def test_jumpstart_pytorch_script_uri(patched_get_model_specs):
patched_get_model_specs.side_effect = get_prototype_model_spec
# inference
uri = script_uris.retrieve(
region="us-west-2",
script_scope="inference",
model_id="pytorch-eqa-bert-base-cased",
model_version="*",
)
assert (
uri ==
"s3://jumpstart-cache-prod-us-west-2/source-directory-tarballs/pytorch/"
"inference/eqa/v1.0.0/sourcedir.tar.gz")
# training
uri = script_uris.retrieve(
region="us-west-2",
script_scope="training",
model_id="pytorch-eqa-bert-base-cased",
model_version="*",
)
assert (
uri ==
"s3://jumpstart-cache-prod-us-west-2/source-directory-tarballs/pytorch/"
"transfer_learning/eqa/v1.0.0/sourcedir.tar.gz")
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 test_jumpstart_inference_retrieve_functions(setup):
model_id, model_version = "catboost-classification-model", "1.0.0"
instance_type = "ml.m5.xlarge"
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"
)
environment_vars = environment_variables.retrieve_default(
model_id=model_id, model_version=model_version
)
inference_job = InferenceJobLauncher(
image_uri=image_uri,
script_uri=script_uri,
model_uri=model_uri,
instance_type=instance_type,
base_name="catboost",
environment_variables=environment_vars,
)
inference_job.launch_inference_job()
inference_job.wait_until_endpoint_in_service()
endpoint_invoker = EndpointInvoker(
endpoint_name=inference_job.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 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
def test_jumpstart_transfer_learning_retrieve_functions(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"
# training
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, include_container_hyperparameters=True
)
default_hyperparameters["epochs"] = "1"
training_job = TrainingJobLauncher(
image_uri=image_uri,
script_uri=script_uri,
model_uri=model_uri,
hyperparameters=default_hyperparameters,
instance_type=training_instance_type,
training_dataset_s3_key=get_training_dataset_for_model_and_version(model_id, model_version),
base_name="huggingface",
)
training_job.create_training_job()
training_job.wait_until_training_job_complete()
# inference
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"
)
environment_vars = environment_variables.retrieve_default(
model_id=model_id, model_version=model_version
)
inference_job = InferenceJobLauncher(
image_uri=image_uri,
script_uri=script_uri,
model_uri=get_model_tarball_full_uri_from_base_uri(
training_job.output_tarball_base_path, training_job.training_job_name
),
instance_type=inference_instance_type,
base_name="huggingface",
environment_variables=environment_vars,
)
inference_job.launch_inference_job()
inference_job.wait_until_endpoint_in_service()
endpoint_invoker = EndpointInvoker(
endpoint_name=inference_job.endpoint_name,
)
response = endpoint_invoker.invoke_spc_endpoint(["hello", "world"])
assert response is not None
def test_jumpstart_common_script_uri(
patched_get_model_specs, patched_verify_model_region_and_return_specs):
patched_verify_model_region_and_return_specs.side_effect = verify_model_region_and_return_specs
patched_get_model_specs.side_effect = get_spec_from_base_spec
script_uris.retrieve(
script_scope="training",
model_id="pytorch-ic-mobilenet-v2",
model_version="*",
)
patched_get_model_specs.assert_called_once_with(
region=sagemaker_constants.JUMPSTART_DEFAULT_REGION_NAME,
model_id="pytorch-ic-mobilenet-v2",
version="*",
)
patched_verify_model_region_and_return_specs.assert_called_once()
patched_get_model_specs.reset_mock()
patched_verify_model_region_and_return_specs.reset_mock()
script_uris.retrieve(
script_scope="inference",
model_id="pytorch-ic-mobilenet-v2",
model_version="1.*",
)
patched_get_model_specs.assert_called_once_with(
region=sagemaker_constants.JUMPSTART_DEFAULT_REGION_NAME,
model_id="pytorch-ic-mobilenet-v2",
version="1.*",
)
patched_verify_model_region_and_return_specs.assert_called_once()
patched_get_model_specs.reset_mock()
patched_verify_model_region_and_return_specs.reset_mock()
script_uris.retrieve(
region="us-west-2",
script_scope="training",
model_id="pytorch-ic-mobilenet-v2",
model_version="*",
)
patched_get_model_specs.assert_called_once_with(
region="us-west-2", model_id="pytorch-ic-mobilenet-v2", version="*")
patched_verify_model_region_and_return_specs.assert_called_once()
patched_get_model_specs.reset_mock()
patched_verify_model_region_and_return_specs.reset_mock()
script_uris.retrieve(
region="us-west-2",
script_scope="inference",
model_id="pytorch-ic-mobilenet-v2",
model_version="1.*",
)
patched_get_model_specs.assert_called_once_with(
region="us-west-2", model_id="pytorch-ic-mobilenet-v2", version="1.*")
patched_verify_model_region_and_return_specs.assert_called_once()
with pytest.raises(NotImplementedError):
script_uris.retrieve(
region="us-west-2",
script_scope="BAD_SCOPE",
model_id="pytorch-ic-mobilenet-v2",
model_version="*",
)
with pytest.raises(KeyError):
script_uris.retrieve(
region="us-west-2",
script_scope="training",
model_id="blah",
model_version="*",
)
with pytest.raises(ValueError):
script_uris.retrieve(
region="mars-south-1",
script_scope="training",
model_id="pytorch-ic-mobilenet-v2",
model_version="*",
)
with pytest.raises(ValueError):
script_uris.retrieve(
model_id="pytorch-ic-mobilenet-v2",
model_version="*",
)
with pytest.raises(ValueError):
script_uris.retrieve(
script_scope="training",
model_version="*",
)
with pytest.raises(ValueError):
script_uris.retrieve(
script_scope="training",
model_id="pytorch-ic-mobilenet-v2",
)