> For the complete documentation index, see [llms.txt](https://run-ai-docs.nvidia.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/quick-starts/ai-practitioner-quick-start.md).

# 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](/self-hosted/2.22/workloads-in-nvidia-run-ai/using-workspaces/quick-starts/jupyter-quickstart.md) - Launch a Jupyter notebook workspace for interactive development and experimentation.
* [Run a standard training workload](/self-hosted/2.22/workloads-in-nvidia-run-ai/using-training/standard-training/quick-starts/standard-training-quickstart.md) - Submit a standard training job to run a model training script on a single GPU.
* [Run a distributed training workload](/self-hosted/2.22/workloads-in-nvidia-run-ai/using-training/distributed-training/quick-starts/distributed-training-quickstart.md) - Submit a distributed PyTorch training job and launch a multi-node training workload using an example PyTorch image.
* [Run a custom inference workload](/self-hosted/2.22/workloads-in-nvidia-run-ai/using-inference/quick-starts/inference-quickstart.md) - 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](/self-hosted/2.22/workloads-in-nvidia-run-ai/introduction-to-workloads.md) - How workloads are defined, scheduled, and executed in NVIDIA Run:ai.
* [Workload types and features](/self-hosted/2.22/workloads-in-nvidia-run-ai/workload-types.md) - The different supported workload types and the capabilities available for each, including scaling, resource configuration, scheduling behavior, and other advanced options.
* [Workload assets](/self-hosted/2.22/workloads-in-nvidia-run-ai/assets.md) - Shared resources used by workloads, such as environments, data sources, and credentials.
* [Workload templates](/self-hosted/2.22/workloads-in-nvidia-run-ai/workload-templates.md) - 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](/self-hosted/2.22/workloads-in-nvidia-run-ai/using-workspaces/running-workspace.md) - Interactive development environment for building and testing. Recommended for lightweight experimentation and debugging.
* [Training](/self-hosted/2.22/workloads-in-nvidia-run-ai/using-training/standard-training/train-models.md) - Workload for standard or distributed training models. Recommended for resource-intensive model development.
* [Inference](/self-hosted/2.22/workloads-in-nvidia-run-ai/using-inference/custom-inference.md) - Deployment of an AI model for serving via an API. Recommended for production use.


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