def single_variant_config():
config_resource_name = random_suffix_name("single-variant-config", 32)
model_resource_name = config_resource_name + "-model"
replacements = REPLACEMENT_VALUES.copy()
replacements["CONFIG_NAME"] = config_resource_name
replacements["MODEL_NAME"] = model_resource_name
model_reference, model_spec, model_resource = create_sagemaker_resource(
resource_plural=MODEL_RESOURCE_PLURAL,
resource_name=model_resource_name,
spec_file="xgboost_model",
replacements=replacements,
)
assert model_resource is not None
config_reference, config_spec, config_resource = create_sagemaker_resource(
resource_plural=CONFIG_RESOURCE_PLURAL,
resource_name=config_resource_name,
spec_file="endpoint_config_single_variant",
replacements=replacements,
)
assert config_resource is not None
yield (config_reference, config_resource)
k8s.delete_custom_resource(model_reference)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(config_reference):
k8s.delete_custom_resource(config_reference)
def faulty_config(name_suffix, single_container_model):
replacements = REPLACEMENT_VALUES.copy()
# copy model data to a temp S3 location and delete it after model is created on SageMaker
model_bucket = replacements["SAGEMAKER_DATA_BUCKET"]
copy_source = {
"Bucket": model_bucket,
"Key": "sagemaker/model/xgboost-mnist-model.tar.gz",
}
model_destination_key = "sagemaker/model/delete/xgboost-mnist-model.tar.gz"
s3.copy_object(model_bucket, copy_source, model_destination_key)
model_resource_name = name_suffix + "faulty-model"
replacements["MODEL_NAME"] = model_resource_name
replacements[
"MODEL_LOCATION"] = f"s3://{model_bucket}/{model_destination_key}"
model_reference, model_spec, model_resource = create_sagemaker_resource(
resource_plural=cfg.MODEL_RESOURCE_PLURAL,
resource_name=model_resource_name,
spec_file="xgboost_model_with_model_location",
replacements=replacements,
)
assert model_resource is not None
if k8s.get_resource_arn(model_resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {model_resource['status']}"
)
assert k8s.get_resource_arn(model_resource) is not None
s3.delete_object(model_bucket, model_destination_key)
config_resource_name = name_suffix + "-faulty-config"
(_, model_resource) = single_container_model
model_resource_name = model_resource["spec"].get("modelName", None)
replacements["ENDPOINT_CONFIG_NAME"] = config_resource_name
config_reference, config_spec, config_resource = create_sagemaker_resource(
resource_plural=cfg.ENDPOINT_CONFIG_RESOURCE_PLURAL,
resource_name=config_resource_name,
spec_file="endpoint_config_multi_variant",
replacements=replacements,
)
assert config_resource is not None
if k8s.get_resource_arn(config_resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {config_resource['status']}"
)
assert k8s.get_resource_arn(config_resource) is not None
yield (config_reference, config_resource)
for cr in (model_reference, config_reference):
_, deleted = k8s.delete_custom_resource(cr, 3, 10)
assert deleted
def xgboost_churn_data_quality_monitoring_schedule(
xgboost_churn_data_quality_job_definition,
):
(_, job_definition_resource) = xgboost_churn_data_quality_job_definition
job_definition_name = job_definition_resource["spec"].get("jobDefinitionName")
monitoring_schedule_name = random_suffix_name("monitoring-schedule", 32)
replacements = REPLACEMENT_VALUES.copy()
replacements["MONITORING_SCHEDULE_NAME"] = monitoring_schedule_name
replacements["JOB_DEFINITION_NAME"] = job_definition_name
replacements["MONITORING_TYPE"] = "DataQuality"
reference, spec, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=monitoring_schedule_name,
spec_file="monitoring_schedule_base",
replacements=replacements,
)
assert resource is not None
yield (reference, resource, spec)
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, cfg.DELETE_WAIT_PERIOD, cfg.DELETE_WAIT_LENGTH)
assert deleted
def xgboost_endpoint(name_suffix, single_variant_config):
endpoint_resource_name = name_suffix
(_, config_resource) = single_variant_config
config_resource_name = config_resource["spec"].get("endpointConfigName",
None)
replacements = REPLACEMENT_VALUES.copy()
replacements["ENDPOINT_NAME"] = endpoint_resource_name
replacements["ENDPOINT_CONFIG_NAME"] = config_resource_name
reference, spec, resource = create_sagemaker_resource(
resource_plural=cfg.ENDPOINT_RESOURCE_PLURAL,
resource_name=endpoint_resource_name,
spec_file="endpoint_base",
replacements=replacements,
)
assert resource is not None
yield (reference, resource, spec)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(reference):
# longer wait incase endpoint is in creating/updating status
_, deleted = k8s.delete_custom_resource(reference, 40,
cfg.DELETE_WAIT_LENGTH)
assert deleted
def xgboost_churn_data_quality_job_definition(xgboost_churn_endpoint):
endpoint_spec = xgboost_churn_endpoint
endpoint_name = endpoint_spec["spec"].get("endpointName")
resource_name = random_suffix_name("data-quality-job-definition", 32)
replacements = REPLACEMENT_VALUES.copy()
replacements["JOB_DEFINITION_NAME"] = resource_name
replacements["ENDPOINT_NAME"] = endpoint_name
job_definition_reference, _, resource = create_sagemaker_resource(
resource_plural=cfg.DATA_QUALITY_JOB_DEFINITION_RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="data_quality_job_definition_xgboost_churn",
replacements=replacements,
)
assert resource is not None
job_definition_name = resource["spec"].get("jobDefinitionName")
yield (job_definition_reference, resource)
if k8s.get_resource_exists(job_definition_reference):
_, deleted = k8s.delete_custom_resource(job_definition_reference, 3,
10)
assert deleted
def xgboost_training_job():
resource_name = random_suffix_name("xgboost-trainingjob", 32)
replacements = REPLACEMENT_VALUES.copy()
replacements["TRAINING_JOB_NAME"] = resource_name
reference, _, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="xgboost_trainingjob",
replacements=replacements,
)
assert resource is not None
if k8s.get_resource_arn(resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {resource['status']}"
)
assert k8s.get_resource_arn(resource) is not None
yield (reference, resource)
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference,
cfg.JOB_DELETE_WAIT_PERIODS,
cfg.JOB_DELETE_WAIT_LENGTH)
assert deleted
def xgboost_model_package_group():
resource_name = random_suffix_name("xgboost-model-package-group", 38)
replacements = REPLACEMENT_VALUES.copy()
replacements["MODEL_PACKAGE_GROUP_NAME"] = resource_name
(
model_package_group_reference,
model_package_group_spec,
model_package_group_resource,
) = create_sagemaker_resource(
resource_plural=cfg.MODEL_PACKAGE_GROUP_RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="xgboost_model_package_group",
replacements=replacements,
)
assert model_package_group_resource is not None
if k8s.get_resource_arn(model_package_group_resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {model_package_group_resource['status']}"
)
assert k8s.get_resource_arn(model_package_group_resource) is not None
yield (model_package_group_reference, model_package_group_resource)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(model_package_group_reference):
_, deleted = k8s.delete_custom_resource(model_package_group_reference,
DELETE_WAIT_PERIOD,
DELETE_WAIT_LENGTH)
assert deleted
def multi_variant_config(name_suffix, single_container_model):
config_resource_name = name_suffix + "-multi-variant-config"
(_, model_resource) = single_container_model
model_resource_name = model_resource["spec"].get("modelName", None)
replacements = REPLACEMENT_VALUES.copy()
replacements["ENDPOINT_CONFIG_NAME"] = config_resource_name
replacements["MODEL_NAME"] = model_resource_name
config_reference, config_spec, config_resource = create_sagemaker_resource(
resource_plural=cfg.ENDPOINT_CONFIG_RESOURCE_PLURAL,
resource_name=config_resource_name,
spec_file="endpoint_config_multi_variant",
replacements=replacements,
)
assert config_resource is not None
if k8s.get_resource_arn(config_resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {config_resource['status']}"
)
assert k8s.get_resource_arn(config_resource) is not None
yield (config_reference, config_resource)
_, deleted = k8s.delete_custom_resource(config_reference, 3, 10)
assert deleted
def xgboost_transformjob(xgboost_model_for_transform):
(transform_resource_name, model_resource_name) = xgboost_model_for_transform
replacements = REPLACEMENT_VALUES.copy()
replacements["MODEL_NAME"] = model_resource_name
replacements["TRANSFORM_JOB_NAME"] = transform_resource_name
reference, _, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=transform_resource_name,
spec_file="xgboost_transformjob",
replacements=replacements,
)
assert resource is not None
if k8s.get_resource_arn(resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {resource['status']}"
)
assert k8s.get_resource_arn(resource) is not None
yield (reference, resource)
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, cfg.JOB_DELETE_WAIT_PERIODS, cfg.JOB_DELETE_WAIT_LENGTH)
assert deleted
def faulty_config(name_suffix, single_container_model):
replacements = REPLACEMENT_VALUES.copy()
# copy model data to a temp S3 location and delete it after model is created on SageMaker
model_bucket = replacements["SAGEMAKER_DATA_BUCKET"]
copy_source = {
"Bucket": model_bucket,
"Key": "sagemaker/model/xgboost-mnist-model.tar.gz",
}
model_destination_key = "sagemaker/model/delete/xgboost-mnist-model.tar.gz"
s3.copy_object(model_bucket, copy_source, model_destination_key)
model_resource_name = name_suffix + "faulty-model"
replacements["MODEL_NAME"] = model_resource_name
replacements[
"MODEL_LOCATION"] = f"s3://{model_bucket}/{model_destination_key}"
model_reference, model_spec, model_resource = create_sagemaker_resource(
resource_plural=MODEL_RESOURCE_PLURAL,
resource_name=model_resource_name,
spec_file="xgboost_model_with_model_location",
replacements=replacements,
)
assert model_resource is not None
model_resource = k8s.get_resource(model_reference)
assert ("ackResourceMetadata" in model_resource["status"]
and "arn" in model_resource["status"]["ackResourceMetadata"])
s3.delete_object(model_bucket, model_destination_key)
config_resource_name = name_suffix + "-faulty-config"
(_, model_resource) = single_container_model
model_resource_name = model_resource["spec"].get("modelName", None)
replacements["CONFIG_NAME"] = config_resource_name
config_reference, config_spec, config_resource = create_sagemaker_resource(
resource_plural=CONFIG_RESOURCE_PLURAL,
resource_name=config_resource_name,
spec_file="endpoint_config_multi_variant",
replacements=replacements,
)
assert config_resource is not None
yield (config_reference, config_resource)
k8s.delete_custom_resource(model_reference)
k8s.delete_custom_resource(config_reference)
def apply_app_yaml(domain_id, user_profile_name):
replacements = REPLACEMENT_VALUES.copy()
replacements["DOMAIN_ID"] = domain_id
replacements["USER_PROFILE_NAME"] = user_profile_name
reference, spec, resource = create_sagemaker_resource(
resource_plural="apps",
resource_name="default",
spec_file="app",
replacements=replacements,
)
return reference, resource, spec
def apply_user_profile_yaml(resource_name, domain_id):
replacements = REPLACEMENT_VALUES.copy()
replacements["USER_PROFILE_NAME"] = resource_name
replacements["DOMAIN_ID"] = domain_id
reference, spec, resource = create_sagemaker_resource(
resource_plural="userprofiles",
resource_name=resource_name,
spec_file="user_profile",
replacements=replacements,
)
return reference, resource, spec
def apply_domain_yaml(resource_name):
replacements = REPLACEMENT_VALUES.copy()
replacements["DOMAIN_NAME"] = resource_name
replacements["VPC_ID"] = get_default_vpc()
replacements["SUBNET_ID"] = get_subnet(replacements["VPC_ID"])
reference, spec, resource = create_sagemaker_resource(
resource_plural="domains",
resource_name=resource_name,
spec_file="domain",
replacements=replacements,
)
return reference, resource, spec
def single_variant_config():
config_resource_name = random_suffix_name("single-variant-config", 32)
model_resource_name = config_resource_name + "-model"
replacements = REPLACEMENT_VALUES.copy()
replacements["ENDPOINT_CONFIG_NAME"] = config_resource_name
replacements["MODEL_NAME"] = model_resource_name
model_reference, model_spec, model_resource = create_sagemaker_resource(
resource_plural=cfg.MODEL_RESOURCE_PLURAL,
resource_name=model_resource_name,
spec_file="xgboost_model",
replacements=replacements,
)
assert model_resource is not None
if k8s.get_resource_arn(model_resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {model_resource['status']}"
)
assert k8s.get_resource_arn(model_resource) is not None
config_reference, config_spec, config_resource = create_sagemaker_resource(
resource_plural=cfg.ENDPOINT_CONFIG_RESOURCE_PLURAL,
resource_name=config_resource_name,
spec_file="endpoint_config_single_variant",
replacements=replacements,
)
assert config_resource is not None
yield (config_reference, config_resource)
k8s.delete_custom_resource(model_reference, 3, 10)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(config_reference):
_, deleted = k8s.delete_custom_resource(config_reference, 3, 10)
assert deleted
def single_container_model(name_suffix):
model_resource_name = name_suffix + "-model"
replacements = REPLACEMENT_VALUES.copy()
replacements["MODEL_NAME"] = model_resource_name
model_reference, model_spec, model_resource = create_sagemaker_resource(
resource_plural=MODEL_RESOURCE_PLURAL,
resource_name=model_resource_name,
spec_file="xgboost_model",
replacements=replacements,
)
assert model_resource is not None
yield (model_reference, model_resource)
k8s.delete_custom_resource(model_reference)
def notebook_instance_lifecycleConfig():
notebook_instance_lfc_name = random_suffix_name("notebookinstancelfc", 50)
replacements = REPLACEMENT_VALUES.copy()
replacements["NOTEBOOK_INSTANCE_LFC_NAME"] = notebook_instance_lfc_name
reference, spec, resource = create_sagemaker_resource(
resource_plural="notebookinstancelifecycleconfigs",
resource_name=notebook_instance_lfc_name,
spec_file="notebook_instance_lifecycle_config",
replacements=replacements,
)
assert resource is not None
yield (reference, resource, spec)
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, DELETE_WAIT_PERIOD,
DELETE_PERIOD_LENGTH)
assert deleted
def xgboost_training_job_debugger():
resource_name = random_suffix_name("xgboost-trainingjob-debugger", 50)
replacements = REPLACEMENT_VALUES.copy()
replacements["TRAINING_JOB_NAME"] = resource_name
reference, _, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="xgboost_trainingjob_debugger",
replacements=replacements,
)
assert resource is not None
yield (reference, resource)
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, 3, 10)
assert deleted
def xgboost_transformjob(sagemaker_client):
# Create model using boto3 for TransformJob
transform_model_file = (
f"s3://{get_bootstrap_resources().DataBucketName}/sagemaker/batch/model.tar.gz"
)
model_name = random_suffix_name("xgboost-model", 32)
create_response = sagemaker_client.create_model(
ModelName=model_name,
PrimaryContainer={
"Image": REPLACEMENT_VALUES["XGBOOST_IMAGE_URI"],
"ModelDataUrl": transform_model_file,
"Environment": {},
},
ExecutionRoleArn=REPLACEMENT_VALUES["SAGEMAKER_EXECUTION_ROLE_ARN"],
)
logging.debug(create_response)
# Check if the model is created successfully
describe_model_response = sagemaker_client.describe_model(
ModelName=model_name)
assert describe_model_response["ModelName"] is not None
resource_name = random_suffix_name("xgboost-transformjob", 32)
# Use the model created above
replacements = REPLACEMENT_VALUES.copy()
replacements["MODEL_NAME"] = model_name
replacements["TRANSFORM_JOB_NAME"] = resource_name
reference, spec, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="xgboost_transformjob",
replacements=replacements,
)
assert resource is not None
yield (reference, resource)
# Delete the model created
sagemaker_client.delete_model(ModelName=model_name)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(reference):
k8s.delete_custom_resource(reference)
def xgboost_hpojob():
resource_name = random_suffix_name("xgboost-hpojob", 32)
replacements = REPLACEMENT_VALUES.copy()
replacements["HPO_JOB_NAME"] = resource_name
reference, spec, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="xgboost_hpojob",
replacements=replacements,
)
assert resource is not None
yield (reference, resource)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(reference):
k8s.delete_custom_resource(reference)
def feature_group():
"""Creates a feature group from a SPEC_FILE."""
feature_group_name = random_suffix_name(RESOURCE_NAME_PREFIX, 32)
replacements = REPLACEMENT_VALUES.copy()
replacements["FEATURE_GROUP_NAME"] = feature_group_name
reference, spec, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=feature_group_name,
spec_file=SPEC_FILE,
replacements=replacements,
)
assert resource is not None
yield (reference, resource)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, WAIT_PERIOD_COUNT,
WAIT_PERIOD_LENGTH)
assert deleted
def xgboost_unversioned_model_package():
resource_name = random_suffix_name("xgboost-unversioned-model-package", 38)
replacements = REPLACEMENT_VALUES.copy()
replacements["MODEL_PACKAGE_NAME"] = resource_name
reference, _, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="xgboost_unversioned_model_package",
replacements=replacements,
)
assert resource is not None
yield (reference, resource)
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, DELETE_WAIT_PERIOD,
DELETE_WAIT_LENGTH)
assert deleted
def single_variant_config(name_suffix, single_container_model):
config_resource_name = name_suffix + "-single-variant-config"
(_, model_resource) = single_container_model
model_resource_name = model_resource["spec"].get("modelName", None)
replacements = REPLACEMENT_VALUES.copy()
replacements["CONFIG_NAME"] = config_resource_name
replacements["MODEL_NAME"] = model_resource_name
config_reference, config_spec, config_resource = create_sagemaker_resource(
resource_plural=CONFIG_RESOURCE_PLURAL,
resource_name=config_resource_name,
spec_file="endpoint_config_single_variant",
replacements=replacements,
)
assert config_resource is not None
yield (config_reference, config_resource)
k8s.delete_custom_resource(config_reference)
def notebook_instance():
default_code_repository = "https://github.com/aws-controllers-k8s/community"
resource_name = random_suffix_name("nb", 32)
replacements = REPLACEMENT_VALUES.copy()
replacements["NOTEBOOK_INSTANCE_NAME"] = resource_name
replacements["DEFAULT_CODE_REPOSITORY"] = default_code_repository
reference, spec, resource = create_sagemaker_resource(
resource_plural="notebookinstances",
resource_name=resource_name,
spec_file="notebook_instance",
replacements=replacements,
)
assert resource is not None
yield (reference, resource, spec)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, DELETE_WAIT_PERIOD,
DELETE_WAIT_LENGTH)
assert deleted
def xgboost_model_package_group():
resource_name = random_suffix_name("xgboost-model-package-group", 50)
replacements = REPLACEMENT_VALUES.copy()
replacements["MODEL_PACKAGE_GROUP_NAME"] = resource_name
reference, spec, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="xgboost_model_package_group",
replacements=replacements,
)
assert resource is not None
yield (reference, resource)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference,
cfg.DELETE_WAIT_PERIOD,
cfg.DELETE_WAIT_LENGTH)
assert deleted
def single_container_model(name_suffix):
model_resource_name = name_suffix + "-model"
replacements = REPLACEMENT_VALUES.copy()
replacements["MODEL_NAME"] = model_resource_name
model_reference, model_spec, model_resource = create_sagemaker_resource(
resource_plural=cfg.MODEL_RESOURCE_PLURAL,
resource_name=model_resource_name,
spec_file="xgboost_model",
replacements=replacements,
)
assert model_resource is not None
if k8s.get_resource_arn(model_resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {model_resource['status']}"
)
assert k8s.get_resource_arn(model_resource) is not None
yield (model_reference, model_resource)
_, deleted = k8s.delete_custom_resource(model_reference, 3, 10)
assert deleted
def xgboost_churn_model_bias_job_definition(xgboost_churn_endpoint):
endpoint_spec = xgboost_churn_endpoint
endpoint_name = endpoint_spec["spec"].get("endpointName")
job_definition_name = random_suffix_name("model-bias-job-definition", 32)
replacements = REPLACEMENT_VALUES.copy()
replacements["JOB_DEFINITION_NAME"] = job_definition_name
replacements["ENDPOINT_NAME"] = endpoint_name
reference, _, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=job_definition_name,
spec_file="model_bias_job_definition_xgboost_churn",
replacements=replacements,
)
assert resource is not None
yield (reference, resource)
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, 3, 10)
assert deleted
def cross_region_model():
resource_name = random_suffix_name("cross-region-model", 32)
region = get_cross_region()
replacements = REPLACEMENT_VALUES.copy()
replacements["MODEL_NAME"] = resource_name
replacements["REGION"] = region
replacements[
"XGBOOST_V1_IMAGE_URI"] = f"{XGBOOST_V1_IMAGE_URIS[region]}/xgboost:latest"
reference, spec, resource = create_sagemaker_resource(
resource_plural=cfg.MODEL_RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="cross_region_model",
replacements=replacements,
)
assert resource is not None
yield (reference, resource)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, 3, 10)
assert deleted
def xgboost_versioned_model_package(xgboost_model_package_group):
resource_name = random_suffix_name("xgboost-versioned-model-package", 38)
(_, model_package_group_resource) = xgboost_model_package_group
model_package_group_resource_name = model_package_group_resource[
"spec"].get("modelPackageGroupName", None)
replacements = REPLACEMENT_VALUES.copy()
replacements[
"MODEL_PACKAGE_GROUP_NAME"] = model_package_group_resource_name
replacements["MODEL_PACKAGE_RESOURCE_NAME"] = resource_name
reference, spec, resource = create_sagemaker_resource(
resource_plural=RESOURCE_PLURAL,
resource_name=resource_name,
spec_file="xgboost_versioned_model_package",
replacements=replacements,
)
assert resource is not None
yield (reference, spec, resource)
# Delete the k8s resource if not already deleted by tests
if k8s.get_resource_exists(reference):
_, deleted = k8s.delete_custom_resource(reference, DELETE_WAIT_PERIOD,
DELETE_WAIT_LENGTH)
assert deleted
def xgboost_churn_endpoint(sagemaker_client):
"""Creates a SageMaker endpoint with the XGBoost churn single-variant model
and data capture enabled.
"""
endpoint_resource_name = random_suffix_name("xgboost-churn", 32)
endpoint_config_resource_name = endpoint_resource_name + "-config"
model_resource_name = endpoint_config_resource_name + "-model"
replacements = REPLACEMENT_VALUES.copy()
replacements["ENDPOINT_NAME"] = endpoint_resource_name
replacements["ENDPOINT_CONFIG_NAME"] = endpoint_config_resource_name
replacements["MODEL_NAME"] = model_resource_name
data_bucket = replacements["SAGEMAKER_DATA_BUCKET"]
replacements[
"MODEL_LOCATION"] = f"s3://{data_bucket}/sagemaker/model/xgb-churn-prediction-model.tar.gz"
model_reference, model_spec, model_resource = create_sagemaker_resource(
resource_plural=cfg.MODEL_RESOURCE_PLURAL,
resource_name=model_resource_name,
spec_file="xgboost_model_with_model_location",
replacements=replacements,
)
assert model_resource is not None
if k8s.get_resource_arn(model_resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {model_resource['status']}"
)
assert k8s.get_resource_arn(model_resource) is not None
(
endpoint_config_reference,
endpoint_config_spec,
endpoint_config_resource,
) = create_sagemaker_resource(
resource_plural=cfg.ENDPOINT_CONFIG_RESOURCE_PLURAL,
resource_name=endpoint_config_resource_name,
spec_file="endpoint_config_data_capture_single_variant",
replacements=replacements,
)
assert endpoint_config_resource is not None
if k8s.get_resource_arn(endpoint_config_resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {endpoint_config_resource['status']}"
)
assert k8s.get_resource_arn(endpoint_config_resource) is not None
endpoint_reference, endpoint_spec, endpoint_resource = create_sagemaker_resource(
resource_plural=cfg.ENDPOINT_RESOURCE_PLURAL,
resource_name=endpoint_resource_name,
spec_file="endpoint_base",
replacements=replacements,
)
assert endpoint_resource is not None
if k8s.get_resource_arn(endpoint_resource) is None:
logging.error(
f"ARN for this resource is None, resource status is: {endpoint_resource['status']}"
)
assert k8s.get_resource_arn(endpoint_resource) is not None
wait_sagemaker_endpoint_status(replacements["ENDPOINT_NAME"], "InService")
yield endpoint_spec
for cr in (model_reference, endpoint_config_reference, endpoint_reference):
_, deleted = k8s.delete_custom_resource(cr, cfg.DELETE_WAIT_PERIOD,
cfg.DELETE_WAIT_LENGTH)
assert deleted
