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onboarding

  • Set Up Local Network Access
  • Open WebUI with Ollama

data-science

  • Optimized JAX
  • Text to Knowledge Graph

tools

  • Comfy UI
  • DGX Dashboard
  • VS Code
  • RAG application in AI Workbench
  • Set up Tailscale on your Spark

fine-tuning

  • FLUX.1 Dreambooth LoRA Fine-tuning
  • LLaMA Factory
  • Fine-tune with NeMo
  • Fine tune with Pytorch
  • Unsloth on DGX Spark
  • Vision-Language Model Fine-tuning

use-case

  • Build and Deploy a Multi-Agent Chatbot
  • NCCL for Two Sparks
  • Connect Two Sparks
  • Video Search and Summarization

inference

  • Multi-modal Inference
  • NIM on Spark
  • NVFP4 Quantization
  • Speculative Decoding
  • TRT LLM for Inference
  • Install and Use vLLM for Inference

DGX Dashboard

30 MIN

Monitor your DGX system and launch JupyterLab

View on GitHub

Basic idea

The DGX Dashboard is a web application that runs locally on DGX Spark devices, providing a graphical interface for system updates, resource monitoring and an integrated JupyterLab environment. Users can access the dashboard locally from the app launcher or remotely through NVIDIA Sync or SSH tunneling. The dashboard is the easiest way to update system packages and firmware when working remotely.

What you'll accomplish

You will learn how to access and use the DGX Dashboard on your DGX Spark device. By the end of this walkthrough, you will be able to launch JupyterLab instances with pre-configured Python environments, monitor GPU performance, manage system updates and run a sample AI workload using Stable Diffusion. You'll understand multiple access methods including desktop shortcuts, NVIDIA Sync and manual SSH tunneling.

What to know before starting

  • Basic terminal usage for SSH connections and port forwarding
  • Understanding of Python environments and Jupyter notebooks

Prerequisites

  • DGX Spark device with Ubuntu Desktop environment
  • NVIDIA Sync installed (for remote access method) or SSH client configured

Ancillary files

  • Python code snippet for SDXL found here on GitHub

Time & risk

  • Duration: 15-30 minutes for complete walkthrough including sample AI workload
  • Risk level: Low - Web interface operations with minimal system impact
  • Rollback: Stop JupyterLab instances through dashboard interface; no permanent system changes made during normal usage.

Resources

  • DGX Spark Documentation
  • DGX Spark Forum