keras-team · maitry63 · Nov 13, 2025 · Nov 13, 2025 · Nov 13, 2025 · Nov 13, 2025
diff --git a/keras/src/trainers/data_adapters/py_dataset_adapter.py b/keras/src/trainers/data_adapters/py_dataset_adapter.py
@@ -40,6 +40,12 @@ class PyDataset:
             multiprocessed setting.
             Reduce this value to reduce the CPU memory consumption of
             your dataset. Defaults to 10.
+        shuffle: Whether to shuffle the sample ordering at the end of
+            each epoch.This argument passed to `model.fit()`. when 
+            `model.fit(.., shuffle=True)`, the training loop 
+            automatically calls `on_epoch_end()` at each epoch 
+            boundary, allowing datasets to implement custom 
+            shuffling logic. Defaults to False.
-        shuffle: Whether to shuffle the sample ordering at the end of
-            each epoch.This argument passed to `model.fit()`. when 
-            `model.fit(.., shuffle=True)`, the training loop 
-            automatically calls `on_epoch_end()` at each epoch 
-            boundary, allowing datasets to implement custom 
-            shuffling logic. Defaults to False.
+        shuffle: Whether to shuffle the sample ordering at the end of
+            each epoch. This argument is passed to `model.fit()`. When
+            `model.fit(..., shuffle=True)`, the training loop
+            automatically calls `on_epoch_end()` at each epoch
+            boundary, allowing datasets to implement custom
+            shuffling logic. Defaults to `False`.
-        shuffle: Whether to shuffle the sample ordering at the end of
-            each epoch.This argument passed to `model.fit()`. when 
-            `model.fit(.., shuffle=True)`, the training loop 
-            automatically calls `on_epoch_end()` at each epoch 
-            boundary, allowing datasets to implement custom 
-            shuffling logic. Defaults to False.
+        shuffle: Whether to shuffle the sample ordering at the end of
+            each epoch. This argument is passed to `model.fit()`. When
+            `model.fit(..., shuffle=True)`, the training loop
+            automatically calls `on_epoch_end()` at each epoch
+            boundary, allowing datasets to implement custom
+            shuffling logic. Defaults to `False`.
 
     Notes:
 
@@ -52,6 +58,9 @@ class PyDataset:
         over the dataset. They are not being used by the `PyDataset` class
         directly. When you are manually iterating over a `PyDataset`,
         no parallelism is applied.
+    - `shuffle=False` keeps the sample order fixed across epochs.
+        For distributed or deterministic training prefer
+        `shuffle=False` and manage the order externally.
 
     Example:
 
@@ -66,10 +75,12 @@ class PyDataset:
 
     class CIFAR10PyDataset(keras.utils.PyDataset):
 
-        def __init__(self, x_set, y_set, batch_size, **kwargs):
+        def __init__(self, x_set, y_set, batch_size,shuffle=False, **kwargs):
             super().__init__(**kwargs)
             self.x, self.y = x_set, y_set
             self.batch_size = batch_size
+            self.shuffle = shuffle
+            self.indices = np.arrange(len(self.x))
 
         def __len__(self):
             # Return number of batches.
@@ -87,6 +98,12 @@ def __getitem__(self, idx):
             return np.array([
                 resize(imread(file_name), (200, 200))
                    for file_name in batch_x]), np.array(batch_y)
+
+        def on_epoch_end(self):
+            # Called automatically by model.fit() when shuffle=True
+            #
+            if self.shuffle:
+                np.random.shuffle(self.indices)
     ```
     """