Add integ for test_hyperparameter_tuning_job and test_transform_job

integ/sagemaker_cleaner.py

@@ -1,5 +1,11 @@
 import datetime
-from sagemaker_core.main.resources import Model, EndpointConfig, Endpoint
+from sagemaker_core.main.resources import (
+    HyperParameterTuningJob,
+    Model,
+    EndpointConfig,
+    Endpoint,
+    TransformJob,
+)
 
 
 class SageMakerCleaner:
@@ -85,6 +91,40 @@ def cleanup_models(self, creation_time_before, creation_time_after):
                 self._track_resource(failed=1)
         self._track_resource(deleted=1)
 
+    def cleanup_hyperparameter_tuningjob(self, creation_time_before, creation_time_after):
+        """Deletes Models before a given timestamp
+
+        Args:
+            creation_time_before (datetime): timestamp for 'CreationTimeBefore' or 'CreatedBefore' boto3 parameter
+            creation_time_after (datetime): timestamp for 'CreationTimeAfter' or 'CreatedAfter' boto3 parameter
+        """
+        tuning_jobs = HyperParameterTuningJob.get_all(
+            creation_time_before=creation_time_before, creation_time_after=creation_time_after
+        )
+        for tuning_job in tuning_jobs:
+            try:
+                tuning_job.delete()
+            except:
+                self._track_resource(failed=1)
+        self._track_resource(deleted=1)
+
+    def cleanup_transform_job(self, creation_time_before, creation_time_after):
+        """Deletes Models before a given timestamp
+
+        Args:
+            creation_time_before (datetime): timestamp for 'CreationTimeBefore' or 'CreatedBefore' boto3 parameter
+            creation_time_after (datetime): timestamp for 'CreationTimeAfter' or 'CreatedAfter' boto3 parameter
+        """
+        transform_jobs = TransformJob.get_all(
+            creation_time_before=creation_time_before, creation_time_after=creation_time_after
+        )
+        for transform_job in transform_jobs:
+            try:
+                transform_job.stop()
+            except:
+                self._track_resource(failed=1)
+        self._track_resource(deleted=1)
+
     def _track_resource(self, deleted=0, failed=0):
         """Updates the resource tracker with # of deleted, or failed resources
 

integ/test_codegen.py

@@ -10,7 +10,11 @@
 from sklearn.model_selection import train_test_split
 
 from sagemaker_cleaner import handle_cleanup
-from sagemaker_core.main.shapes import ContainerDefinition, ProductionVariant, ProfilerConfig
+from sagemaker_core.main.shapes import (
+    ContainerDefinition,
+    ProductionVariant,
+    ProfilerConfig,
+)
 from sagemaker_core.main.resources import (
     TrainingJob,
     AlgorithmSpecification,

integ/test_hyperparameter_tuning_job_and_transform_job.py

@@ -0,0 +1,298 @@
+import datetime
+import logging
+import time
+import unittest
+import pandas as pd
+from io import StringIO
+
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+
+from sagemaker_cleaner import handle_cleanup
+from sagemaker_core.main.shapes import (
+    AutoParameter,
+    Autotune,
+    ContainerDefinition,
+    HyperParameterAlgorithmSpecification,
+    HyperParameterTrainingJobDefinition,
+    HyperParameterTuningJobConfig,
+    HyperParameterTuningJobObjective,
+    ParameterRanges,
+    ResourceLimits,
+    TransformDataSource,
+    TransformInput,
+    TransformOutput,
+    TransformResources,
+    TransformS3DataSource,
+)
+from sagemaker_core.main.resources import (
+    HyperParameterTuningJob,
+    TrainingJob,
+    TransformJob,
+    AlgorithmSpecification,
+    Channel,
+    DataSource,
+    S3DataSource,
+    OutputDataConfig,
+    ResourceConfig,
+    StoppingCondition,
+    Model,
+)
+from sagemaker_core.helper.session_helper import Session, get_execution_role
+
+logger = logging.getLogger()
+
+sagemaker_session = Session()
+region = sagemaker_session.boto_region_name
+role = get_execution_role()
+bucket = sagemaker_session.default_bucket()
+
+### Data preparation for test_hyperparameter_tuning_job and test_transform_job
+data = sagemaker_session.read_s3_file(
+    f"sagemaker-example-files-prod-{region}", "datasets/tabular/synthetic/churn.txt"
+)
+
+df = pd.read_csv(StringIO(data))
+
+df = df.drop("Phone", axis=1)
+df["Area Code"] = df["Area Code"].astype(object)
+df = df.drop(["Day Charge", "Eve Charge", "Night Charge", "Intl Charge"], axis=1)
+
+model_data = pd.get_dummies(df)
+model_data = pd.concat(
+    [
+        model_data["Churn?_True."],
+        model_data.drop(["Churn?_False.", "Churn?_True."], axis=1),
+    ],
+    axis=1,
+)
+model_data = model_data.astype(float)
+
+train_data2, validation_data = train_test_split(model_data, test_size=0.33, random_state=42)
+
+validation_data, test_data2 = train_test_split(validation_data, test_size=0.33, random_state=42)
+
+test_target_column = test_data2["Churn?_True."]
+test_data2.drop(["Churn?_True."], axis=1, inplace=True)
+
+train_data2.to_csv("train2.csv", header=False, index=False)
+validation_data.to_csv("validation.csv", header=False, index=False)
+test_data2.to_csv("test.csv", header=False, index=False)
+
+s3_train_input = sagemaker_session.upload_data("train2.csv", bucket)
+s3_validation_input = sagemaker_session.upload_data("validation.csv", bucket)
+s3_test_input = sagemaker_session.upload_data("test.csv", bucket)
+
+image2 = "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.7-1"
+instance_type = "ml.m4.xlarge"
+instance_count = 1
+volume_size_in_gb = 30
+max_runtime_in_seconds = 600
+
+
+class TestSageMakerCore(unittest.TestCase):
+
+    def test_hyperparameter_tuning_job_and_transform_job(self):
+        ############ Create training jobs resource
+        job_name = "xgboost-churn-" + time.strftime(
+            "%Y-%m-%d-%H-%M-%S", time.gmtime()
+        )  # Name of training job
+        instance_type = "ml.m4.xlarge"  # SageMaker instance type to use for training
+        instance_count = 1  # Number of instances to use for training
+        volume_size_in_gb = 30  # Amount of storage to allocate to training job
+        max_runtime_in_seconds = 600  # Maximum runtimt. Job exits if it doesn't finish before this
+        s3_output_path = f"s3://{bucket}"  # bucket and optional prefix where the training job stores output artifacts, like model artifact.
+
+        hyper_parameters = {
+            "max_depth": "5",
+            "eta": "0.2",
+            "gamma": "4",
+            "min_child_weight": "6",
+            "subsample": "0.8",
+            "verbosity": "0",
+            "objective": "binary:logistic",
+            "num_round": "100",
+        }
+
+        training_job = TrainingJob.create(
+            training_job_name=job_name,
+            hyper_parameters=hyper_parameters,
+            algorithm_specification=AlgorithmSpecification(
+                training_image=image2, training_input_mode="File"
+            ),
+            role_arn=role,
+            input_data_config=[
+                Channel(
+                    channel_name="train",
+                    content_type="csv",
+                    data_source=DataSource(
+                        s3_data_source=S3DataSource(
+                            s3_data_type="S3Prefix",
+                            s3_uri=s3_train_input,
+                            s3_data_distribution_type="FullyReplicated",
+                        )
+                    ),
+                ),
+                Channel(
+                    channel_name="validation",
+                    content_type="csv",
+                    data_source=DataSource(
+                        s3_data_source=S3DataSource(
+                            s3_data_type="S3Prefix",
+                            s3_uri=s3_validation_input,
+                            s3_data_distribution_type="FullyReplicated",
+                        )
+                    ),
+                ),
+            ],
+            output_data_config=OutputDataConfig(s3_output_path=s3_output_path),
+            resource_config=ResourceConfig(
+                instance_type=instance_type,
+                instance_count=instance_count,
+                volume_size_in_gb=volume_size_in_gb,
+            ),
+            stopping_condition=StoppingCondition(max_runtime_in_seconds=max_runtime_in_seconds),
+        )
+
+        training_job.wait()
+
+        ########### Create and test HyperParameterTuningJob
+        tuning_job_name = "xgboost-tune-" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
+        max_number_of_training_jobs = 50
+        max_parallel_training_jobs = 5
+        max_runtime_in_seconds = 3600
+        s3_output_path = f"s3://{bucket}/tuningjob"
+
+        hyper_parameter_training_job_definition = HyperParameterTrainingJobDefinition(
+            role_arn=role,
+            algorithm_specification=HyperParameterAlgorithmSpecification(
+                training_image=image2, training_input_mode="File"
+            ),
+            input_data_config=[
+                Channel(
+                    channel_name="train",
+                    content_type="csv",
+                    data_source=DataSource(
+                        s3_data_source=S3DataSource(
+                            s3_data_type="S3Prefix",
+                            s3_uri=s3_train_input,
+                            s3_data_distribution_type="FullyReplicated",
+                        )
+                    ),
+                ),
+                Channel(
+                    channel_name="validation",
+                    content_type="csv",
+                    data_source=DataSource(
+                        s3_data_source=S3DataSource(
+                            s3_data_type="S3Prefix",
+                            s3_uri=s3_validation_input,
+                            s3_data_distribution_type="FullyReplicated",
+                        )
+                    ),
+                ),
+            ],
+            output_data_config=OutputDataConfig(s3_output_path=s3_output_path),
+            stopping_condition=StoppingCondition(max_runtime_in_seconds=max_runtime_in_seconds),
+            resource_config=ResourceConfig(
+                instance_type=instance_type,
+                instance_count=instance_count,
+                volume_size_in_gb=volume_size_in_gb,
+            ),
+        )
+
+        tuning_job_config = HyperParameterTuningJobConfig(
+            strategy="Bayesian",
+            hyper_parameter_tuning_job_objective=HyperParameterTuningJobObjective(
+                type="Maximize", metric_name="validation:auc"
+            ),
+            resource_limits=ResourceLimits(
+                max_number_of_training_jobs=max_number_of_training_jobs,
+                max_parallel_training_jobs=max_parallel_training_jobs,
+                max_runtime_in_seconds=3600,
+            ),
+            training_job_early_stopping_type="Auto",
+            parameter_ranges=ParameterRanges(
+                auto_parameters=[
+                    AutoParameter(name="max_depth", value_hint="5"),
+                    AutoParameter(name="eta", value_hint="0.1"),
+                    AutoParameter(name="gamma", value_hint="8"),
+                    AutoParameter(name="min_child_weight", value_hint="2"),
+                    AutoParameter(name="subsample", value_hint="0.5"),
+                    AutoParameter(name="num_round", value_hint="50"),
+                ]
+            ),
+        )
+
+        tuning_job = HyperParameterTuningJob.create(
+            hyper_parameter_tuning_job_name=tuning_job_name,
+            autotune=Autotune(mode="Enabled"),
+            training_job_definition=hyper_parameter_training_job_definition,
+            hyper_parameter_tuning_job_config=tuning_job_config,
+        )
+
+        tuning_job.wait()
+
+        fetch_tuning_job = HyperParameterTuningJob.get(
+            hyper_parameter_tuning_job_name=tuning_job_name
+        )
+        assert (
+            fetch_tuning_job.training_job_definition.output_data_config.s3_output_path
+            == s3_output_path
+        )
+        assert fetch_tuning_job.hyper_parameter_tuning_job_config.strategy == "Bayesian"
+
+        creation_time_after = datetime.datetime.now() - datetime.timedelta(days=5)
+
+        resource_iterator = HyperParameterTuningJob.get_all(creation_time_after=creation_time_after)
+        tuning_jobs = [job.hyper_parameter_tuning_job_name for job in resource_iterator]
+
+        assert len(tuning_jobs) > 0
+        assert tuning_job_name in tuning_jobs
+
+        ########### Create Model resource for transform job use
+        model_s3_uri = TrainingJob.get(
+            tuning_job.best_training_job.training_job_name
+        ).model_artifacts.s3_model_artifacts
+        model_name_for_tranformjob = (
+            f'customer-churn-xgboost-{time.strftime("%H-%M-%S", time.gmtime())}'
+        )
+        customer_churn_model = Model.create(
+            model_name=model_name_for_tranformjob,
+            primary_container=ContainerDefinition(image=image2, model_data_url=model_s3_uri),
+            execution_role_arn=role,
+        )
+
+        ########### Create and test Transform jobs
+        s3_output_path = f"s3://{bucket}/transform"
+        transform_job_name = "churn-prediction" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
+
+        transform_job = TransformJob.create(
+            transform_job_name=transform_job_name,
+            model_name=model_name_for_tranformjob,
+            transform_input=TransformInput(
+                data_source=TransformDataSource(
+                    s3_data_source=TransformS3DataSource(
+                        s3_data_type="S3Prefix", s3_uri=s3_test_input
+                    )
+                ),
+                content_type="text/csv",
+            ),
+            transform_output=TransformOutput(s3_output_path=s3_output_path),
+            transform_resources=TransformResources(
+                instance_type=instance_type, instance_count=instance_count
+            ),
+        )
+
+        transform_job.wait()
+
+        fetch_transform_job = TransformJob.get(transform_job_name=transform_job_name)
+        assert fetch_transform_job.transform_output.s3_output_path == s3_output_path
+
+        creation_time_after = datetime.datetime.now() - datetime.timedelta(days=5)
+
+        resource_iterator = TransformJob.get_all(creation_time_after=creation_time_after)
+        transform_jobs = [job.transform_job_name for job in resource_iterator]
+
+        assert len(transform_jobs) > 0
+        assert transform_job_name in transform_jobs