Add NVIDIA AI Workbench documentation (#497)

Co-authored-by: Naty Clementi <[email protected]>

source/platforms/index.md

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 `````{gridtoctree} 1 2 2 3
 :gutter: 2 2 2 2
 
+````{grid-item-card}
+:link: nvidia-ai-workbench
+:link-type: doc
+NVIDIA AI Workbench
+^^^
+Run RAPIDS in NVIDIA AI Workbench, GPU workstation setup tool that enables developers to work, manage, and collaborate across heterogeneous platforms.
+
+{bdg}`single-node`
+````
+
 ````{grid-item-card}
 :link: kubernetes
 :link-type: doc

source/platforms/nvidia-ai-workbench.md

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+# NVIDIA AI Workbench
+
+[NVIDIA AI Workbench](https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/workbench/) is a developer toolkit for data science, machine learning, and AI projects. It lets you develop on your laptop/workstation and then easily transition workloads to scalable GPU resources in a data center or the cloud. AI Workbench is free, you can install it in minutes on both local or remote computers, and offers a desktop application as well as a command-line interface (CLI).
+
+## Installation
+
+You can install AI Workbench locally, or on a remote computer that you have SSH access to.
+
+Follow the [AI Workbench installation](https://docs.nvidia.com/ai-workbench/user-guide/latest/installation/overview.html) documentation for instructions on installing on different operating systems.
+
+```{admonition} Local GPU System
+:class: note
+
+If you are working on a system that has an NVIDIA GPU you can use the AI Workbench
+by installing the desktop application and configure Docker if you don't have it installed already. Then you can run notebooks in Python environments with access to your GPU.
+
+```
+
+```{admonition} Remote GPU System
+:class: note
+
+If you don't have an NVIDIA GPU in your system, but have remote SSH access to a system that does, then you can use AI Workbench to connect to that system. Your code will be executed on the remote system, but files will be synced between your local and remote environments automatically. This allows you to burst from a system without NVIDIA GPUs like a lightweight laptop to a powerful remote AI system.
+
+To use AI Workbench in this way you need to install the desktop application on your local system and the CLI application on the remote system.
+```
+
+## Configure your system
+
+Once you have installed AI Workbench you can launch the desktop application. On first run it will talk you through installing some dependencies if they aren't available already.
+
+Then you will be able to choose between using your local environment or working on a remote system (you can switch between them later very easily).
+
+If you wish to configure a remote system click the "Add Remote System" button and enter the configuration information for that system.
+
+![Screenshot of adding a new remote location with a form where you can enter SSH information](../_static/images/platforms/nvidia-ai-workbench/add-remote-system-dialog.png)
+
+Once configured select the system you wish to use. You will then be greeted with a screen where you can create a new project or clone an existing one.
+
+![Screenshot of ](../_static/images/platforms/nvidia-ai-workbench/new-project.png)
+
+Select "Start a new project" and give it a name and description. You can also change the default location to store the project files.
+
+![Screenshot of the "Start a new project" button](../_static/images/platforms/nvidia-ai-workbench/create-project.png)
+
+Then scroll down and select "RAPIDS with CUDA" from the list of templates.
+
+![Screenshot of the template selector with "RAPIDS with CUDA" highlighted](../_static/images/platforms/nvidia-ai-workbench/rapids-with-cuda.png)
+
+The new project will then be created. AI Workbench will automatically build a container for this project, this may take a few minutes.
+
+![Screenshot of the AI workbench UI. In the bottom corner the build status says "Building" and the "Open Jupyterlab" button is greyed out](../_static/images/platforms/nvidia-ai-workbench/project-building.png)
+
+Once the project has built you can select "Open Jupyterlab" to launch Jupyter in your RAPIDS environment.
+
+![Screenshot of the AI workbench UI. In the bottom corner the build status says "Build Ready" and the "Open Jupyterlab" button is highlighted](../_static/images/platforms/nvidia-ai-workbench/open-jupyter.png)
+
+Then you can start working with the RAPIDS libraries in your notebooks.
+
+![Screenshot of Jupyterlab running some cudf code to demonstrate that the RAPIDS libraries are available and working](../_static/images/platforms/nvidia-ai-workbench/cudf-example.png)
+
+## Further reading
+
+For more information and to learn more about what you can do with NVIDIA AI Workbench [see the documentation](https://docs.nvidia.com/ai-workbench/user-guide/latest/overview/introduction.html).