[CheckpointStatus] - add checkpoint status handler; Model.train() accepts status param

status.py: add CheckpointStatus(save_fn, update_interval) — prints progress
and calls save_fn at every report interval to persist model state mid-training.
model.py: Model.train() now accepts an optional Status instance; defaults to
plain Status() if none provided.
test_faces_sub_image.py: use CheckpointStatus(model.save) during training.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/rbm/model.py

@@ -20,12 +20,12 @@ class Model(ABC):
 				obj_list.append(value)
 		return obj_list
 
-	def train(self, batch: Mat):
+	def train(self, batch: Mat, status: Status = None):
 		entities = self.objects(Entity)
 		_batch = np.array(batch)   # np.array() converts numpy→CuPy; np.copy() does not
 		for entity in entities:
 			if entity.enable_training:
-				train(entity, _batch, Status())
+				train(entity, _batch, status if status is not None else Status())
 			_batch = entity.forward(_batch)
 
 	def forward(self, x: Mat) -> Mat:

src/rbm/status.py

@@ -36,4 +36,17 @@ class Status:
 
 	def on_report(self, entity: Entity, status: dict) -> bool:
 		Status.print_status(entity, status)
+		return True
+
+
+class CheckpointStatus(Status):
+	"""Prints progress and saves model state via save_fn at every report interval."""
+
+	def __init__(self, save_fn, update_interval: int = 10):
+		super().__init__(update_interval)
+		self.save_fn = save_fn
+
+	def on_report(self, entity: Entity, status: dict) -> bool:
+		Status.print_status(entity, status)
+		self.save_fn()
 		return True

src/tests/test_faces_sub_image.py

@@ -7,6 +7,7 @@ from rbm.model import Model
 from rbm.entity import Entity, EntityParams, TrainingParams
 from rbm.image import SubImage, normalize
 from rbm.matrix import Mat, np, convert
+from rbm.status import CheckpointStatus
 
 DATA_DIR  = '/media/jens/cifs/bilder/MachineVision/Caltech_WebFaces/'
 PATCH     = 32
@@ -214,7 +215,7 @@ if __name__ == '__main__':
 		print(f'Loading {args.n_images} training images (stride={STRIDE})...')
 		train_patches = load_patches(DATA_DIR, args.n_images)
 		print(f'Training on {train_patches.shape[0]} patches ({N_VIS}-dim each)')
-		model.train(train_patches)
+		model.train(train_patches, status=CheckpointStatus(save_fn=model.save))
 		model.save()
 
 	# Show learned weight filters