doc: enhance documentation for training and validation data systems input handling

Author: OutisLi

deepmd/utils/argcheck.py

@@ -2993,8 +2993,9 @@ def training_data_args() -> list[
     link_sys = make_link("systems", "training/training_data/systems")
     doc_systems = (
         "The data systems for training. "
+        "This key can be a list or a str. "
         "When provided as a string, it can be a system directory path (containing 'type.raw') or a parent directory path to recursively search for all system subdirectories. "
-        "When provided as a list, each string item is processed the same way as individual string inputs."
+        "When provided as a list, each string item in the list is processed the same way as individual string inputs, i.e., each path can be a system directory or a parent directory to recursively search for all system subdirectories."
     )
     doc_patterns = (
         "The customized patterns used in `rglob` to collect all training systems. "
@@ -3074,8 +3075,9 @@ def validation_data_args() -> list[
     link_sys = make_link("systems", "training/validation_data/systems")
     doc_systems = (
         "The data systems for validation. "
+        "This key can be a list or a str. "
         "When provided as a string, it can be a system directory path (containing 'type.raw') or a parent directory path to recursively search for all system subdirectories. "
-        "When provided as a list, each string item is processed the same way as individual string inputs."
+        "When provided as a list, each string item in the list is processed the same way as individual string inputs, i.e., each path can be a system directory or a parent directory to recursively search for all system subdirectories."
     )
     doc_patterns = (
         "The customized patterns used in `rglob` to collect all validation systems. "

doc/train/training-advanced.md

@@ -76,8 +76,8 @@ Other training parameters are given in the {ref}`training <training>` section.
 The sections {ref}`training_data <training/training_data>` and {ref}`validation_data <training/validation_data>` give the training dataset and validation dataset, respectively. Taking the training dataset for example, the keys are explained below:
 
 - {ref}`systems <training/training_data/systems>` provide paths of the training data systems. DeePMD-kit allows you to provide multiple systems with different numbers of atoms. This key can be a `list` or a `str`.
-  - `list`: {ref}`systems <training/training_data/systems>` gives the training data systems.
-  - `str`: {ref}`systems <training/training_data/systems>` should be a valid path. DeePMD-kit will recursively search all data systems in this path.
+  - `str`: {ref}`systems <training/training_data/systems>` should be a valid path. It can be a system directory path (containing 'type.raw') or a parent directory path to recursively search for all system subdirectories.
+  - `list`: {ref}`systems <training/training_data/systems>` gives a list of paths. Each string item in the list is processed the same way as individual string inputs, i.e., each path can be a system directory or a parent directory to recursively search for all system subdirectories.
 - At each training step, DeePMD-kit randomly picks {ref}`batch_size <training/training_data/batch_size>` frame(s) from one of the systems. The probability of using a system is by default in proportion to the number of batches in the system. More options are available for automatically determining the probability of using systems. One can set the key {ref}`auto_prob <training/training_data/auto_prob>` to
   - `"prob_uniform"` all systems are used with the same probability.
   - `"prob_sys_size"` the probability of using a system is proportional to its size (number of frames).