Data Science

Speed Up Your AI Development: NVIDIA AI Workbench Goes GA

Illustration representing AI Workbench.

NVIDIA AI Workbench, a toolkit for AI and ML developers, is now generally available as a free download. It features automation that removes roadblocks for novice developers and makes experts more productive.

Developers can experience a fast and reliable GPU environment setup and the freedom to work, manage, and collaborate across heterogeneous platforms regardless of skill level. Enterprise support is also available for customers who purchase a license for NVIDIA AI Enterprise.

Key AI Workbench features include: 

  • Fast installation, setup, and configuration for GPU-based development environments.
  • Pre-built, ready-to-go generative AI, and ML example projects based on the latest models. 
  • Deploy generative AI models with cloud endpoints from the NVIDIA API catalog or locally with NVIDIA NIM microservices
  • An intuitive UX plus command line interface (CLI). 
  • Easy reproducibility and portability across development environments. 
  • Automation for Git and container-based developer environments. 
  • Version control and management for containers and Git repositories. 
  • Integrations with GitHub, GitLab, and the NVIDIA NGC catalog.
  • Transparent handling of credentials, secrets, and file system changes. 

 Since its Beta release, AI Workbench also has several new key features: 

  • Visual Studio (VS) Code support: Directly integrated with VS Code to orchestrate containerized projects on GPU environments.
  • Choice of base images: Users can choose their own container image as the project base image when creating projects. The container image must use image labels that follow the base image specifications. 
  • Improved package management: Users can manage and add packages directly to containers through the Workbench user interface. 
  • Installation improvements: Users have an easier install path on Windows and MacOS. There is also improved support for the Docker container runtime. 

Bring generative AI to your NVIDIA RTX 

Generative AI has exploded. AI Workbench can bring generative AI development to any GPU-enabled environment with a unified interface on hundreds of millions of modern NVIDIA RTX-powered workstations and PCs, or across the data center and the cloud. Mac users can install AI Workbench, and migrate projects to NVIDIA-powered systems for collaboration and greater compute power.  

Get started faster 

In addition to fast GPU workstation setup, AI Workbench provides example projects as a ready-to-go starting point to help developers get started even faster on their data and use cases. Workbench Projects pull together all the resources and metadata needed to streamline your workflow management across various infrastructures while facilitating seamless portability, and reproducibility anywhere.

NVIDIA provides a series of free Workbench Project examples to help users get started:  

  • Chat with your documents using hybrid Retrieval Augmented Generation (RAG). Run an embedding model on your system to store documents in a private vector database. Configure inference to run in the cloud with an NVIDIA API or locally with NIM Inference microservices on an RTX system. 
  • Customize LLMs at any scale. From running quantized models locally to full fine-tuning for optimized precision. Fine-tune and run anywhere—locally on an RTX system or scale out to a data center or the cloud. View the Llama-2 or Mistral-7B projects on GitHub. 
  • Generate custom images from text prompts by running Stable Diffusion XL locally on an RTX PC or in the cloud. Easily reproduce on your choice of GPU-enabled environment to fine-tune models with your images.

Visit NVIDIA on GitHub to reference NVIDIA AI Workbench Projects that can get you started with faster results. Some models in the NVIDIA API Catalog have associated Workbench Projects, which enable customizing and fine-tuning them before inference.

A better developer experience 

Many things can impact developer productivity. Let’s look at a few of these things and what AI Workbench does to improve productivity and the development experience. 

Setup and configuration

AI Workbench automates tasks to set up the target GPU system while configuring a GPU-enabled container for your chosen development environment.  It ensures the proper installation of compatible components across the stack, including OS drivers, CUDA drivers, and firmware.

Freedom to work and collaborate anywhere

Seamlessly migrate workloads between different systems and locations without worrying about things breaking. Move to the best platform for collaboration, speed, scale, and cost, whether local, data center, or public cloud; Windows, Linux, or macOS. Use public cloud APIs, local microservices, containers, and popular repositories in a Workbench Project. AI Workbench handles the complexity of portability and reproducibility challenges so developers don’t have to. 

Managed AI and ML workflows

AI Workbench manages development workflows behind the scenes for tasks such as file versioning, location changes, and tracking of project dependencies. This enables both novice and skilled developers to focus on execution without the worry of configuration and management challenges.  

Get AI Workbench

Download NVIDIA AI Workbench for Windows, macOS, and Ubuntu Linux. Using NVIDIA Launchpad, you can also get immediate, short-term access to try AI Workbench example projects.

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