Deploying Models with Inference API
Last updated July 1, 2024
Introduction: The Hugging Face Inference API allows you to deploy models quickly and efficiently, making them accessible for real-time predictions via API calls.
Steps:
- Setting Up Your Model on Hugging Face Hub Upload Your Model: Ensure your model is uploaded to the Hugging Face Hub. You can follow the Hugging Face documentation for detailed steps on uploading.
- Accessing the Inference API
API Endpoint: Obtain your API endpoint from the Hugging Face model page. It usually looks like
https://api-inference.huggingface.co/models/{username}/{model_name}. - Authenticating Your API Requests
- API Token: Generate an API token from your Hugging Face account settings.
- Include Token in Requests:
import requests API_URL = "https://api-inference.huggingface.co/models/{username}/{model_name}" headers = {"Authorization": f"Bearer {your_api_token}"}
- Making Predictions Send Data for Inference defquery(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() data = {"inputs": "Your input text here"} result = query(data) print(result)
- Handling API Responses
- Interpreting Results: Process the response to extract and use the predicted outputs for your application.
- Example Response Handling:
python
Copy code
print(f"Prediction: {result[0]['label']} with score {result[0]['score']}")
- Monitoring and Scaling
- Monitor Usage: Use the Hugging Face dashboard to monitor your API usage and performance.
- Scaling Options: Consider upgrading your plan or optimizing your model to handle increased load efficiently.
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