Running jupyter notebook --port 8888 --ip ''

I am trying to use fbprophet in jupyter notebook in a program for making predictions.

To use it requires python=3.61 which means that I must make a virtual environment
since my base environment uses python 3.8.

I make the virtual environment with no problem. I then active keras-env and that works.

This is all on the command line, new -terminal.

So far everything is fine, but I must make some additional changes or the when I go back to the jupyter notebook
IDE the changes will not be recognized. The IDE has to have these commands.

conda install ipkernel
python -m ipykernel install --user --name=kerasenv.

Now I assume these last two commands above must be run in an active keras-env environment. Nowhere does it say so, but I am just assuming that it must be true.

However, when I leave the command line and go back to the jupyter notebook IDE, it does now have keras-env in the upper right hand corner. So I am assuming I did it right.

I did not!

If I use the drop down down menu and select keras-env it does say that at the upper right hand corner.

However, when I go the first jupyter notebook IDE space and type in:

!python -V

and then execute that one command, I get python 3.85, not 3.61. As I said I want the version of 3.61 because when using keras that is the only python version that works with it a far as I know.

Everything seems, but I am still in the base environment, not the virtual environment.

What am I doing wrong? I believe that I am taking the right steps, but the result is that when in the jupyter notebook IDE, I am still using python 3.85 and not python 3.61. That means I never entered the virtual environment as far as the IDE is concerned. That is what I want to use the keras-env, not my base environment.

My base environment cannot run keras because it has python 3.85.

I have included a series of screenshots that go through the steps that I discussed above.

The only thing that I can think of as being wrong is that my jupyter notebook is being run in Docker container and maybe there is something wrong there.

How I have tried many different Docker containers and it happens in all of them. I have done routine many times before, usually separated by a few months. I must relearn each time.

Any Help appreciated. Thanks in advance.