update doc for mini batch

Author: enigne

docs/advanced.rst

@@ -10,9 +10,11 @@ Mini-batch
 
 PINNICLE supports mini-batch training when using the ``pytorch`` backend. In the current implementation, mini-batches are resampled at every epoch to enhance convergence stability.
 To activate mini-batch training, simply add the following setting to the hyperparameter dictionary. PINNICLE will automatically generate mini-batches from the data specified in ``hp["data"]``.
+And in the data section, you can set the ``"data_size"`` to a large number or simply ``"MAX"``, to cover a large amount of your data.
 
 .. code-block:: python
    
+   hp_local["data"] = {"ISSM": {"data_size": {"u":100, "v":"MAX", "s":100, "H":10, "C":None}}}
    hp["mini_batch"] = mini_batch
 
 - Avoid setting ``mini_batch`` too large, as this can lead to inefficiencies and excessive memory usage.

tests/test_pinn.py

@@ -277,7 +277,7 @@ def test_only_callbacks(tmp_path):
     hp_local = dict(hp)
     hp_local["save_path"] = str(tmp_path)
     hp_local["num_collocation_points"] = 100
-    issm["data_size"] = {"u":100, "v":100, "s":100, "H":10, "C":None, "vel":100}
+    issm["data_size"] = {"u":100, "v":"MAX", "s":100, "H":10, "C":None, "vel":100}
     hp_local["data"] = {"ISSM": issm}
     hp_local["min_delta"] = 1e10
     hp_local["period"] = 5