Fix content_type dropped when converting InputData to Channel in tuner

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: JiwaniZakir

sagemaker-train/src/sagemaker/train/tuner.py

@@ -1430,6 +1430,7 @@ def _build_training_job_definition(self, inputs):
                         input_data_config.append(
                             Channel(
                                 channel_name=inp.channel_name,
+                                content_type=inp.content_type,
                                 data_source=DataSource(
                                     s3_data_source=S3DataSource(
                                         s3_data_type="S3Prefix",

sagemaker-train/tests/unit/train/test_tuner.py

@@ -574,3 +574,36 @@ def test_build_training_job_definition_includes_internal_channels(self):
         assert "train" in channel_names, "User 'train' channel should be included"
         assert "validation" in channel_names, "User 'validation' channel should be included"
         assert len(channel_names) == 4, "Should have exactly 4 channels"
+
+    def test_build_training_job_definition_preserves_content_type(self):
+        """Test that InputData content_type is preserved when converting to Channel.
+
+        This test verifies the fix for GitHub issue #5632 where content_type was
+        dropped during InputData -> Channel conversion in HyperparameterTuner.
+        """
+        from sagemaker.core.training.configs import InputData
+
+        mock_trainer = _create_mock_model_trainer(with_internal_channels=False)
+
+        user_inputs = [
+            InputData(
+                channel_name="train",
+                data_source="s3://bucket/train",
+                content_type="text/csv",
+            ),
+        ]
+
+        tuner = HyperparameterTuner(
+            model_trainer=mock_trainer,
+            objective_metric_name="accuracy",
+            hyperparameter_ranges=_create_single_hp_range(),
+        )
+
+        definition = tuner._build_training_job_definition(user_inputs)
+
+        train_channel = next(
+            ch for ch in definition.input_data_config if ch.channel_name == "train"
+        )
+        assert train_channel.content_type == "text/csv", (
+            "content_type should be preserved when converting InputData to Channel"
+        )