Chapter 17: Model Deployment with PyTorch
Abstract: Deploying PyTorch models involves making a trained model accessible for inference in a production environment. This process can vary significantly depending on the target environment and desired scale. Key Steps in PyTorch Model Deployment: Model Export/Serialization: TorchScript: PyTorch models are often converted to TorchScript, an intermediate representation that can be run independently of Python. This enables deployment in C++ environments, mobile devices, and serverless functions. Saving the Model: The model's state dictionary and architecture can be saved using torch.save() . Python import torch import torchvision.models as models # Assuming 'model' is your trained PyTorch model model = models.resnet18(pretrained= True ) torch.save(model.state_dict(), ' model_weights.pth ' ) # For TorchScript: scripted_model = torch.jit.script(model) scripted_model.save( " scripted_model.pt " ) Choosing a D...