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Hey. Can you provide a sample filename that you want to use? The names are important because they're used to figure out which model you're loading and set it up correctly. I think that documentation is outdated, the files saved are: • Model's checkpoints • Configuration • Dataset (if

save_dataset=True

)

Good morning @José Morales, I was using the sample code from the documentation,

nf.save(path='./checkpoints/test_run/',

model_index=None,

overwrite=True,

save_dataset=True)

The model files saved were,

automlp_0.ckpt

configuration.pkl

dataset.pkl

nbeats_0.ckpt

nhits_0.ckpt

I noticed the saved files do not have the following pkl files as per documentation. Does the saved configuration.pkl covers all 3 models?

automlp_0.pkl

nbeats_0.pkl

nhits_0.pkl

I would like to use my own custom model filenames. Is this possible? If yes, what should the code be like?

Acc_Model_UV_automlp_0.pkl

Acc_Model_UV_automlp_0.ckpt

Acc_Model_UV_nbeats_0.pkl

Acc_Model_UV_nbeats_0.ckpt

Acc_Model_UV_nhits_0.pkl

Acc_Model_UV_nhits_0.ckpt

Thanks.

Yes, the configuration is general, so only the model weights are required for each model. About the filenames, those won't work. The current logic of the load method is to split by underscore and take the first entry as the model name. If you want to use those filenames you can manually save and load them yourself, e.g. for save:

nf.save(path, model_index=[]) #  this doesn't save the models
for model in self.models:
    model.save(your_custom_path)

load:

from neuralforecast.core import MODEL_FILENAME_DICT

nf = NeuralForecast.load(path)
for fname in your_custom_path:
    # extract the model name from the filename
    model_name = fname.split('_')[3]  # this would extract the automlp model from your sample path (Acc_Model_UV_automlp_0.pkl)
    model = MODEL_FILENAME_DICT[model_name].load_from_checkpoint(fname)
    nf.models.append(model)

@José Morales, many thanks for the workaround code. I think I can use it. So, the configuration,pkl file is not required for loading the models and for future period predictions? if not required for predictions, then I will just disregard it.

The configuration is required for the predictions if you're using a dataset, i.e. predicting for the same series. If you use something like

nf.predict(new_df)

then you don't need it. If you do need it you can use the snippets I provided above and those will save and load the configuration

@José Morales, sorry to bump this thread. are we able to save and load nfc models using pickle? if yes, what would the code be like? My team also suggested i auto rename the saved model files to the names i need?

If you want to you can just save the whole object with something like:

nf = NeuralForecast(...)
...
with open('nf.pkl', 'wb') as f:
    pickle.dump(nf, f)

that'd have the dataset, models, etc

@José Morales, pickle worked! thank you! I can now save the nfc model in custom model file name. I have tested loading the pickle file and predict. The prediction results are correct.