Repository: huggingface/transformers
Pull request: disable deepspeed when setting up fake trainer

# What does this PR do?
When training with deepspeed and `WANDB_LOG_MODEL` is enabled, on train end, a fake trainer is created on the train end to create artifacts, but results in https://gist.github.com/winglian/e4640bb86860678ba2415cde54d986f4

This removes deepspeed from the args to workaround that issue.

<!-- Remove if not applicable -->

Fixes # (issue)


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Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
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Changed files:
filename: src/transformers/integrations/integration_utils.py
patch: @@ -15,6 +15,7 @@
 Integrations with other Python libraries.
 """
 
+import copy
 import functools
 import importlib.metadata
 import importlib.util
@@ -33,7 +34,7 @@
 import numpy as np
 import packaging.version
 
-from .. import PreTrainedModel, TFPreTrainedModel
+from .. import PreTrainedModel, TFPreTrainedModel, TrainingArguments
 from .. import __version__ as version
 from ..utils import (
     PushToHubMixin,
@@ -929,13 +930,17 @@ def on_train_begin(self, args, state, control, model=None, **kwargs):
         if not self._initialized:
             self.setup(args, state, model, **kwargs)
 
-    def on_train_end(self, args, state, control, model=None, processing_class=None, **kwargs):
+    def on_train_end(self, args: TrainingArguments, state, control, model=None, processing_class=None, **kwargs):
         if self._wandb is None:
             return
         if self._log_model.is_enabled and self._initialized and state.is_world_process_zero:
             from ..trainer import Trainer
 
-            fake_trainer = Trainer(args=args, model=model, processing_class=processing_class, eval_dataset=["fake"])
+            args_for_fake = copy.deepcopy(args)
+            args_for_fake.deepspeed = None
+            fake_trainer = Trainer(
+                args=args_for_fake, model=model, processing_class=processing_class, eval_dataset=["fake"]
+            )
             with tempfile.TemporaryDirectory() as temp_dir:
                 fake_trainer.save_model(temp_dir)
                 metadata = (