How to deploy models in PyTorch?

PyTorch offers various methods for model deployment, with the most common being to use the torch.jit module to convert PyTorch models into Torch scripts or Torch models. This can be done by following these steps:

1. Convert a PyTorch model into a Torch script.

import torch

# 加载PyTorch模型
model = MyModel()

# 转换为Torch脚本
scripted_model = torch.jit.script(model)

1. Convert a PyTorch model to a Torch model.

import torch.onnx

# 加载PyTorch模型
model = MyModel()

# 转换为Torch模型
torch.save(model, 'model.pth')

1. Deploying Torch scripts or Torch models for deployment.

import torch

# 加载Torch脚本
scripted_model = torch.jit.load('scripted_model.pt')

# 加载Torch模型
model = torch.load('model.pth')

Once the model is converted to Torch script or Torch model, they can be used for inference or deployed to a production environment. Additionally, PyTorch also supports model deployment through tools like TorchServe and TorchScript, allowing users to choose the appropriate method based on their specific needs.