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403 forbidden when running custom docker container in vs code jupyter notebook

I am following a course on udemy for docker with python. I am supposed to be creating a custom docker image by pulling down the pre-existing jupyter/tensorflow-notebook image which does not have pytorch installed in it, install pytorch and torchvision then run it in a jupyter notebook.
I get a 403 forbidden error when I run it and I can't figure out why. Here are the steps in order:
docker pull jupyter/tensorflow-notebook
docker run -d --name temp jupyter/tensorflow-notebook
docker exec -it temp pip install torch torchvision
docker stop temp
docker commit temp myjupyter:torch
docker run -it -rm -p 8888:8888 -v ${PWD}:/home/joyvan/notebooks myjupyter:torch (my working directory is the folder containing the ipynb file that the course will have me working in once the image is running successfully)
I've seen some other SO posts that say to check docker container ps and that returns
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
(base) justinbenfit#MacBook-Pro-3 manual-build %
so I'm assuming temp images don't show up so I'm not really sure what to do next to find out why the server is saying forbidden. Thanks.

Run docker daemon from pycharm

I've just started to use docker and got an error.
I use Pycharm on macOS. In my project I clone a github repo (that's a simple LogisticRegression from sklearn) that includes dockerfile.
I expected, that all what I needed was
docker build . -t servername
But I got an error
Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?
Where should i run Docker daemon?
Thank you for your help!

You need to install and run Docker first. Here is the Link for Docker Desktop for MacOS.

Run it with sudo so sudo docker build . -t servername
else try sudo systemctl enable docker.service && systemctl enable containerd.service

how to run docker container from python image?

I pulled a python image from hub images doing docker pull python:3.9-alpine.
Then i tried to launch the container from this image doing like docker run -p 8888:8888 --name test_container d4d6be1b90ec.
The container is never up. with docker ps i didn't find it.
Do you know why please?
Thank you,

Your container is not launched because there is no server(nginx,apache etc..) to point, there is only python and the necessary dependencies.
In order to run that image you can try the following command:
docker run --name test_python -it [id_image]
And if you open another terminal and use docker ps you will see that the container is up.

How can I access the project directories that are created in the Docker toolbox Linux containers on my Windows OS?

I have just installed Docker ToolBox on Windows 10 OS and created a Linux Python container. I have cloned a project from GitHub in that container. How can I access the project directories that are created in the Linux containers on my Windows OS? Are they saved in any drive or directory?
I have obtained the docker image container using the command
docker pull floydhub/dl-docker:cpu
and running it using the command
docker run -it -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder floydhub/dl-docker:cpu bash
Also I am running my project using docker quick start terminal, is there any other way (GUI based) to manage and run my projects?

More details about how your Linux Python container was launched, I.E. the build file/ run command, would be helpful. But that being said, you can copy files out of a container using docker cp as described here:
https://docs.docker.com/engine/reference/commandline/cp/
Also, if you use volumes you can view the cloned repo right on your Windows machine directly, but those volumes must be setup during the build process and cannot be added to a running container. More details on volumes and how data persistence works in docker:
https://docs.docker.com/engine/admin/volumes/volumes/
To answer your question about a GUI docker tool, I've used Kitematic (https://kitematic.com/) but personally found the CLI faster and easier to use.
Edit
According to edits you do in fact have volumes that will persist data to your local Windows machine. But I believe you need to use a separate -v flag for each: (docker documentation is unclear about this as they now suggest using --mount, but according to this SO post: Mounting multiple volumes on a docker container? suggests the multiple flags):
docker -v /sharedfolder:/root/sharedfolder \
-v floydhub/dl-docker:cpu
A side note: it is sometimes confusing to not use absolute paths when mounting volumes as you've done in floydhub/dl-docker:cpu. Its not always clear where this data might live.
Any data that is stored in the docker container at /root/sharedfolder should be available in Windows at /sharedfolder
Edit 2
To match your original docker run command:
docker run -it -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder -v floydhub/dl-docker:cpu bash

Docker on Ubuntu 14.04 my browser is not starting after run command for Kaggle/python

In Kaggle/python docker on Ubuntu 14.04 my browser is not starting.Anyone has face this issue and resolution? I am using below command from terminal
"(sleep 3 && sensible-browser "http://127.0.0.1:8888")& docker run -v $PWD:/tmp/working -w=/tmp/working -p 8888:8888 --rm -it kaggle/python jupyter notebook --no-browser --ip=0.0.0.0 --notebook-dir=/tmp/working"
and when I go to Firefox web browser localhost:8888, it ain't show up nothing.

You can use datmo/kaggle:python to be able to run kaggle projects with jupyter notebook. After pulling the image, you can use code like this in the shell:
docker run --rm -it -p 8888:8888 -v ~/.:/home/ datmo/kaggle:python 'jupyter notebook'
This mounts the local directory onto the container having access to it.
Then, go to the browser and hit https://localhost:8888, and when I open a new kernel it's with Python 3.5. I don't recall doing anything special when pulling the image or setting up Docker.
You can find more information from here.
You can also try using datmo in order to easily setup environment and track machine learning projects to make experiments reproducible. You can run datmo task command as follows for setting up jupyter notebook,
datmo task run 'jupyter notebook' --port 8888
It sets up your project and files inside the environment to keep track of your progress.