Feat/document model saving loading #1210
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Codecov ReportBase: 94.02% // Head: 94.01% // Decreases project coverage by
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## master #1210 +/- ##
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…jkocbek/darts into feat/document-model-saving-loading
| @@ -40,6 +40,54 @@ Furthermore, we define the following types of time series consumed by the models | |||
| * **Target series:** the series that we are interested in forecasting. | |||
| * **Covariate series:** some other series that we are not interested in forecasting, but that can provide valuable inputs to the forecasting model. | |||
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| ## Reproducibility | |||
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| If the user wishes to save a particular model and use it elsewhere or at a later point in time, darts leverages pickle to achieve that. | |||
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[nitpicking] You shouldn't talk about the user in third person. Either don't address the reader, or say "you".
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Noted and fixed.
| @@ -40,6 +40,54 @@ Furthermore, we define the following types of time series consumed by the models | |||
| * **Target series:** the series that we are interested in forecasting. | |||
| * **Covariate series:** some other series that we are not interested in forecasting, but that can provide valuable inputs to the forecasting model. | |||
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| ## Reproducibility | |||
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| If the user wishes to save a particular model and use it elsewhere or at a later point in time, darts leverages pickle to achieve that. | |||
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Darts does not only leverage pickle. Torch models rely on saving PyTorch Lightning trainer checkpoints.
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Lightning trainer checkpoints are now mentioned in the description.
| model.save("my_model.pkl") | ||
| model_loaded = RegressionModel.load("my_model.pkl") | ||
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| from darts.models import NaiveSeasonal |
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Do we need this second example? I'd say maybe the one above is enough. Also the one below seems to be saving to a .py file?
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| Special class methods for torch models: | ||
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| * **save_checkpoint** In addition, we need to use PTL save_checkpoint() to properly save the trainer and model. It is used to be able to save a snapshot of the model mid-training, and then be able to retrieve the model later. |
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Do we offer this as a method?
refine load from checkpoint explanation Co-authored-by: Julien Herzen <j.herzen@gmail.com>
refine what gets saved description Co-authored-by: Julien Herzen <j.herzen@gmail.com>
refine general description Co-authored-by: Julien Herzen <j.herzen@gmail.com>
…jkocbek/darts into feat/document-model-saving-loading
| model = NBEATSModel(input_chunk_length=24, | ||
| output_chunk_length=12) | ||
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| model.fit([series1, series2]) |
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I guess we don't even need this line. We can save a model that's not been fitted yet too.
Update title Co-authored-by: Julien Herzen <j.herzen@gmail.com>
Update saved model file name Co-authored-by: Julien Herzen <j.herzen@gmail.com>
Fixes #.
Summary
Add Reproducibility section in the Forecasting Overview part of the User Guide, describing model saving and loading.
Other Information
Still WIP, as more detail and care is needed for torch models.