existing code compilation?
Do we need to change all the requirements.txt versions according to the 3.7?
If yes, will there is an impact of the application?
the Python language does not provide backward compatibility. My recommendation for you is to run your code on a virtual env with the new version of Python and test your code. If you do not want to use virtual env you could create a new docker image with the Python version and test your app. Regarding the requirements.txt, without seeing the libraries or packages there, it is impossible to say if you should change the file.
I am trying to run using pygal libray to show graph in AWS lambda.But this error is shown, even I have already installed lxml.deployment_package
my_source_code
import_error
It's because lxml contains binary pre-compiled libraries that it uses. When you install lxml locally on your Windows machine, you install a Windows-compatible version of it. However this is not compatible with the Lambda execution environment which is Linux based.
So you have to create a Lambda compatible deployment package. You have couple of options doing so. You can use sam build --use-container, you can build the libraries in a Docker environment and then zip those, etc. See this answer as well.