Monitor Your AI Compute with DGX Dashboard

Access DGX Dashboard

Choose one of the following methods to open the DGX Dashboard web interface.

Option A: Desktop shortcut (local access)

If you have local access to your hardware platform:

1. Log into the desktop environment on your hardware platform
2. Open the app launcher (for example, from the bottom-left corner of the screen)
3. Click the DGX Dashboard shortcut
4. The dashboard opens in your default web browser at http://localhost:11000

Option B: NVIDIA Sync (recommended for remote access)

If you have NVIDIA Sync installed on your local machine:

1. Click the NVIDIA Sync icon in your system tray
2. Select your hardware platform from the device list
3. Click Connect
4. Click DGX Dashboard to launch the dashboard
5. The dashboard opens in your default web browser at http://localhost:11000 using an automatic SSH tunnel

Option C: Manual SSH tunnels

For remote access without NVIDIA Sync, configure an SSH tunnel to the hardware platform.

Open a tunnel for the Dashboard server (port 11000) and for JupyterLab if you want remote notebook access. Each user account has a different assigned JupyterLab port.

1. Check your assigned JupyterLab port by SSH-ing into your hardware platform and running:

cat /opt/nvidia/dgx-dashboard-service/jupyterlab_ports.yaml

2. Look for your username and note the assigned port number.
3. Create an SSH tunnel that includes both ports:

ssh -L 11000:localhost:11000 -L <ASSIGNED_PORT>:localhost:<ASSIGNED_PORT> <USERNAME>@<HARDWARE_IP>

Replace <USERNAME> with your hardware platform username and <HARDWARE_IP> with the hardware platform's reachable IP address. Replace <ASSIGNED_PORT> with the port number from the YAML file.

Open your web browser and navigate to http://localhost:11000.

Log into DGX Dashboard

Once the dashboard loads in your browser:

1. Enter your hardware platform system username
2. Enter your system password
3. Click Login

You should see the main dashboard with panels for JupyterLab management, system monitoring, and settings.

Launch JupyterLab

Create and start a JupyterLab environment:

1. Click the Start button in the right panel
2. Monitor the status as it transitions through: Starting → Preparing → Running
3. Wait for the status to show Running (first launch may take several minutes)
4. If JupyterLab does not open automatically (for example, a pop-up was blocked), click Open In Browser

When starting, a default working directory (/home/<USERNAME>/jupyterlab) is created and a virtual environment is set up automatically. Review installed packages in the requirements.txt file created in that working directory.

To use a different working directory later, click Stop, change the path, then click Start again to create a new isolated environment.

Test with a sample AI workload

Verify your setup by running a Stable Diffusion XL image generation example:

1. In JupyterLab, create a new notebook: File → New → Notebook
2. Select Python 3 (ipykernel)
3. Add a new cell and paste the following code (the same cell is provided in assets/jupyter-cell.py):

import warnings
warnings.filterwarnings('ignore', message='.*cuda capability.*')
import tqdm.auto
tqdm.auto.tqdm = tqdm.std.tqdm

from diffusers import DiffusionPipeline
import torch
from PIL import Image
from IPython.display import display

# --- Model setup ---
MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
dtype = torch.float16 if torch.cuda.is_available() else torch.float32

pipe = DiffusionPipeline.from_pretrained(
    MODEL_ID,
    torch_dtype=dtype,
    variant="fp16" if dtype == torch.float16 else None,
)
pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")

# --- Prompt setup ---
prompt = "a cozy modern reading nook with a big window, soft natural light, photorealistic"
negative_prompt = "low quality, blurry, distorted, text, watermark"

# --- Generation settings ---
height = 1024
width = 1024
steps = 30
guidance = 7.0

# --- Generate ---
result = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=steps,
    guidance_scale=guidance,
    height=height,
    width=width,
)

# --- Save to file ---
image: Image.Image = result.images[0]
display(image)
image.save("sdxl_output.png")
print("Saved image as sdxl_output.png")

4. Run the cell (Shift+Enter or the Run button)
5. The notebook downloads the model and generates an image (first run may take several minutes)

Monitor GPU utilization

While the image generation is running:

1. Switch back to the DGX Dashboard tab in your browser
2. Observe the GPU telemetry data in the monitoring panels

Stop JupyterLab

When finished with your session:

1. Return to the main DGX Dashboard tab
2. Click Stop in the JupyterLab panel
3. Confirm the status changes from Running to Stopped

Manage system updates

If updates are available, a banner appears or the Settings page indicates them.

From the Settings page, under the Updates tab:

1. Click Update to open the confirmation dialog
2. Click Update Now to start the update
3. Wait for the update to complete and the hardware platform to reboot

Cleanup

To clean up resources after this walkthrough:

1. Stop any running JupyterLab instances via the dashboard
2. Delete the JupyterLab working directory if you no longer need it

No permanent changes are made to the system during normal dashboard usage.

Next steps

With DGX Dashboard configured, you can:

• Create additional JupyterLab environments for different projects
• Use the dashboard for ongoing system maintenance and updates
• Explore other playbooks that build on local notebooks and GPU-backed workloads