diff --git a/parakeet/training/chekpoint.py b/parakeet/training/chekpoint.py
new file mode 100644
index 0000000..2e327c4
--- /dev/null
+++ b/parakeet/training/chekpoint.py
@@ -0,0 +1,97 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import operator
+from queue import PriorityQueue
+from typing import Callable, Mapping
+from pathlib import Path
+
+
+class KBest(object):
+    """
+    A utility class to help save the hard drive by only keeping K best 
+    checkpoints. 
+    
+    To be as modularized as possible, this class does not assume anything like 
+    a Trainer class or anything like a checkpoint directory, it does not know 
+    about the model or the optimizer, etc. 
+    
+    It is basically a dynamically mantained K-bset Mapping. When a new item is 
+    added to the map, save_fn is called. And when an item is remove from the 
+    map, del_fn is called. `save_fn` and `del_fn` takes a Path object as input 
+    and returns nothing.
+
+    Though it is designed to control checkpointing behaviors, it can be used 
+    to do something else if you pass some save_fn and del_fn.
+
+    Example
+    --------
+
+    >>> from pathlib import Path
+    >>> import shutil
+    >>> import paddle
+    >>> from paddle import nn
+     
+    >>> model = nn.Linear(2, 3)
+    >>> def save_model(path):
+    ...     paddle.save(model.state_dict())
+
+    >>> kbest_manager = KBest(max_size=5, save_fn=save_model)
+    >>> checkpoint_dir = Path("checkpoints")
+    >>> shutil.rmtree(checkpoint_dir)
+    >>> checkpoint_dir.mkdir(parents=True)
+    >>> a = np.random.rand(20)
+    >>> for i, score in enumerate(a):
+    ...     path = checkpoint_dir / f"step_{i}"
+    ...     kbest_manager.add_checkpoint(score, path)
+    >>> assert len(list(checkpoint_dir.glob("step_*"))) == 5
+    """
+
+    def __init__(self,
+                 max_size: int=5,
+                 save_fn: Callable=None,
+                 del_fn: Callable=lambda f: f.unlink()):
+        self.best_records: Mapping[Path, float] = {}
+        self.save_fn = save_fn
+        self.del_fn = del_fn
+        self.max_size = max_size
+        self._save_all = (max_size == -1)
+
+    def should_save(self, metric: float) -> bool:
+        if not self.full():
+            return True
+
+        # already full
+        worst_record_path = max(self.best_records, key=self.best_records.get)
+        worst_metric = self.best_records[worst_record_path]
+        return metric < worst_metric
+
+    def full(self):
+        return (not self._save_all) and len(self.best_records) == self.max_size
+
+    def add_checkpoint(self, metric, path):
+        if self.should_save(metric):
+            self.save_checkpoint_and_update(metric, path)
+
+    def save_checkpoint_and_update(self, metric, path):
+        # remove the worst
+        if self.full():
+            worst_record_path = max(self.best_records,
+                                    key=self.best_records.get)
+            self.best_records.pop(worst_record_path)
+            self.del_fn(path)
+
+        # add the new one
+        self.save_fn(path)
+        self.best_records[path] = metric
diff --git a/tests/test_checkpoint.py b/tests/test_checkpoint.py
new file mode 100644
index 0000000..56088ea
--- /dev/null
+++ b/tests/test_checkpoint.py
@@ -0,0 +1,34 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from parakeet.training.chekpoint import KBest
+import numpy as np
+from pathlib import Path
+import shutil
+
+
+def test_kbest():
+    def save_fn(path):
+        with open(path, 'wt') as f:
+            f.write(f"My path is {str(path)}\n")
+
+    kbest_manager = KBest(max_size=5, save_fn=save_fn)
+    checkpoint_dir = Path("checkpoints")
+    shutil.rmtree(checkpoint_dir)
+    checkpoint_dir.mkdir(parents=True)
+    a = np.random.rand(20)
+    for i, score in enumerate(a):
+        path = checkpoint_dir / f"step_{i}"
+        kbest_manager.add_checkpoint(score, path)
+    assert len(list(checkpoint_dir.glob("step_*"))) == 5