To deploy a Sentence Transformer model as an API, you can use web frameworks like Flask or FastAPI to create endpoints that handle inference requests. The core steps involve loading the model, defining an endpoint to accept input text, and returning embeddings. For example, with FastAPI, you initialize the model once at startup to avoid reloading it for each request. The API receives text via a POST request, encodes it using the model’s encode() method, and returns the embeddings as a JSON response. This approach is lightweight and suitable for small-scale deployments or prototyping. You’ll need to handle input validation, error cases (like empty text), and ensure the output is serializable (e.g., converting NumPy arrays to Python lists).
For production environments, TorchServe provides a dedicated serving solution optimized for PyTorch models. You package the Sentence Transformer model into a .mar file using TorchServe’s utilities, which includes the model weights and a custom handler class. The handler processes incoming requests, converts text inputs into tensors, runs the model, and formats the output. TorchServe manages scaling via worker threads, metrics, and versioning, making it easier to handle higher traffic. For instance, you might write a handler that batches requests or applies tokenization before inference. While this requires more setup than Flask/FastAPI, it offers better performance and scalability out of the box.
Key considerations include hardware optimization (using GPUs with device=cuda), dependency management (e.g., Docker containers to bundle PyTorch and transformers), and latency reduction (caching or batching requests). For Flask/FastAPI, use ASGI servers like Uvicorn with multiple workers to parallelize requests. Testing with tools like curl or automated scripts ensures reliability. For example, a typical request to a FastAPI endpoint might send {"texts": ["example sentence"]} and receive a {"embeddings": [...]} response. Regardless of the tool, logging, monitoring, and input sanitization (to prevent injection attacks) are critical for maintaining a robust service.