Run Validation and Checkpoint every n steps

implementation help
1

I am trying to train a model indefinitely with max_steps=-1. I want to run the validation after 1_000 steps and save the best model. How can I do this?

I am trying with:

    def validation_step(self, batch, batch_idx):
        loss = self.common_step(batch, batch_idx)
        self.log("validation_loss", loss)

and

checkpoint_callback = ModelCheckpoint(
    monitor='validation_loss',
    dirpath='checkpoints',
    filename='model-{epoch:02d}-{step}-{validation_loss:.2f}',
    every_n_train_steps=1_000,
    save_top_k=1,
    verbose=True
)

trainer = Trainer(accelerator="auto",
                  default_root_dir="checkpoints",
                  # accumulate_grad_batches=accumulate_grad_batches,
                  max_steps=-1,
                  callbacks=[checkpoint_callback])

But I am gettin “HINT: Did you call log('validation_loss', value) in the LightningModule?” and only after the first epoch it starts gettins validation_loss.