This guide provides an overview of how to deploy Hugging Face models. Whether you're a beginner or an experienced developer, you'll find this guide helpful in understanding the deployment process.

Prerequisites

Before you start deploying your model, make sure you have the following prerequisites:

  • A Hugging Face account
  • A trained model stored in a Hugging Face model repository
  • Basic knowledge of Python and machine learning

Deployment Steps

  1. Choose a Deployment Platform

  2. Create a Container Image

    • Use Docker to create a container image with your model and dependencies.
    • Example command: docker build -t my-model-image .

  3. Deploy the Container

    • Deploy the container to your chosen cloud platform.
    • Example command for AWS: eksctl create cluster --name my-cluster --region us-west-2
    • Example command for Google Cloud: gcloud run deploy my-model --image gcr.io/my-project/my-model-image

  4. Test the Deployment

    • Send requests to your deployed model and verify the responses.

Best Practices

  • Security: Always use secure protocols for data transmission and storage.
  • Scalability: Choose a deployment platform that can scale with your traffic.
  • Monitoring: Implement monitoring and logging to keep track of your model's performance.

Additional Resources

For more detailed information, check out the following resources:

Deploying with a Local Server

If you prefer to deploy your model locally, you can use Flask or FastAPI to create a web server.

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None

@app.post("/items/")
async def create_item(item: Item):
    return item

To run the server, execute the following command:

uvicorn main:app --reload

Now, your model is ready to accept requests at http://127.0.0.1:8000/items/.

Docker Container