Serving PyTorch Models with TorchServe: A Step-by-Step Guide

TorchServe is a model serving library for PyTorch that simplifies the process of deploying and serving PyTorch models in production environments. In this tutorial, we will go through the steps required to serve PyTorch models with TorchServe.

Step 1: Install TorchServe
First, you need to install TorchServe. You can do this by running the following command:

pip install torchserve torch-model-archiver

Step 2: Prepare your PyTorch model
Before serving your PyTorch model with TorchServe, you need to make sure that your model is compatible with TorchServe. Your model should be trained using PyTorch and saved in a format that TorchServe can understand. You can save your PyTorch model using the torch.save() function.

import torch

# Define your PyTorch model
class MyModel(torch.nn.Module):
    # Define your model architecture here

# Initialize an instance of your model
model = MyModel()

# Save the model
torch.save(model.state_dict(), 'model.pth')

Step 3: Create a TorchServe model archive
Next, you need to create a TorchServe model archive from your PyTorch model. A model archive is a file that contains the model weights, configuration, and metadata required for serving the model.

You can create a model archive using the torch-model-archiver command line tool. First, create a model handler script that specifies how to load and forward the model:

# model_handler.py

import torch
from ts.torch_handler.base_handler import BaseHandler

class MyModelHandler(BaseHandler):
    def initialize(self, context):
        self.model = torch.load('model.pth')
        self.model.eval()

    def preprocess(self, data):
        # Preprocess input data here
        return data

    def inference(self, data):
        output = self.model(data)
        return output

    def postprocess(self, data):
        # Postprocess output data here
        return data

Next, create the model archive by running the following command:

torch-model-archiver --model-name my_model --handler model_handler.py --serialized-file model.pth --export-path model_store

This command will create a model archive named my_model.mar in the model_store directory.

Step 4: Start TorchServe
Now that you have created a model archive, you can start TorchServe and serve your PyTorch model. You can start TorchServe using the following command:

torchserve --start --model-store model_store --models my_model=my_model.mar

This command starts TorchServe and loads the my_model.mar model archive into memory.

Step 5: Make predictions with your PyTorch model
You can now make predictions with your PyTorch model using TorchServe. You can send a POST request to the TorchServe endpoint to make predictions:

curl -X POST http://localhost:8080/predictions/my_model -T your_input_file.json

In this command, replace your_input_file.json with the file containing your input data. TorchServe will preprocess the input data, pass it to your PyTorch model for inference, and return the output predictions.

Step 6: Stop TorchServe
Once you are done serving your PyTorch model, you can stop TorchServe using the following command:

torchserve --stop

This command will stop TorchServe and release any resources used for serving the model.

In conclusion, serving PyTorch models with TorchServe is a straightforward process that involves preparing your model, creating a model archive, starting TorchServe, making predictions, and stopping TorchServe when you are done. With TorchServe, you can easily deploy PyTorch models in production environments and scale them to serve a large number of requests.