NVIDIA AI Workbench Topology running on Windows, WSL and Linux with an example

NVIDIA AI workbench lets you create and manage ML training environments and do development locally and over the network. Development is done via Jupyter notebooks running in customized containers that can have CUDA or Tensor support if the target host has NVIDIA hardware. AI Workbench builds a custom docker image for each project and manages that container when you want to start or stop work. NVIDIA Workbench leverages the Microsoft WSL making Windows and Linux virtually identical.
Related Videos
* NVIDIA AI Workbench Topology on Windows and Linux a first local project • NVIDIA AI Workbench To...
* NVIDIA AI Workbench running a project on Windows in WSL and exploring the workbench WSL file system • NVIDIA AI Workbench ru...
* NVIDIA AI Workbench running a project on a remote server and ssh into the machine to see the files • NVIDIA AI Workbench cl...
Git repository used github.com/fre...
Blog: joe.blog.freem...
I DO NOT see Workbench as an appropriate tool for casual users at the time (2024/05) of this video primarily because
1. An existing project can only be made Workbench compatible by stuffing some files into your repository
2. The GIT interface gives you no control over what changes are committed