Save and load a Forecaster#
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.
Before we begin, we need to install chronos if it isn’t already available, we choose to use pytorch as deep learning backend.
!pip install --pre --upgrade bigdl-chronos[pytorch] !pip uninstall -y torchtext # uninstall torchtext to avoid version conflict
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.
# get TSDataset for training and testing tsdata_train, tsdata_test = get_data() # get a trained forecaster forecaster = get_trained_forecaster(tsdata_train)
Save and Load#
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.
If you are in a new session, then you should define a forecaster first, then load it by filename.
from bigdl.chronos.forecaster.tcn_forecaster import TCNForecaster forecaster = TCNForecaster(past_seq_len=48, future_seq_len=1, input_feature_num=1, output_feature_num=1) forecaster.load("./forecaster.txt")