What is NVIDIA AI Workbench?
NVIDIA AI Workbench is a developer toolkit for data science, machine learning, and AI project development.
AI Workbench is free, you can install it in minutes on your local or remote computers, and it provides both a desktop application and a command-line interface (CLI). With AI Workbench you can do a variety of tasks, including the following:
Develop on your laptop and then move workloads to scalable GPU resources in a data center or the cloud.
Write and run code in JupyterLab, VS Code, or a text editor.
Collaborate and distribute your work through GitHub and GitLab.
Use NVIDIA’s pre-built containers, or your own fully-custom containers.
Clone fully-functioning NVIDIA-provided examples from GitHub for tasks such as Retrieval-Augmented Generation (RAG), model fine-tuning, and RAPIDS data science workflows.
AI Workbench is supported on the following operating systems:
Windows 11 (build 22000 or later)
Windows 10 (build 19044 or later)
Ubuntu 24.04
Ubuntu 22.04
macOS Monterrey (12) or later
A computer that has AI Workbench installed is called a location. The first place you install AI Workbench is on your local computer, which can be a CPU-only computer. Later you can install AI Workbench on one or more remote computers, such as workstations, servers, cloud instances, and virtual machines. For more information, see AI Workbench Locations.
A Workbench Project contains everything you need to do your work, such as code, data, models, environment configuration, metadata, and work history. Workbench Projects are specially-formatted Git repositories, and AI Workbench uses the format and metadata to provide automation for container and version management. For more information, see Workbench Projects.
The environment for a Workbench Project is a container that AI Workbench builds and runs. NVIDIA provides default containers that you can select from as the starting point for each new project. The NVIDIA-provided default containers include the following:
For the full list of available containers, see NVIDIA NGC Containers.
For more information about how AI Workbench handles containers, see Base Environments and Container Runtimes.
AI Workbench lets you connect to external systems, such as container registries and Git servers, through authentication methods like personal-access-tokens (PAT) and Oauth integrations. AI Workbench stores your credentials securely so you don’t have to log in every time. After you authenticate to an external system, you can access it from any project running in any of your locations, including remote locations. For more information, see AI Workbench Integrations.
Applications are software or user interfaces available to use in, or with, a Workbench Project. For example:
JupyterLab — Each of the NVIDIA-provided base container options has JupyterLab installed by default.
Visual Studio Code — If VS Code is installed on your local computer, you can connect it to your Workbench project.
Gradio Apps — Some Projects have simple web apps like chat apps or RAG apps installed.
For more information, see AI Workbench Applications.