Save and load data and model

In this section, we will present how to save and load data and model.

Save data and model

When we use the run_hopwise() function mentioned in Use run_hopwise, it will save the best model parameters in training process and its corresponding config settings. If you want to save filtered dataset and split dataloaders, you can set parameter save_dataset and parameter save_dataloaders to True to save filtered dataset and split dataloaders.

You can refer to Config Introduction for more details about save_dataset and save_dataloaders.

Load data and model

If you want to reload the data and model, you can apply load_data_and_model() to get them. You can also pass dataset_file and dataloader_file to this function to reload data from file, which can reduce the time of data filtering and data splitting.

Here we present a typical usage of load_data_and_model():

config, model, dataset, train_data, valid_data, test_data = load_data_and_model(
    model_file='saved/BPR-Aug-21-2021_13-06-00.pth',
)
# Here you can replace it by your model path.
# And you can also pass 'dataset_file' and 'dataloader_file' to this function.