Hello,

I have a set of samples (X1, Y1) that I train a neural network on (Input X1, predict Y1). This is the code that I use:

```
net = NN(nparams=nparams)
# Setup trainer.
trainer = pl.Trainer(
enable_model_summary=None,
max_epochs=1000,
log_every_n_steps=1,
callbacks=[EarlyStopping(monitor="val_loss", patience=100)],
)
# Fit model.
trainer.fit(net, datamodule=data_module)
trainer.save_checkpoint("model.ckpt")
```

Now, I obtain new data X2, Y2, and I would like to retrain the model on (X1 + X2, Y1 + Y2) where by + denote concatenation. I do this by

```
trainer = pl.Trainer(
enable_model_summary=None,
max_epochs=1000 * (t + 2),
log_every_n_steps=1,
resume_from_checkpoint="model.ckpt",
callbacks=[EarlyStopping(monitor="val_loss", patience=100)],
)
trainer.fit(net, datamodule=datamodule)
trainer.save_checkpoint("model.ckpt")
```

and then iterate this many more iterations. Here t is the iteration number, so if t = 0, I have gathered (X2, Y2) and would like to train for 2000 epochs (so 1000 from the first run, 1000 for the second run). However, after iteration 1 no retraining occurs. Why is this?

Thanks!