Add example notebooks to integ tests

integ/test_codegen.py

@@ -42,6 +42,7 @@
 iris_df = iris_df[["target"] + [col for col in iris_df.columns if col != "target"]]
 train_data, test_data = train_test_split(iris_df, test_size=0.2, random_state=42)
 train_data.to_csv("./data/train.csv", index=False, header=False)
+test_data_no_target = test_data.drop("target", axis=1)
 
 # Upload Data
 prefix = "DEMO-scikit-iris"
@@ -149,6 +150,21 @@ def test_training_and_inference(self):
         )
         endpoint.wait_for_status("InService")
 
+        invoke_result = endpoint.invoke(
+            body=test_data_no_target.to_csv(header=False, index=False),
+            content_type="text/csv",
+            accept="text/csv",
+        )
+
+        assert invoke_result.body
+
+        invoke_result = endpoint.invoke_with_response_stream(
+            test_data_no_target.to_csv(header=False, index=False),
+            content_type="text/csv",
+            accept="application/csv",
+        )
+        assert invoke_result.body.payload_part
+
     def test_intelligent_defaults(self):
         os.environ["SAGEMAKER_CORE_ADMIN_CONFIG_OVERRIDE"] = (
             self._setup_intelligent_default_configs_and_fetch_path()

integ/test_experiment_and_trial.py

@@ -0,0 +1,78 @@
+import datetime
+import time
+import unittest
+
+from sagemaker_core.helper.session_helper import Session, get_execution_role
+from sagemaker_core.main.resources import Experiment, Trial, TrialComponent
+from sagemaker_core.main.shapes import RawMetricData, TrialComponentParameterValue
+from sagemaker_core.main.utils import get_textual_rich_logger
+
+logger = get_textual_rich_logger(__name__)
+
+sagemaker_session = Session()
+region = sagemaker_session.boto_region_name
+role = get_execution_role()
+bucket = sagemaker_session.default_bucket()
+
+
+class TestExperimentAndTrial(unittest.TestCase):
+    def test_experiment_and_trial(self):
+        experiment_name = "local-pyspark-experiment-example-" + time.strftime(
+            "%Y-%m-%d-%H-%M-%S", time.gmtime()
+        )
+        run_group_name = "Default-Run-Group-" + experiment_name
+        run_name = "local-experiment-run-" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
+
+        experiment = Experiment.create(experiment_name=experiment_name)
+        trial = Trial.create(trial_name=run_group_name, experiment_name=experiment_name)
+
+        created_after = datetime.datetime.now() - datetime.timedelta(days=5)
+        experiments_iterator = Experiment.get_all(created_after=created_after)
+        experiments = [exp.experiment_name for exp in experiments_iterator]
+
+        assert len(experiments) > 0
+        assert experiment.experiment_name in experiments
+
+        trial_component_parameters = {
+            "num_train_samples": TrialComponentParameterValue(number_value=5),
+            "num_test_samples": TrialComponentParameterValue(number_value=5),
+        }
+
+        trial_component = TrialComponent.create(
+            trial_component_name=run_name,
+            parameters=trial_component_parameters,
+        )
+        trial_component.associate_trail(trial_name=trial.trial_name)
+
+        training_parameters = {
+            "device": TrialComponentParameterValue(string_value="cpu"),
+            "data_dir": TrialComponentParameterValue(string_value="test"),
+            "optimizer": TrialComponentParameterValue(string_value="sgd"),
+            "epochs": TrialComponentParameterValue(number_value=5),
+            "hidden_channels": TrialComponentParameterValue(number_value=10),
+        }
+        trial_component.update(parameters=training_parameters)
+
+        metrics = []
+        for i in range(5):
+            accuracy_metric = RawMetricData(
+                metric_name="test:accuracy",
+                value=i / 10,
+                step=i,
+                timestamp=time.time(),
+            )
+            metrics.append(accuracy_metric)
+
+        trial_component.batch_put_metrics(metric_data=metrics)
+
+        time.sleep(10)
+        trial_component.refresh()
+
+        assert len(trial_component.parameters) == 7
+        assert len(trial_component.metrics) == 1
+        assert trial_component.metrics[0].count == 5
+
+        trial_component.disassociate_trail(trial_name=trial.trial_name)
+        trial_component.delete()
+        trial.delete()
+        experiment.delete()

src/sagemaker_core/main/code_injection/constants.py

@@ -13,7 +13,7 @@
 """Constants used in the code_injection modules."""
 from enum import Enum
 
-BASIC_TYPES = ["string", "boolean", "integer", "long", "double", "timestamp", "float"]
+BASIC_TYPES = ["string", "boolean", "integer", "long", "double", "timestamp", "float", "blob"]
 STRUCTURE_TYPE = "structure"
 MAP_TYPE = "map"
 LIST_TYPE = "list"

src/sagemaker_core/main/utils.py

@@ -500,7 +500,8 @@ def _serialize_dict(value: Dict) -> dict:
     """
     serialized_dict = {}
     for k, v in value.items():
-        if serialize_result := serialize(v):
+        serialize_result = serialize(v)
+        if serialize_result is not None:
             serialized_dict.update({k: serialize_result})
     return serialized_dict
 
@@ -517,7 +518,8 @@ def _serialize_list(value: List) -> list:
     """
     serialized_list = []
     for v in value:
-        if serialize_result := serialize(v):
+        serialize_result = serialize(v)
+        if serialize_result is not None:
             serialized_list.append(serialize_result)
     return serialized_list
 
@@ -534,7 +536,8 @@ def _serialize_shape(value: Any) -> dict:
     """
     serialized_dict = {}
     for k, v in vars(value).items():
-        if serialize_result := serialize(v):
+        serialize_result = serialize(v)
+        if serialize_result is not None:
             key = snake_to_pascal(k) if is_snake_case(k) else k
             serialized_dict.update({key[0].upper() + key[1:]: serialize_result})
     return serialized_dict

tst/generated/test_resources.py

@@ -378,8 +378,8 @@ def _convert_dict_keys_into_pascal_case(self, input_args: dict):
         return converted
 
     def _convert_to_pascal(self, string: str):
-        if string == "auto_ml_job_name":
-            return "AutoMLJobName"
+        if string.startswith("auto_ml_"):
+            return "AutoML" + snake_to_pascal(string[7:])
         return snake_to_pascal(string)
 
     def _get_required_parameters_for_function(self, func) -> dict: