Container Environments for AI Workbench Projects
When you create an AI Workbench project you choose a container environment for the project, and AI Workbench runs the project in its own containerized development environment. Each AI Workbench project can have a different container environment.
NVIDIA provides default environments that you can choose as the starting point for each new AI Workbench project. Each default environment has Python and JupyterLab pre-installed. The Pytorch environment has TensorBoard installed.
The NVIDIA-provided default environments include the following:
For the full list of available containers, see NVIDIA NGC Containers.
If the default container environments for AI Workbench do not meet your needs, you can customize your environment in one of the following ways:
If you want to use one of the pre-built containers and make simple customizations, such as adding packages, see Walkthrough: Customize Your Environment and Environment Configuration.
If you want to change the behavior of the container environment for a single project, see Customize Your Container.
If you want to create a fully-custom container that you can use for you own projects, or that you can publish and share with other AI Workbench users, see Use Your Own Container instead. This is an advanced scenario.
To run additional isolated environments for your project, see Multiple Container Support With Docker Compose.