docs: improve checkpoint description (#2784)

Fix #2767.

deepmd/entrypoints/train.py

@@ -86,9 +86,9 @@ def train(
     INPUT : str
         json/yaml control file
     init_model : Optional[str]
-        path to checkpoint folder or None
+        path prefix of checkpoint files or None
     restart : Optional[str]
-        path to checkpoint folder or None
+        path prefix of checkpoint files or None
     output : str
         path for dump file with arguments
     init_frz_model : str

deepmd/model/frozen.py

@@ -74,7 +74,7 @@ def build(
         frz_model : str, optional
             The path to the frozen model
         ckpt_meta : str, optional
-            The path to the checkpoint and meta file
+            The path prefix of the checkpoint and meta files
         suffix : str, optional
             The suffix of the scope
         reuse : bool or tf.AUTO_REUSE, optional

deepmd/model/linear.py

@@ -166,7 +166,7 @@ def build(
         frz_model : str, optional
             The path to the frozen model
         ckpt_meta : str, optional
-            The path to the checkpoint and meta file
+            The path prefix of the checkpoint and meta files
         suffix : str, optional
             The suffix of the scope
         reuse : bool or tf.AUTO_REUSE, optional

deepmd/model/model.py

@@ -191,7 +191,7 @@ def build(
         frz_model : str, optional
             The path to the frozen model
         ckpt_meta : str, optional
-            The path to the checkpoint and meta file
+            The path prefix of the checkpoint and meta files
         suffix : str, optional
             The suffix of the scope
         reuse : bool or tf.AUTO_REUSE, optional
@@ -259,7 +259,7 @@ def build_descrpt(
         frz_model : str, optional
             The path to the frozen model
         ckpt_meta : str, optional
-            The path to the checkpoint and meta file
+            The path prefix of the checkpoint and meta files
         suffix : str, optional
             The suffix of the scope
         reuse : bool or tf.AUTO_REUSE, optional

deepmd/utils/argcheck.py

@@ -1601,7 +1601,7 @@ def training_args():  # ! modified by Ziyao: data configuration isolated.
     doc_disp_file = "The file for printing learning curve."
     doc_disp_freq = "The frequency of printing learning curve."
     doc_save_freq = "The frequency of saving check point."
-    doc_save_ckpt = "The file name of saving check point."
+    doc_save_ckpt = "The path prefix of saving check point files."
     doc_disp_training = "Displaying verbose information during training."
     doc_time_training = "Timing durining training."
     doc_profiling = "Profiling during training."

deepmd_cli/main.py

@@ -150,14 +150,14 @@ def main_parser() -> argparse.ArgumentParser:
         "--init-model",
         type=str,
         default=None,
-        help="Initialize the model by the provided checkpoint.",
+        help="Initialize the model by the provided path prefix of checkpoint files.",
     )
     parser_train_subgroup.add_argument(
         "-r",
         "--restart",
         type=str,
         default=None,
-        help="Restart the training from the provided checkpoint.",
+        help="Restart the training from the provided path prefix of checkpoint files.",
     )
     parser_train_subgroup.add_argument(
         "-f",
@@ -549,7 +549,7 @@ def main_parser() -> argparse.ArgumentParser:
         "--restart",
         type=str,
         default=None,
-        help="Restart the training from the provided checkpoint.",
+        help="Restart the training from the provided prefix of checkpoint files.",
     )
     parser_train_nvnmd.add_argument(
         "-s",

doc/nvnmd/nvnmd.md

@@ -162,7 +162,7 @@ where items are defined as:
 | numb_test  | the accuracy is test by using {numb_test} sample    | a positive integer |
 | disp_file  | the log file where the training message display     | a string           |
 | disp_freq  | display frequency                                   | a positive integer |
-| save_ckpt  | check point file                                    | a string           |
+| save_ckpt  | path prefix of check point files                    | a string           |
 | save_freq  | save frequency                                      | a positive integer |
 | systems    | a list of data directory which contains the dataset | string list        |
 | set_prefix | the prefix of dataset                               | a string           |
@@ -181,7 +181,7 @@ dp train-nvnmd train_qnn.json -s s2
 
 After the training process, you will get two folders: `nvnmd_cnn` and `nvnmd_qnn`. The `nvnmd_cnn` contains the model after continuous neural network (CNN) training. The `nvnmd_qnn` contains the model after quantized neural network (QNN) training. The binary file `nvnmd_qnn/model.pb` is the model file that is used to perform NVNMD in the server [http://nvnmd.picp.vip].
 
-You can also restart the CNN training from the checkpoint (`nvnmd_cnn/model.ckpt`) by
+You can also restart the CNN training from the path prefix of checkpoint files (`nvnmd_cnn/model.ckpt`) by
 
 ``` bash
 dp train-nvnmd train_cnn.json -r nvnmd_cnn/model.ckpt -s s1

doc/train/training-advanced.md

@@ -121,7 +121,7 @@ optional arguments:
   --skip-neighbor-stat  Skip calculating neighbor statistics. Sel checking, automatic sel, and model compression will be disabled. (default: False)
 ```
 
-**`--init-model model.ckpt`**, initializes the model training with an existing model that is stored in the checkpoint `model.ckpt`, the network architectures should match.
+**`--init-model model.ckpt`**, initializes the model training with an existing model that is stored in the path prefix of checkpoint files `model.ckpt`, the network architectures should match.
 
 **`--restart model.ckpt`**, continues the training from the checkpoint `model.ckpt`.