Creating a custom Jupyter Lab GPU enabled tensorflow image

This page descirbes the steps needed to produce a ##tensorman## image that has all the elements of an enviornment that uses the GPU and also has a suitable ##conda## (% class="mark" %)Jupyter(%%) enviroment.

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= Steps =

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{{{sudo tensorman run -p 8889:8889 --root --gpu --python3 --jupyter --name jan bash

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}}}

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The port specified should be appropriately mapped to the outside using the nginxproxymanager. This specific port 8889 is assigned to user jan. Eventually the container will be visible (acessible) from [[https:~~/~~/jupyter-jan.informeer.de>>https://jupyter-jan.informeer.de]]

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Inside the running instance do the following:

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{{{apt update

apt install git

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We need (want) to have git available later because we want to install ##git## support in ##jupyter-lab## hence git itself also needs to be available.

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Apart from plain ##jupyter## notebook we also want (actually prefer) ##jupyter lab##. The ##tensorflow## image has ##jupyter## installed by means of ##pip## hence we adopt the same procedure. After all this is a separate environment inside a ##docker## image. We can still use ##conda## for our actual working environments and packages. This is because the ##cwd## is mounted under the ##/project## volume.

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{{{pip install jupyterlab jupyterlab-git

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Then from another terminal/console this:

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{{{tensorman save jan jupyter-lab-gpu

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}}}

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The first ##jan## is the container name and the second ##jupyter-gpu## is the image name (dfferent names can be chosen of course). This saves the container and makes the changes (installing ##git##) persistent. Next we start the container as a regular user (has to be in ##docker## group though). The ##~-~-name $USER## part is added to be able to identify it when multiples (different) instances are present.

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When starting ##tensorman## it will match the current working directory to the ##tensorman (docker)## /##project## volume. If you choose to stare in ##$HOME## this may cause interference with local configurations i.e. outside of ##tensorman (docker)##. To prevent this it is advised to start in a subdirectory of ##$HOME## i.e. ##$HOME/project## (although it can be any name). All subsequent actions from within the ##tensorman## ##docker## instance will use this as its ##$HOME##. This means you can make a fresh ##conda## install without affecting the one in the hosts ##$HOME##.

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{{{mkdir $HOME/project

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cd $HOME/project

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tensorman =jupyter-lab-gpu run -p 8889:8889 --gpu --python3 --jupyter --name $USER bash

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}}}