# Save and load a Forecaster#

## Introduction#

Once we have trained a forecaster, we usually need to save the model and load it later for further training, either model evaluation or model deployment. In this guidance, we demonstrate how to save and load a forecaster in detail.

We will take TCNForecaster and nyc_taxi dataset as an example in this guide.

## Setup#

Before we begin, we need to install chronos if it isn’t already available, we choose to use pytorch as deep learning backend.

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!pip install --pre --upgrade bigdl-chronos[pytorch]
!pip uninstall -y torchtext # uninstall torchtext to avoid version conflict


## Forecaster preparation#

Before saving a forecaster, a forecaster should be created and trained. The training process is introduced in the previous guidance Train forcaster on single node in detail, therefore we directly create and train a TCNForecaster based on the nyc taxi dataset.

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# get TSDataset for training and testing
tsdata_train, tsdata_test = get_data()
# get a trained forecaster
forecaster = get_trained_forecaster(tsdata_train)


After you have trained a forecaster, you can simply save your forecaster by calling save() with a filename. Then you should load it with the same filename.

[ ]:

forecaster.save("./forecaster.txt")

[ ]:

from bigdl.chronos.forecaster.tcn_forecaster import TCNForecaster