tornado | Ruby DSL for easily launching

I encontered the same issue some time ago with Jupyterlab when working locally on my machine with virtual environments.

This is a problem with Jedi being too slow (or rather taking forever) to load the completion, what worked for me was to add the follwing line at the top of the notebook (for example where you typically do the imports):

Jupyter Notebook (notebook module) contains both:

• the server for notebooks (the backend of the web application that hosts the notebook contents, proxies interaction with kernels, and interacts with the operating system by e.g. opening an internet browser on start; this part is generally written in Python), and
• the client (the frontend of the web application, e.g. HTML code, javascript, and some extra REST API points on the server).

However, because there are now multiple clients (frontends) providing different web applications for notebooks:

• Jupyter Notebook
• JupyterLab
• RetroLab
• ntreact
• multiple proprietary clients developed outside of project Jupyter

It made sense to split the server component used by all of these so that e.g. JupyterLab does not have to depend on notebook. This also means that if a fix to the server component is needed, it can be released quickly independent of Jupyter Notebook release cycle (and users of all frontents can benefit immediately).

As a consequence, and to make the break up clean, the old Jupyter Notebook was split into:

• jupyter-server - the server which was adapted by JuptyterLab, RetroLab, ntreact
• nbclassic - the Jupyter Notebook as a jupyter-server extension

This implies changes for users and developers, some already described in "migrate from notebook" docs:

• the options specific to server rather than notebook were renamed from c.NotebookApp to c.ServerApp (the options specific to notebook remain c.NotebookApp)
• the server-specific configuration is now stored in jupyter_server_config.py rather than jupyter_notebook_config.py (same for .json version)
• users should now use jupyter server extension rather than jupyter serverextension (note the extra space!) to list, enable or disable extensions
• the server extensions need to place their files in a new location: etc/jupyter/jupyter_server_config.d rather than etc/jupyter/jupyter_notebook_config.d (in practice most extensions that were updated to support jupyter server are now placing files in both locations for backward compatibility with notebook, but this will change in the future)

It is important to note that depending on how you start your jupyter notebook, you will see different servers being used:

• jupyter nbclassic (assuming nbclassic is installed) will use the new jupyter-server
• jupyter notebook will use the old notebook server
• jupyter lab will use new jupyter-server starting with JupyterLab 3.0 unless running on JupyterHub/Binder where it might still be using old notebook server, depending on configuration

This also implies that you may see different extensions when running jupyter notebook vs jupyter nbclassic (depending on whether their developers updated the locations, and whether they decided they want to support the legacy notebook server).

The creation of nbclassic replacement rather than removal of the server code from existing notebook package was meant to ensure backward compatibility, and this is why you still have two copies of the Tornado server (one provided by jupyter notebook and one by jupyter server). To make the situation simpler you could remove notebook and install nblcassic, but given that the transition is in progress you may need to adjust a few things manually. However, this is only a temporary situation, as it is planned that Notebook will be migrated to use jupyter server starting with v7.0.

This might look inconvenient for now, but this step ensures greater maintainability of the core Jupyter infrastructure in the future and will benefit users and system admins greatly later on.

A workaround for my problem is to use opencv-python-headless headless instead.

Instead of operating on ij array, and convert its values to indices in source arrays, use dask to operate on the actual source arrays. It will be substantially faster.

I created source arrays as:

1. The source (Numpy) array to create x:

cd to your cert folder, and type this command:

I had the same problem, so I uninstalled Python and reinstalled. However, the issue with pip continued.

So, I came up with an additional solution:

1. Uninstall Python
2. Go to the folder that had the python installation. You will see that files remain in the folder; delete all the files in that folder.
3. Reinstall Python
4. Check if your environment variable was set

You can't use asyncio with Tornado prior to version 5.0.

Use Tornado's own ioloop to run your program:

It looks like you stopped following the pattern that you applied in the beginning. As you'll see, prior to line 310, you have used

Sanic is not multi-threaded. And, forking is only used to setup the workers. If you instantiate motor using one of the server life cycle listeners, you will have no problem.

https://sanicframework.org/en/guide/basics/listeners.html