def test_jumpstart_inference_model_class(setup):
model_id, model_version = "catboost-classification-model", "1.0.0"
instance_type, instance_count = "ml.m5.xlarge", 1
print("Starting inference...")
image_uri = image_uris.retrieve(
region=None,
framework=None,
image_scope="inference",
model_id=model_id,
model_version=model_version,
instance_type=instance_type,
)
script_uri = script_uris.retrieve(model_id=model_id,
model_version=model_version,
script_scope="inference")
model_uri = model_uris.retrieve(model_id=model_id,
model_version=model_version,
model_scope="inference")
model = Model(
image_uri=image_uri,
model_data=model_uri,
source_dir=script_uri,
entry_point=INFERENCE_ENTRY_POINT_SCRIPT_NAME,
role=get_sm_session().get_caller_identity_arn(),
sagemaker_session=get_sm_session(),
enable_network_isolation=True,
)
model.deploy(
initial_instance_count=instance_count,
instance_type=instance_type,
tags=[{
"Key": JUMPSTART_TAG,
"Value": os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]
}],
)
endpoint_invoker = EndpointInvoker(endpoint_name=model.endpoint_name, )
download_inference_assets()
ground_truth_label, features = get_tabular_data(
InferenceTabularDataname.MULTICLASS)
response = endpoint_invoker.invoke_tabular_endpoint(features)
assert response is not None
def __init__(
self,
image_uri,
script_uri,
model_uri,
hyperparameters,
instance_type,
training_dataset_s3_key,
suffix=time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime()),
region=JUMPSTART_DEFAULT_REGION_NAME,
boto_config=Config(retries={
"max_attempts": 10,
"mode": "standard"
}),
base_name="jumpstart-training-job",
execution_role=None,
) -> None:
self.account_id = boto3.client("sts").get_caller_identity()["Account"]
self.suffix = suffix
self.test_suite_id = os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]
self.region = region
self.config = boto_config
self.base_name = base_name
self.execution_role = execution_role or get_sm_session(
).get_caller_identity_arn()
self.image_uri = image_uri
self.script_uri = script_uri
self.model_uri = model_uri
self.hyperparameters = hyperparameters
self.instance_type = instance_type
self.training_dataset_s3_key = training_dataset_s3_key
self.sagemaker_client = self.get_sagemaker_client()
def package_artifacts(self):
self.model_name = self.get_model_name()
cache_bucket_uri = f"s3://{get_test_artifact_bucket()}"
repacked_model_uri = "/".join([
cache_bucket_uri,
self.test_suite_id,
"inference_model_tarballs",
self.model_name,
"repacked_model.tar.gz",
])
repack_model(
inference_script="inference.py",
source_directory=self.script_uri,
dependencies=None,
model_uri=self.model_uri,
repacked_model_uri=repacked_model_uri,
sagemaker_session=get_sm_session(),
kms_key=None,
)
return repacked_model_uri
def test_jumpstart_transfer_learning_estimator_class(setup):
model_id, model_version = "huggingface-spc-bert-base-cased", "1.0.0"
training_instance_type = "ml.p3.2xlarge"
inference_instance_type = "ml.p2.xlarge"
instance_count = 1
print("Starting training...")
image_uri = image_uris.retrieve(
region=None,
framework=None,
image_scope="training",
model_id=model_id,
model_version=model_version,
instance_type=training_instance_type,
)
script_uri = script_uris.retrieve(
model_id=model_id, model_version=model_version, script_scope="training"
)
model_uri = model_uris.retrieve(
model_id=model_id, model_version=model_version, model_scope="training"
)
default_hyperparameters = hyperparameters.retrieve_default(
model_id=model_id,
model_version=model_version,
)
default_hyperparameters["epochs"] = "1"
estimator = Estimator(
image_uri=image_uri,
source_dir=script_uri,
model_uri=model_uri,
entry_point=TRAINING_ENTRY_POINT_SCRIPT_NAME,
role=get_sm_session().get_caller_identity_arn(),
sagemaker_session=get_sm_session(),
enable_network_isolation=True,
hyperparameters=default_hyperparameters,
tags=[{"Key": JUMPSTART_TAG, "Value": os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]}],
instance_count=instance_count,
instance_type=training_instance_type,
)
estimator.fit(
{
"training": f"s3://{get_jumpstart_content_bucket(JUMPSTART_DEFAULT_REGION_NAME)}/"
f"{get_training_dataset_for_model_and_version(model_id, model_version)}",
}
)
print("Starting inference...")
image_uri = image_uris.retrieve(
region=None,
framework=None,
image_scope="inference",
model_id=model_id,
model_version=model_version,
instance_type=inference_instance_type,
)
script_uri = script_uris.retrieve(
model_id=model_id, model_version=model_version, script_scope="inference"
)
model_uri = model_uris.retrieve(
model_id=model_id, model_version=model_version, model_scope="inference"
)
endpoint_name = name_from_base(f"{model_id}-transfer-learning")
estimator.deploy(
initial_instance_count=instance_count,
instance_type=inference_instance_type,
entry_point=INFERENCE_ENTRY_POINT_SCRIPT_NAME,
image_uri=image_uri,
source_dir=script_uri,
endpoint_name=endpoint_name,
)
endpoint_invoker = EndpointInvoker(
endpoint_name=endpoint_name,
)
response = endpoint_invoker.invoke_spc_endpoint(["hello", "world"])
assert response is not None