DOCS-2661: Updates to some custom training script functions (#3240)

Author: npentrel

docs/services/ml/upload-training-script.md

@@ -85,15 +85,16 @@ Follow this guide to create, upload, and submit a Python script that loads a tra
    # The data_json variable will contain the metadata for the dataset
    # that you should use to train the model.
    def parse_args():
-       """Dataset file and model output directory are required parameters. These
-       must be parsed as command line arguments and then used as the model input
-       and output, respectively.
+       """Returns dataset file, model output directory, and num_epochs if present. These
+       must be parsed as command line arguments and then used as the model input and output, respectively.
+       The number of epochs can be used to optionally override the default.
        """
        parser = argparse.ArgumentParser()
        parser.add_argument("--dataset_file", dest="data_json", type=str)
        parser.add_argument("--model_output_directory", dest="model_dir", type=str)
+       parser.add_argument("--num_epochs", dest="num_epochs", type=int)
        args = parser.parse_args()
-       return args.data_json, args.model_dir
+       return args.data_json, args.model_dir, args.num_epochs
 
    # This is used for parsing the dataset file (produced and stored in Viam),
    # parse it to get the label annotations
@@ -102,6 +103,7 @@ Follow this guide to create, upload, and submit a Python script that loads a tra
        filename: str, all_labels: ty.List[str], model_type: str
    ) -> ty.Tuple[ty.List[str], ty.List[str]]:
        """Load and parse JSON file to return image filenames and corresponding labels.
+       The JSON file contains lines, where each line has the key "image_path" and "classification_annotations".
        Args:
            filename: JSONLines file containing filenames and labels
            all_labels: list of all N_LABELS
@@ -121,6 +123,8 @@ Follow this guide to create, upload, and submit a Python script that loads a tra
                    if model_type == multi_label:
                        if annotation["annotation_label"] in all_labels:
                            labels.append(annotation["annotation_label"])
+                   # For single label model, we want at most one label.
+                   # If multiple valid labels are present, we arbitrarily select the last one.
                    if model_type == single_label:
                        if annotation["annotation_label"] in all_labels:
                            labels = [annotation["annotation_label"]]