How can I customize PyTorch Serve for my model?

How can I customize PyTorch Serve for my model?

You can customize PyTorch Serve by creating a custom handler, modifying the configuration, and adding specific routes.

What is a custom handler and how do I create one?

A custom handler is a Python script that defines how to preprocess inputs and postprocess outputs. You can create one by subclassing the BaseHandler.

Can I add custom metrics to monitor my model's performance?

Yes, you can add custom metrics by implementing them in your handler and using a monitoring tool like Prometheus.

What are the best practices for deploying models with PyTorch Serve?

Best practices include optimizing your model, using versioning, and ensuring proper resource allocation for scalability.