Deployment with TorchServe

Introduction

TorchServe is a flexible and easy-to-use tool for serving PyTorch models for inference in production. This lesson covers the deployment of models using TorchServe, including setup, configuration, and best practices.

What is TorchServe?

TorchServe is a model serving framework that allows you to deploy your PyTorch models at scale. It provides features such as:

• Model management
• Multi-model serving
• Logging and monitoring
• Custom inference handlers

Installation

To get started with TorchServe, you need to have Python and PyTorch installed on your machine. Follow these steps to install TorchServe:

1. Install Java 8 or later.
2. Install TorchServe using pip:

pip install torchserve torch-model-archiver

3. Verify the installation by checking the version:

torchserve --version

Model Setup

Before deploying a model, you need to package it. This involves creating a model archive (.mar) file:

1. Export your model to a .pth file.
2. Create a model handler if needed (for custom preprocessing and postprocessing).
3. Use the Torch Model Archiver to create the .mar file:

torch-model-archiver --model-name  --version 1.0 --serialized-file  --handler  --extra-files 

Deployment

After packaging your model, you can deploy it using TorchServe:

1. Start the TorchServe server:

torchserve --start --model-store  --ts-config 

2. Register your model:

curl -X POST "http://localhost:8081/models?url=.mar"

3. Test the endpoint with a sample input:

curl -X POST "http://localhost:8080/predictions/" -H "Content-Type: application/json" -d '{"data": }'

Best Practices

When deploying models with TorchServe, consider the following best practices:

• Monitor performance metrics (latency, throughput).
• Use version control for models.
• Implement logging for debugging.
• Test your models thoroughly before deployment.

FAQ