Now that visual studio code supports dev environment inside a container is it possible to work on multiple platform with the same machine.

And with a few parameter added you can use your GPU thanks to the Nvidia Container Toolkit !

You must have installed:

  • nvidia driver installed
  • docker installed
  • nvidia container
  • visual studio code

First install the GPU driver (proprietary, tested)

I wanted to try the upcoming version of tensorflow 2.5
But the challenge is to try it on my wsl2 machine on top on windows.

Here is the recipe !
This Nvidia documentation is to follow along, i just updated the parts for my specific install:

Unfortunately this is only available with windows insider

I recommend having a safe windows environnement to play with, i don’t recommend using this in a work or personal computer for instance, as it can be broken sometimes.

Use the DEV channel.
After installing and done a few updates, you should be on a version 20145+

Recently the Tensorflow team released the new version 2 available in alpha.

I had some earlier version of tensorflow on my local machine, but I didn’t remember the version of Nvidia driver / CUDA / CUDnn i used.

So I decided to create a fresh Ubuntu 18.04 and reinstall cuda with a newer version. That was a bad idea.

I followed many tutorials but could not make it work. The different framework needed to make tensorflow works have versions that are pretty important. If you don’t check this and use the latest version it probably won’t work.

After many attempts…

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