[bugfix] - support large CPU-side datasets; fix numpy/CuPy interop

train.py: add _to_gpu() helper; convert mini-batches from CPU numpy to GPU
CuPy on the fly so large datasets can stay in RAM. Use numpy permutation for
numpy batches during shuffle. Limit status err_rms to first 5000 samples to
avoid GPU OOM on status checks.

model.py: replace np.copy() with np.array() — cupy.copy() does not convert
numpy arrays to CuPy; cupy.array() does. Fixes entity.forward() crash after
training when batch was passed as numpy.

test_faces_sub_image.py: load_patches normalizes on CPU and returns numpy,
avoiding multi-copy GPU OOM for large datasets (500 images ≈ 1.4 GB).
show_reconstructions converts numpy patches to CuPy before inference.
Add --n_images CLI arg.

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

src/rbm/model.py

@@ -22,7 +22,7 @@ class Model(ABC):
 
 	def train(self, batch: Mat):
 		entities = self.objects(Entity)
-		_batch = np.copy(batch)
+		_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())