The saved module serializes all of the methods, submodules, parameters, and attributes of this module. It can be loaded into the C++ API using torch::jit::load( ...

Saves an object to a disk file. ... A common PyTorch convention is to save tensors using .pt file extension. ... PyTorch preserves storage sharing ...

and then load_state_dict() to avoid GPU RAM surge when loading a model checkpoint.

All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.

Saves given tensors for a future call to backward() . save_for_backward should be called at most once, only from inside the forward() method, and only with ...

load would recover the module to devices where the module was saved from. Note. When a model is trained on M nodes with batch=N , the gradient will be ...

Buffers, by default, are persistent and will be saved alongside parameters.

Buffers, by default, are persistent and will be saved alongside parameters.

Given input model and a state_dict containing model observer stats, load the stats back into the model. The observer state_dict can be saved using ...

The call `debugger` cannot be saved since it calls into Python ...