Serialization (torch::serialize)

The torch::serialize namespace provides utilities for saving and loading model weights, tensors, and optimizer state. This enables checkpointing during training and deployment of trained models.

When to use torch::serialize:

• When saving trained models to disk
• When implementing training checkpoints
• When loading pre-trained weights
• When transferring models between C++ and Python

Basic usage:

#include <torch/torch.h>

// Save a model
auto model = std::make_shared<Net>();
// ... train the model ...
torch::save(model, "model.pt");

// Load a model
auto loaded_model = std::make_shared<Net>();
torch::load(loaded_model, "model.pt");

// Save and load tensors
torch::Tensor tensor = torch::randn({2, 3});
torch::save(tensor, "tensor.pt");

torch::Tensor loaded_tensor;
torch::load(loaded_tensor, "tensor.pt");

Header Files

• torch/csrc/api/include/torch/serialize.h - Main serialization header
• torch/csrc/api/include/torch/serialize/archive.h - Archive classes
• torch/csrc/api/include/torch/serialize/input-archive.h - Input archive
• torch/csrc/api/include/torch/serialize/output-archive.h - Output archive

Serialization Categories

:maxdepth: 1

save_load
archives
checkpoints