def test_model(sagemaker_session):
model = XGBoostModel(
"s3://some/data.tar.gz",
role=ROLE,
framework_version=XGBOOST_LATEST_VERSION,
entry_point=SCRIPT_PATH,
sagemaker_session=sagemaker_session,
)
predictor = model.deploy(1, CPU)
assert isinstance(predictor, XGBoostPredictor)
def main():
print('Starting model training.')
print('Note: if launching for the first time in local mode, container image download might take a few minutes to complete.')
hyperparameters = {
"max_depth": "5",
"eta": "0.2",
"gamma": "4",
"min_child_weight": "6",
"subsample": "0.7",
"objective": "reg:squarederror",
"num_round": "50",
"verbosity": "2",
}
xgb_script_mode_estimator = XGBoost(
entry_point="./code/abalone.py",
hyperparameters=hyperparameters,
role=DUMMY_IAM_ROLE,
instance_count=1,
instance_type='local',
framework_version="1.2-1"
)
train_input = TrainingInput("file://./data/train/abalone", content_type="text/libsvm")
xgb_script_mode_estimator.fit({"train": train_input, "validation": train_input})
print('Completed model training')
model_data = xgb_script_mode_estimator.model_data
print(model_data)
xgb_inference_model = XGBoostModel(
model_data=model_data,
role=DUMMY_IAM_ROLE,
entry_point="./code/inference.py",
framework_version="1.2-1",
)
print('Deploying endpoint in local mode')
predictor = xgb_inference_model.deploy(
initial_instance_count=1,
instance_type="local",
)
a_young_abalone = "6 1:3 2:0.37 3:0.29 4:0.095 5:0.249 6:0.1045 7:0.058 8:0.067"
do_inference_on_local_endpoint(predictor, a_young_abalone)
an_old_abalone = "15 1:1 2:0.655 3:0.53 4:0.175 5:1.2635 6:0.486 7:0.2635 8:0.415"
do_inference_on_local_endpoint(predictor, an_old_abalone)
print('About to delete the endpoint to stop paying (if in cloud mode).')
predictor.delete_endpoint(predictor.endpoint_name)
def test_xgboost_serverless_inference(
xgboost_training_job,
sagemaker_session,
xgboost_latest_version,
):
endpoint_name = unique_name_from_base(
"test-xgboost-deploy-model-serverless")
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
desc = sagemaker_session.sagemaker_client.describe_training_job(
TrainingJobName=xgboost_training_job)
model_data = desc["ModelArtifacts"]["S3ModelArtifacts"]
xgboost = XGBoostModel(
sagemaker_session=sagemaker_session,
model_data=model_data,
role=ROLE,
entry_point=os.path.join(DATA_DIR, "xgboost_abalone",
"abalone.py"),
framework_version=xgboost_latest_version,
)
xgboost.deploy(serverless_inference_config=ServerlessInferenceConfig(),
endpoint_name=endpoint_name)
def test_model_custom_serialization(sagemaker_session,
xgboost_framework_version):
model = XGBoostModel(
"s3://some/data.tar.gz",
role=ROLE,
framework_version=xgboost_framework_version,
entry_point=SCRIPT_PATH,
sagemaker_session=sagemaker_session,
)
custom_serializer = Mock()
custom_deserializer = Mock()
predictor = model.deploy(
1,
CPU,
serializer=custom_serializer,
deserializer=custom_deserializer,
)
assert isinstance(predictor, XGBoostPredictor)
assert predictor.serializer is custom_serializer
assert predictor.deserializer is custom_deserializer