Release Highlights
This section will detail the major new capabilities and user-requested updates in the latest release.
Major New Capabilities
- Enhance collaboration through expanded Git support, such as branching, merging, diffs, and finer-grained control for commits and gitignore.
- Create complex applications and workflows with multicontainer environments through Docker Compose support.
- Simple, fast, and secure rapid prototyping with application sharing with single-user URLs.
User Requested Updates
- Dark mode for the Desktop App
- Improved installation on localized versions of Windows
Expanded Git Support
Previously, AI Workbench supported only single, monolithic commits on the main branch. Users had to manage branches and merges manually, and this created various types of confusion, especially around resolving merge conflicts. Now, users can manage branches, merges, and conflicts directly in the Desktop App and the CLI. In addition, they can see and triage individual file diffs for commits. The UI is built to work seamlessly with manual Git operations and will update to reflect relevant changes.
Multicontainer Support with Docker Compose Stacks
AI Workbench now supports Docker Compose. Users can work with multicontainer applications and workflows with the same ease of configuration, reproducibility, and portability that AI Workbench provides for single-container environments.
Dark Mode and Localized Windows Installation
Many users requested a dark mode option because it’s easier on the eyes. It’s now available and can be selected through the Settings window that is now available directly from within the Desktop App. Learn more about how dark mode works.
New AI Workbench Projects
This release introduces new example projects designed to jumpstart your AI development journey, detailed below.
Multimodal Virtual Assistant Example Project
This project enables users to build their own virtual assistant using a multimodal retrieval-augmented generation (RAG) pipeline with fallback to web search. Users can interact with two RAG-based applications to learn more about AI Workbench, converse with the user documentation, troubleshoot their own installation, or even focus the RAG pipeline to their own, custom product.
Competition-Kernel Example Project
This project provides an easy, local experience when working on Kaggle competitions. You can easily leverage your local machine or a cloud instance to work on competition datasets, write code, build out models, and submit results, all through AI Workbench.
Get Started
To get started with AI Workbench, install the application from the webpage. For more information about installing and updating, see the NVIDIA AI Workbench documentation.
Conclusion
This release of NVIDIA AI Workbench marks a significant step forward in providing a frictionless experience for AI development across GPU systems. New features from this release, including expanded Git support, support for multicontainer environments, and secure web app sharing, streamline developing and collaborating on AI workloads.
Frequently Asked Questions
Q: What are the major new capabilities in this release?
A: The major new capabilities include enhanced Git support, multicontainer support with Docker Compose, and simple, fast, and secure rapid prototyping with application sharing.
Q: What are the user-requested updates in this release?
A: The user-requested updates include dark mode for the Desktop App and improved installation on localized versions of Windows.
Q: How do I get started with AI Workbench?
A: To get started with AI Workbench, install the application from the webpage and follow the documentation for installing and updating.
Q: What are the new example projects available with this release?
A: The new example projects include the Multimodal Virtual Assistant Example Project and the Competition-Kernel Example Project.
