Deploying with TorchServe: Model Archiver, Handlers, REST + gRPC Endpoints, and Scaling

TorchServe is a model serving framework that exposes trained models via REST and gRPC endpoints, handles batching, and manages versioning. This post covers model archiving, custom handlers, and deployment.

Model Archiving

torch-model-archiver --model-name resnet18 --version 1.0 --model-file model.py --serialized-file model.pt --handler image_classifier

This creates a .mar file that TorchServe can serve.

Conclusion

TorchServe simplifies model deployment, handling batching, versioning, and scalability. Custom handlers enable preprocessing tailored to your domain. Understanding TorchServe is essential for production ML systems. Next: we'll build a lightweight FastAPI service for inference.