I use create_supervised_trainer in combination with ModelCheckpoint.

checkpoint = ModelCheckpoint(
    f"./models/{start_time}",
    "resnet_50",
    score_name="loss",
    score_function=lambda engine: 1 - engine.state.output, # output contains the minibatch output
    n_saved=10,
    create_dir=True,
)

With supervised trainer in not straight forward to add a metric. So users only have access to the state.output, which only contains the minibatch. This causes models to be saved that based on the model performance on the last minibatch, not the whole epoch.

It might be a good idea to add a warning to the description of Modelcheckpoint that output contains the minibach output only. And/or add an example that shows how to use it in combination with a metric.