Deploying Hugging Face Generative AI Services on DigitalOcean GPU Droplet and Integrating with Open WebUI

Introduction

Hugging Face’s Generative AI Services (HUGS) makes deploying and managing LLMs easier and faster. Now, with DigitalOcean’s 1-Click deployment for HUGS on GPU Droplets, you can set up, scale, and optimize LLMs on a cloud infrastructure tailored for high performance. This guide walks you through deploying HUGS on a DigitalOcean GPU Droplet and integrating it with Open WebUI. It also explains why this setup is ideal for seamless, scalable LLM inference.

Prerequisites

• A DigitalOcean Cloud account.
• A GPU Droplet deployed and running, and another Droplet up and running to deploy and run the Open WebUI docker container.
• Familiarity with SSH and basic Docker commands.
• An SSH key for logging into your Droplet.

Step 1 - Create and Access Your GPU Droplet

1. Set up the Droplet:
   Go to DigitalOcean’s Droplets page and create a new GPU Droplet. Under the Choose an Image tab, please select 1-Click Models and use one of the available Hugging Face images.

2. Access the Console:
   Once your Droplet is ready, click on its name in the Droplets section and select Launch Web Console.

3. Please note the Message of the Day (MOTD): This contains the bearer token and inference endpoint for API access, which you’ll need later.

   

Step 2 - Start Hugging Face HUGS

Hugging Face HUGS will automatically start after the Droplet setup. To verify, check the status of the Caddy service managing the inference API:

sudo systemctl status caddy

[secondary_label Output
● caddy.service - Caddy
     Loaded: loaded (/lib/systemd/system/caddy.service; enabled; vendor preset: enabled)
    Drop-In: /etc/systemd/system/caddy.service.d
             └─override.conf
     Active: active (running) since Wed 2024-10-30 10:27:10 UTC; 2min 58s ago
       Docs: https://caddyserver.com/docs/
   Main PID: 8239 (caddy)
      Tasks: 17 (limit: 629145)
     Memory: 48.8M
        CPU: 73ms
     CGroup: /system.slice/caddy.service
             └─8239 /usr/bin/caddy run --config /etc/caddy/Caddyfile

Allow 5-10 minutes for the model to fully load.

Step 3 - Start Open WebUI

Launch Open WebUI using Docker on another Droplet. Please use the below docker command to run the Open WebUI docker container.

docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main

Once Open WebUI runs, access it at http://<your_droplet_ip>:3000.

Step 4 - Integrate HUGS with Open WebUI

To connect Open WebUI with Hugging Face HUGS:

1. Open Settings:

   • In Open WebUI, click your user icon at the bottom left, then click Settings.

2. Go to Admin:

   • Navigate to the Admin tab, then select Connections.

   

3. Set the Inference Endpoint:

   • In the API link field, enter your Droplet’s IP followed by /v1. If a specific port is required, include it, e.g., http://<your_droplet_ip>/v1.
   • Use the API token from the MOTD for authentication.

4. Verify Connection:

   • Click Verify Connection. A green light confirms a successful connection. Open WebUI will then auto-detect available models, such as hfhgus/Meta-Llama.

   

Step 5: Start Chatting with the Model

With HUGS integrated into Open WebUI, you’re ready to interact with your LLM:

• Ask questions like “What is DigitalOcean?”

• Monitor requests logs from the container while asking a follow-up question: Does DigitalOcean offer object storage?:

 sudo docker ps
 sudo docker logs <your-container-ID> -f

Why Choose HUGS on DigitalOcean GPU Droplets?

1. Ease of Deployment and Simplified Management
   Deploying HUGS with DigitalOcean’s one-click setup is straightforward. No need for manual configurations—DigitalOcean and Hugging Face handle the backend, allowing you to focus on scaling.

2. Optimized Performance for Large-Scale Inference HUGS on DigitalOcean GPUs ensures optimal performance, running LLMs efficiently on GPU hardware without manual tuning.

3. Scalability and Flexibility DigitalOcean’s infrastructure supports scalable deployments with load balancers for high availability, letting you serve users globally with low latency.

By using Hugging Face HUGS on DigitalOcean GPU Droplets, you not only benefit from high-performance LLM inference but also gain the flexibility to scale and manage the deployment effortlessly. This combination of optimized hardware, scalability, and simplicity makes DigitalOcean an excellent choice for production-level AI workloads.

Conclusion

With HUGS deployed on DigitalOcean’s GPU Droplet and Open WebUI, you can efficiently manage, scale, and optimize LLM inference. This setup eliminates hardware optimization concerns and provides a ready-to-scale solution for delivering fast, reliable responses across multiple regions.