Push to hub save

src/transformers/trainer.py

@@ -966,7 +966,7 @@ def _tune_save_checkpoint(self):
             return
         with tune.checkpoint_dir(step=self.state.global_step) as checkpoint_dir:
             output_dir = os.path.join(checkpoint_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
-            self.save_model(output_dir)
+            self.save_model(output_dir, _internal_call=True)
             if self.args.should_save:
                 self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
                 torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
@@ -1634,7 +1634,7 @@ def _save_checkpoint(self, model, trial, metrics=None):
             self.store_flos()
 
         output_dir = os.path.join(run_dir, checkpoint_folder)
-        self.save_model(output_dir)
+        self.save_model(output_dir, _internal_call=True)
         if self.deepspeed:
             # under zero3 model file itself doesn't get saved since it's bogus! Unless deepspeed
             # config `stage3_gather_fp16_weights_on_model_save` is True
@@ -2002,7 +2002,7 @@ def is_world_process_zero(self) -> bool:
         else:
             return self.args.process_index == 0
 
-    def save_model(self, output_dir: Optional[str] = None):
+    def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):
         """
         Will save the model, so you can reload it using `from_pretrained()`.
 
@@ -2051,6 +2051,10 @@ def save_model(self, output_dir: Optional[str] = None):
         elif self.args.should_save:
             self._save(output_dir)
 
+        # Push to the Hub when `save_model` is called by the user.
+        if self.args.push_to_hub and not _internal_call:
+            self.push_to_hub(commit_message="Model save")
+
     def _save_tpu(self, output_dir: Optional[str] = None):
         output_dir = output_dir if output_dir is not None else self.args.output_dir
         logger.info(f"Saving model checkpoint to {output_dir}")
@@ -2768,9 +2772,10 @@ def push_to_hub(self, commit_message: Optional[str] = "End of training", blockin
                 model_name = Path(self.args.output_dir).name
             else:
                 model_name = self.args.hub_model_id.split("/")[-1]
+
         # Needs to be executed on all processes for TPU training, but will only save on the processed determined by
         # self.args.should_save.
-        self.save_model()
+        self.save_model(_internal_call=True)
 
         # Only push from one node.
         if not self.is_world_process_zero():

src/transformers/training_args.py

@@ -365,9 +365,18 @@ class TrainingArguments:
             Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
             down the training and evaluation speed.
         push_to_hub (`bool`, *optional*, defaults to `False`):
-            Whether or not to upload the trained model to the hub after training. If this is activated, and
-            `output_dir` exists, it needs to be a local clone of the repository to which the [`Trainer`] will be
+            Whether or not to push the model to the Hub every time the model is saved. If this is activated,
+            `output_dir` will begin a git directory synced with the the repo (determined by `hub_model_id`) and the
+            content will be pushed each time a save is triggered (depneding on your `save_strategy`). Calling
+            [`~Trainer.save_model`] will also trigger a push
+
+            <Tip warning={true}>
+
+            If `output_dir` exists, it needs to be a local clone of the repository to which the [`Trainer`] will be
             pushed.
+
+            </Tip>
+
         resume_from_checkpoint (`str`, *optional*):
             The path to a folder with a valid checkpoint for your model. This argument is not directly used by
             [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example
@@ -384,7 +393,7 @@ class TrainingArguments:
             Defines the scope of what is pushed to the Hub and when. Possible values are:
 
             - `"end"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a
-              draft of a model card at the end of training.
+              draft of a model card when the [`~Trainer.save_model`] method is called.
             - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and
               a draft of a model card each time there is a model save. The pushes are asynchronous to not block
               training, and in case the save are very frequent, a new push is only attempted if the previous one is