Introduction
Exciting news! The Deepseek R1 model is now available in Azure AI Foundry, a powerful platform that simplifies the process of deploying, fine-tuning, and managing AI models at scale. This means you can seamlessly integrate Deepseek R1 into your applications with built-in tools for optimization and monitoring.
In this guide, we’ll walk you through the steps to run the Deepseek R1 model on Azure AI Foundry, covering deployment, fine-tuning, and integration.
Prerequisites
Before you start, make sure you have the following set up:
- Azure Account: Sign up for a Microsoft Azure account if you don’t already have one.
- Access to Azure AI Foundry: Ensure your Azure subscription has access to the Azure AI Foundry service.
- Python 3.7+: Required for interacting with Azure AI Foundry and running the model.
- Azure CLI: Install the Azure CLI for managing Azure resources.
- Git: To clone the Deepseek R1 repository (if needed).
Step 1: Set Up Azure AI Foundry
1. Log in to Azure Portal
- Go to the Azure Portal and log in with your credentials.
2. Create an Azure AI Foundry Workspace
- Navigate to AI Foundry in the Azure Portal.
- Click on Create to set up a new workspace.
- Fill in the required details:
- Subscription: Choose your subscription.
- Resource Group: Create a new one or use an existing group.
- Workspace Name: e.g.,
deepseek-r1-workspace
. - Region: Choose a region close to you.
- Click Review + Create, then Create (this process may take a few minutes).
3. Set Up Compute Resources
- Once the workspace is created, navigate to the Compute section.
- Create a new compute instance (e.g., NC-series GPU-enabled VM) for running the Deepseek R1 model.
Step 2: Access the Deepseek R1 Model in Azure AI Foundry
1. Navigate to the Model Catalog
- In your Azure AI Foundry workspace, go to the Model Catalog.
- Search for the Deepseek R1 model.
2. Deploy the Model
- Select the Deepseek R1 model and click Deploy.
- Choose the deployment type (e.g., real-time endpoint or batch processing).
- Configure the deployment settings, such as compute resources and scaling options.
3. Test the Deployment
- Once the model is deployed, you’ll receive an endpoint URL.
- Use the endpoint to send requests to the model for inference.
Step 3: Fine-Tune the Deepseek R1 Model (Optional)
Fine-tuning Deepseek R1 allows you to adapt the model for tasks like text generation, sentiment analysis, or domain-specific predictions.
1. Prepare Your Dataset
- Upload your dataset to Azure Blob Storage or another supported data source.
2. Create a Fine-Tuning Job
- In the Azure AI Foundry workspace, navigate to Jobs.
- Create a new fine-tuning job for the Deepseek R1 model.
- Specify the dataset, hyperparameters, and compute resources.
3. Monitor the Job
- Track the progress of the fine-tuning job in the Azure AI Foundry dashboard.
- Once completed, the fine-tuned model will be available for deployment.
Step 4: Integrate the Model into Your Application
1. Use the Model Endpoint
After deployment, you can integrate the Deepseek R1 model into your application using the provided endpoint.
Example Python code for sending a request:
import requests
endpoint = "YOUR_MODEL_ENDPOINT_URL"
api_key = "YOUR_API_KEY"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
data = {
"input": "Your input data here"
}
response = requests.post(endpoint, headers=headers, json=data)
print(response.json())
Note: Make sure to replace "YOUR_MODEL_ENDPOINT_URL"
and "YOUR_API_KEY"
with the actual values from your Azure AI Foundry deployment.
2. Monitor Model Performance
- Use Azure AI Foundry’s monitoring tools to track model performance, latency, and usage.
Step 5: Optimize and Scale
1. Scale the Deployment
- Adjust the compute resources and scaling settings based on your application’s needs.
- Azure AI Foundry supports automatic scaling for real-time endpoints.
2. Optimize Costs
- Use Azure Cost Management to monitor and optimize the costs of running the Deepseek R1 model.
Conclusion
Now that you have everything set up, go ahead and test Deepseek R1 in your projects! Whether you’re working on real-time AI applications or large-scale batch processing, Azure AI Foundry gives you the flexibility and power to deploy AI seamlessly.
With Deepseek R1 now available in Azure AI Foundry, deploying and managing advanced AI models has never been easier. By following this guide, you can set up, fine-tune, and integrate the model into your applications using Azure’s powerful cloud infrastructure.
I hope you guys enjoyed the article and found it helpful. Please leave your feedback in the comment section. Thanks.,
Note: For the latest updates and detailed documentation, refer to the official Azure AI Foundry documentation.
P.S. Modern AI tool has been used for creating some of the content. Technical validation and proofing are done by the author.