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torch.save
pytorch doc

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

torch.jit.save

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( ...

torch.load

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

torch.jit.load

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

torch.autograd.function.FunctionCtx.save_for_backward - Function

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 ...

torch.nn.parallel.DistributedDataParallel

Without map_location , torch.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 ...

torch.jit.ScriptModule - Module

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

torch.jit.ignore

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

torch.nn.Module - Module

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

torch.quantization.observer.load_observer_state_dict

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 ...

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