Quick Start for AI Practitioners

This guide is for AI practitioners responsible for running experiments and production workloads on NVIDIA Run:ai.

The quick start walks through the essential steps to begin using the platform, from initial access and project selection to launching a workspace and submitting your first workloads. The focus is on day-to-day workload execution and resource consumption, so you can experiment, train models, and deploy inference within your assigned project.

Prerequisites

To begin, ensure you meet the following conditions set up by your platform administrator:

• You have an active user account and credentials to access the NVIDIA Run:ai UI

• You are assigned to at least one project

• Your project has available resources to run workloads

Getting Started

Choose a quick start based on your goal. Each scenario walks through a practical example so you can validate access, confirm resource availability, and understand how workloads run in your environment.

• Run your first workspace - Launch a Jupyter notebook workspace for interactive development and experimentation. A guided tour is also available in the UI to help you familiarize yourself with the workspace experience.

• Run a standard training workload - Submit a standard training job to run a model training script on a single GPU.

• Run a distributed training workload - Submit a distributed PyTorch training job and launch a multi-node training workload using an example PyTorch image.

• Run a custom inference workload - Submit an inference workload and query the inference server to verify it is serving requests correctly.

Understand Workload Capabilities

After completing the quick starts, explore the broader workload capabilities available in NVIDIA Run:ai. This helps you move beyond basic scenarios and take advantage of advanced scheduling, scaling, and configuration options.

• Introduction to workloads - How workloads are defined, scheduled, and executed in NVIDIA Run:ai.

• Workload types and features - The different supported workload types and the capabilities available for each, including scaling, resource configuration, scheduling behavior, and other advanced options.

• Workload assets - Shared resources used by workloads, such as environments, data sources, and credentials.

• Workload templates - Reusable configurations that help standardize and simplify workload creation.

Run Workloads for Your Use Case

Once you understand the supported workload types and configuration options, proceed to the workload-specific documentation to configure and run workloads tailored for your project. Each workload section includes complete configuration examples and step-by-step instructions for the UI, API, and CLI.

• Workspace - Interactive development environment for building and testing. Recommended for lightweight experimentation and debugging.

• Training - Workload for standard or distributed training models. Recommended for resource-intensive model development.

• Inference - Deployment of an AI model for serving via an API. Recommended for production use.

• Via YAML - Submission of a range of supported workload types using a standard Kubernetes YAML.

Tutorials for End-to-End Workflows

Full end-to-end tutorials are available for deeper learning. These guides provide complete, practical examples that walk through development, training, and deployment workflows, showing how NVIDIA Run:ai features work together in real-world scenarios. See Tutorials for more details.