`ultralytics 8.1.30` add advanced HUB train arguments (#9110)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.29"
+__version__ = "8.1.30"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld

ultralytics/engine/model.py

@@ -124,7 +124,7 @@ class Model(nn.Module):
         # Check if Ultralytics HUB model from https://hub.ultralytics.com
         if self.is_hub_model(model):
             # Fetch model from HUB
-            checks.check_requirements("hub-sdk>0.0.2")
+            checks.check_requirements("hub-sdk>=0.0.5")
             self.session = self._get_hub_session(model)
             model = self.session.model_file
 

ultralytics/hub/session.py

@@ -170,10 +170,19 @@ class HUBTrainingSession:
 
         return api_key, model_id, filename
 
-    def _set_train_args(self, **kwargs):
-        """Initializes training arguments and creates a model entry on the Ultralytics HUB."""
+    def _set_train_args(self):
+        """
+        Initializes training arguments and creates a model entry on the Ultralytics HUB.
+
+        This method sets up training arguments based on the model's state and updates them with any additional
+        arguments provided. It handles different states of the model, such as whether it's resumable, pretrained,
+        or requires specific file setup.
+
+        Raises:
+            ValueError: If the model is already trained, if required dataset information is missing, or if there are
+                issues with the provided training arguments.
+        """
         if self.model.is_trained():
-            # Model is already trained
             raise ValueError(emojis(f"Model is already trained and uploaded to {self.model_url} 🚀"))
 
         if self.model.is_resumable():
@@ -182,26 +191,16 @@ class HUBTrainingSession:
             self.model_file = self.model.get_weights_url("last")
         else:
             # Model has no saved weights
-            def get_train_args(config):
-                """Parses an identifier to extract API key, model ID, and filename if applicable."""
-                return {
-                    "batch": config["batchSize"],
-                    "epochs": config["epochs"],
-                    "imgsz": config["imageSize"],
-                    "patience": config["patience"],
-                    "device": config["device"],
-                    "cache": config["cache"],
-                    "data": self.model.get_dataset_url(),
-                }
-
-            self.train_args = get_train_args(self.model.data.get("config"))
+            self.train_args = self.model.data.get("train_args")  # new response
+
             # Set the model file as either a *.pt or *.yaml file
             self.model_file = (
                 self.model.get_weights_url("parent") if self.model.is_pretrained() else self.model.get_architecture()
             )
 
-        if not self.train_args.get("data"):
-            raise ValueError("Dataset may still be processing. Please wait a minute and try again.")  # RF fix
+        if "data" not in self.train_args:
+            # RF bug - datasets are sometimes not exported
+            raise ValueError("Dataset may still be processing. Please wait a minute and try again.")
 
         self.model_file = checks.check_yolov5u_filename(self.model_file, verbose=False)  # YOLOv5->YOLOv5u
         self.model_id = self.model.id