Save TB logs as part of push_to_hub (#27022)

* Support runs/

* Upload runs folder as part of push to hub

* Add a test

* Add to test deps

* Update with proposed solution from Slack

* Ensure that repo gets deleted in tests

tests/trainer/test_trainer.py

@@ -30,7 +30,7 @@ from typing import Dict, List
 from unittest.mock import Mock, patch
 
 import numpy as np
-from huggingface_hub import HfFolder, delete_repo, list_repo_commits
+from huggingface_hub import HfFolder, delete_repo, list_repo_commits, list_repo_files
 from parameterized import parameterized
 from requests.exceptions import HTTPError
 
@@ -60,6 +60,7 @@ from transformers.testing_utils import (
     require_safetensors,
     require_sentencepiece,
     require_sigopt,
+    require_tensorboard,
     require_tokenizers,
     require_torch,
     require_torch_bf16_cpu,
@@ -138,11 +139,14 @@ class RegressionDataset:
 class RegressionTrainingArguments(TrainingArguments):
     a: float = 0.0
     b: float = 0.0
+    keep_report_to: bool = False
 
     def __post_init__(self):
         super().__post_init__()
-        # save resources not dealing with reporting (also avoids the warning when it's not set)
-        self.report_to = []
+        # save resources not dealing with reporting unless specified (also avoids the warning when it's not set)
+        # can be explicitly disabled via `keep_report_to`
+        if not self.keep_report_to:
+            self.report_to = []
 
 
 class RepeatDataset:
@@ -319,7 +323,9 @@ if is_torch_available():
             h = nn.functional.relu(self.linear2(x))
             return self.ln2(x + h + self.bias)
 
-    def get_regression_trainer(a=0, b=0, double_output=False, train_len=64, eval_len=64, pretrained=True, **kwargs):
+    def get_regression_trainer(
+        a=0, b=0, double_output=False, train_len=64, eval_len=64, pretrained=True, keep_report_to=False, **kwargs
+    ):
         label_names = kwargs.get("label_names", None)
         train_dataset = RegressionDataset(length=train_len, label_names=label_names)
         eval_dataset = RegressionDataset(length=eval_len, label_names=label_names)
@@ -340,7 +346,7 @@ if is_torch_available():
         output_dir = kwargs.pop("output_dir", "./regression")
         preprocess_logits_for_metrics = kwargs.pop("preprocess_logits_for_metrics", None)
 
-        args = RegressionTrainingArguments(output_dir, a=a, b=b, **kwargs)
+        args = RegressionTrainingArguments(output_dir, a=a, b=b, keep_report_to=keep_report_to, **kwargs)
         return Trainer(
             model,
             args,
@@ -2155,7 +2161,7 @@ class TrainerIntegrationWithHubTester(unittest.TestCase):
 
     @classmethod
     def tearDownClass(cls):
-        for model in ["test-trainer", "test-trainer-epoch", "test-trainer-step"]:
+        for model in ["test-trainer", "test-trainer-epoch", "test-trainer-step", "test-trainer-tensorboard"]:
             try:
                 delete_repo(token=cls._token, repo_id=model)
             except HTTPError:
@@ -2264,6 +2270,28 @@ class TrainerIntegrationWithHubTester(unittest.TestCase):
         for i in range(5, max_steps, 5):
             self.assertIn(f"Training in progress, step {i}", commits)
 
+    @require_tensorboard
+    def test_push_to_hub_with_tensorboard_logs(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            trainer = get_regression_trainer(
+                output_dir=os.path.join(tmp_dir, "test-trainer-tensorboard"),
+                hub_token=self._token,
+                save_strategy="epoch",
+                report_to=["tensorboard"],
+                keep_report_to=True,
+            )
+            trainer.train()
+            # Push the runs via `push_to_hub()`
+            trainer.push_to_hub()
+
+        files = list_repo_files(f"{USER}/test-trainer-tensorboard", token=self._token)
+        found_log = False
+        for f in files:
+            if len(f.split("runs")) > 1 and "events.out.tfevents" in f:
+                found_log = True
+
+        assert found_log is True, "No tensorboard log found in repo"
+
 
 @require_torch
 @require_optuna