fix: hyperparameter tuning with spot instances and checkpoints

src/sagemaker/session.py

@@ -425,7 +425,7 @@ def compile_model(
         LOGGER.info("Creating compilation-job with name: %s", job_name)
         self.sagemaker_client.create_compilation_job(**compilation_job_request)
 
-    def tune(
+    def tune(  # noqa: C901
         self,
         job_name,
         strategy,
@@ -450,6 +450,9 @@ def tune(
         early_stopping_type="Off",
         encrypt_inter_container_traffic=False,
         vpc_config=None,
+        train_use_spot_instances=False,
+        checkpoint_s3_uri=None,
+        checkpoint_local_path=None,
     ):
         """Create an Amazon SageMaker hyperparameter tuning job
 
@@ -512,6 +515,18 @@ def tune(
                 The key in vpc_config is 'Subnets'.
                 * security_group_ids (list[str]): List of security group ids.
                 The key in vpc_config is 'SecurityGroupIds'.
+            train_use_spot_instances (bool): whether to use spot instances for training.
+            checkpoint_s3_uri (str): The S3 URI in which to persist checkpoints
+                that the algorithm persists (if any) during training. (default:
+                ``None``).
+            checkpoint_local_path (str): The local path that the algorithm
+                writes its checkpoints to. SageMaker will persist all files
+                under this path to `checkpoint_s3_uri` continually during
+                training. On job startup the reverse happens - data from the
+                s3 location is downloaded to this path before the algorithm is
+                started. If the path is unset then SageMaker assumes the
+                checkpoints will be provided under `/opt/ml/checkpoints/`.
+                (default: ``None``).
 
         """
         tune_request = {
@@ -569,6 +584,15 @@ def tune(
         if encrypt_inter_container_traffic:
             tune_request["TrainingJobDefinition"]["EnableInterContainerTrafficEncryption"] = True
 
+        if train_use_spot_instances:
+            tune_request["TrainingJobDefinition"]["EnableManagedSpotTraining"] = True
+
+        if checkpoint_s3_uri:
+            checkpoint_config = {"S3Uri": checkpoint_s3_uri}
+            if checkpoint_local_path:
+                checkpoint_config["LocalPath"] = checkpoint_local_path
+            tune_request["TrainingJobDefinition"]["CheckpointConfig"] = checkpoint_config
+
         LOGGER.info("Creating hyperparameter tuning job with name: %s", job_name)
         LOGGER.debug("tune request: %s", json.dumps(tune_request, indent=4))
         self.sagemaker_client.create_hyper_parameter_tuning_job(**tune_request)

src/sagemaker/tuner.py

@@ -890,6 +890,10 @@ def start_new(cls, tuner, inputs):
             "encrypt_inter_container_traffic"
         ] = tuner.estimator.encrypt_inter_container_traffic
 
+        tuner_args["train_use_spot_instances"] = tuner.estimator.train_use_spot_instances
+        tuner_args["checkpoint_s3_uri"] = tuner.estimator.checkpoint_s3_uri
+        tuner_args["checkpoint_local_path"] = tuner.estimator.checkpoint_local_path
+
         tuner.estimator.sagemaker_session.tune(**tuner_args)
 
         return cls(tuner.sagemaker_session, tuner._current_job_name)

tests/unit/test_session.py

@@ -565,6 +565,51 @@ def assert_create_tuning_job_request(**kwrags):
     )
 
 
+def test_tune_with_spot_and_checkpoints(sagemaker_session):
+    def assert_create_tuning_job_request(**kwrags):
+        assert (
+            kwrags["HyperParameterTuningJobConfig"]
+            == SAMPLE_TUNING_JOB_REQUEST["HyperParameterTuningJobConfig"]
+        )
+        assert kwrags["HyperParameterTuningJobName"] == "dummy-tuning-1"
+        assert kwrags["TrainingJobDefinition"]["EnableManagedSpotTraining"] is True
+        assert (
+            kwrags["TrainingJobDefinition"]["CheckpointConfig"]["S3Uri"]
+            == "s3://mybucket/checkpoints/"
+        )
+        assert (
+            kwrags["TrainingJobDefinition"]["CheckpointConfig"]["LocalPath"] == "/tmp/checkpoints"
+        )
+        assert kwrags.get("WarmStartConfig", None) is None
+
+    sagemaker_session.sagemaker_client.create_hyper_parameter_tuning_job.side_effect = (
+        assert_create_tuning_job_request
+    )
+    sagemaker_session.tune(
+        job_name="dummy-tuning-1",
+        strategy="Bayesian",
+        objective_type="Maximize",
+        objective_metric_name="val-score",
+        max_jobs=100,
+        max_parallel_jobs=5,
+        parameter_ranges=SAMPLE_PARAM_RANGES,
+        static_hyperparameters=STATIC_HPs,
+        image="dummy-image-1",
+        input_mode="File",
+        metric_definitions=SAMPLE_METRIC_DEF,
+        role=EXPANDED_ROLE,
+        input_config=SAMPLE_INPUT,
+        output_config=SAMPLE_OUTPUT,
+        resource_config=RESOURCE_CONFIG,
+        stop_condition=SAMPLE_STOPPING_CONDITION,
+        tags=None,
+        warm_start_config=None,
+        train_use_spot_instances=True,
+        checkpoint_s3_uri="s3://mybucket/checkpoints/",
+        checkpoint_local_path="/tmp/checkpoints",
+    )
+
+
 def test_stop_tuning_job(sagemaker_session):
     sms = sagemaker_session
     sms.sagemaker_client.stop_hyper_parameter_tuning_job = Mock(