Hi,
I’m currently implementing an autoencoder with PyTorch (client with Libtorch in C++) and I want to ask if there is an example or some guide on how to retrain model using Flower with new data to improve the prediction performance.
Hi @neonduck,
Flower doesn’t have a special “retrain” mode. Retraining with new data is done by starting a new run and initializing it with the previously trained global model.