[Bug Report] linear_learner_mnist_with_file_system_data_source notebook errors on subnet and security_group_id value type

[hrushig]

Link to the notebook Add the link to the notebook.

Describe the bug This code here when run errors out indicating invalid value type.

linear = sagemaker.estimator.Estimator(
    container,
    role,
    subnets=subnets,
    security_group_ids=security_groups_ids,
    train_instance_count=1,
    train_instance_type="ml.c4.xlarge",
    output_path=output_location,
    sagemaker_session=sess,
)

The solution to this error is to make subnets and security_group_ids as array. See the solution here:

linear = sagemaker.estimator.Estimator(
    container,
    role,
    subnets=[subnets],
    security_group_ids=[security_groups_ids],
    train_instance_count=1,
    train_instance_type="ml.c4.xlarge",
    output_path=output_location,
    sagemaker_session=sess,
)

To reproduce Run the above notebook using SageMaker Studio Notebook (Python 3 (Data Science)). Here is sdk version:

Name: sagemaker
Version: 1.72.1
---
Name: boto3
Version: 1.24.62
---
Name: botocore
Version: 1.27.62

Even after upgrading to v2 you get same error, but also train_* warnings.

Logs

Parameter image_name will be renamed to image_uri in SageMaker Python SDK v2.
---------------------------------------------------------------------------
ParamValidationError                      Traceback (most recent call last)
<ipython-input-49-8fc2f5ab22f6> in <module>
     17 linear.set_hyperparameters(feature_dim=784, predictor_type="binary_classifier", mini_batch_size=200)
     18 
---> 19 linear.fit({"train": file_system_input})

/opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in fit(self, inputs, wait, logs, job_name, experiment_config)
    494         self._prepare_for_training(job_name=job_name)
    495 
--> 496         self.latest_training_job = _TrainingJob.start_new(self, inputs, experiment_config)
    497         self.jobs.append(self.latest_training_job)
    498         if wait:

/opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in start_new(cls, estimator, inputs, experiment_config)
   1089             train_args["enable_sagemaker_metrics"] = estimator.enable_sagemaker_metrics
   1090 
-> 1091         estimator.sagemaker_session.train(**train_args)
   1092 
   1093         return cls(estimator.sagemaker_session, estimator._current_job_name)

/opt/conda/lib/python3.7/site-packages/sagemaker/session.py in train(self, input_mode, input_config, role, job_name, output_config, resource_config, vpc_config, hyperparameters, stop_condition, tags, metric_definitions, enable_network_isolation, image, algorithm_arn, encrypt_inter_container_traffic, train_use_spot_instances, checkpoint_s3_uri, checkpoint_local_path, experiment_config, debugger_rule_configs, debugger_hook_config, tensorboard_output_config, enable_sagemaker_metrics)
    588         LOGGER.info("Creating training-job with name: %s", job_name)
    589         LOGGER.debug("train request: %s", json.dumps(train_request, indent=4))
--> 590         self.sagemaker_client.create_training_job(**train_request)
    591 
    592     def process(

/opt/conda/lib/python3.7/site-packages/botocore/client.py in _api_call(self, *args, **kwargs)
    510                 )
    511             # The "self" in this scope is referring to the BaseClient.
--> 512             return self._make_api_call(operation_name, kwargs)
    513 
    514         _api_call.__name__ = str(py_operation_name)

/opt/conda/lib/python3.7/site-packages/botocore/client.py in _make_api_call(self, operation_name, api_params)
    881         }
    882         request_dict = self._convert_to_request_dict(
--> 883             api_params, operation_model, context=request_context
    884         )
    885         resolve_checksum_context(request_dict, operation_model, api_params)

/opt/conda/lib/python3.7/site-packages/botocore/client.py in _convert_to_request_dict(self, api_params, operation_model, context)
    942         )
    943         request_dict = self._serializer.serialize_to_request(
--> 944             api_params, operation_model
    945         )
    946         if not self._client_config.inject_host_prefix:

/opt/conda/lib/python3.7/site-packages/botocore/validate.py in serialize_to_request(self, parameters, operation_model)
    379             )
    380             if report.has_errors():
--> 381                 raise ParamValidationError(report=report.generate_report())
    382         return self._serializer.serialize_to_request(
    383             parameters, operation_model

ParamValidationError: Parameter validation failed:
Invalid type for parameter VpcConfig.Subnets, value: subnet-0af64674c564b501b, type: <class 'str'>, valid types: <class 'list'>, <class 'tuple'>
Invalid type for parameter VpcConfig.SecurityGroupIds, value: sg-0377a8692191789a7, type: <class 'str'>, valid types: <class 'list'>, <class 'tuple'>