AI/ML
Set Up Qwen 2.5 in Docker with Ollama on Azure VM - Complete Guide
Qwen 2.5 Model for your Business?
Cost Efficiency (Open Source)
Lower Long Term costs
Customised data control
Pre-trained model
Get Your Qwen 2.5 AI Model Running in a Day
Free Installation Guide - Step by Step Instructions Inside!
Introduction
Qwen 2.5 is a robust AI model built for advanced text processing and natural language understanding. Deploying it within a Docker container on an Azure Virtual Machine (VM) provides a streamlined, scalable and isolated execution environment.
Prerequisites
Before proceeding, make sure you have:
- An Azure VM (Ubuntu recommended) with Docker installed.
- SSH access to the instance.
Step 1: Connect to Your Azure VM
Access your VM by executing the following command in your terminal:
ssh -i your-private-key.pem azure-user@your-vm-ipOnce connected, update the package lists:
sudo apt update && sudo apt upgrade -y Step 2: Deploy the Ollama Container
Launch an Ollama container to manage the model execution:
docker run -d --name ollama -v ollama:/root/.ollama -p 11434:11434 ollama/ollamaStep 3: Enter the Ollama Container
After the container is up and running, gain shell access to it:
docker exec -it ollama /bin/bashStep 4: Download the Qwen 2.5 Model
Retrieve the Qwen 2.5 model inside the Ollama container:
ollama pull qwen:2.5
This command ensures all dependencies are properly installed.
Step 5: Start the Model
Start the model with the following command:
ollama run qwen:2.5Test it by entering a prompt:
>>> Describe the history of artificial intelligence.
Step 6: Enable Web-Based Interaction
To interact with Qwen 2.5 through a browser, deploy the Open WebUI tool:
docker run -d -p 3000:8080 -e OLLAMA_BASE_URL=http://<YOUR-VM-IP>:11434 -vopen-webui:/app/backend/data --name open-webui --restart alwaysghcr.io/open-webui/open-webui:main
Now, open http://<YOUR-VM-IP>:3000 in a browser to start using Qwen 2.5 via a web interface.
Conclusion
By running Qwen 2.5 on an Azure Virtual Machine with Docker and Ollama, you achieve an efficient and scalable AI deployment. This approach simplifies model execution while maintaining an isolated and reusable setup for various workloads.
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AI/ML