def test_list_environment(url, mock_oauth, use_google_oauth):
merlin.set_url(url, use_google_oauth=use_google_oauth)
_mock_list_environment_call()
envs = merlin.list_environment()
assert len(envs) == 2
assert envs[0].name == env_1.name
assert envs[1].name == env_2.name
def test_resource_request(integration_test_url, project_name,
use_google_oauth):
merlin.set_url(integration_test_url, use_google_oauth=use_google_oauth)
merlin.set_project(project_name)
merlin.set_model("resource-request", ModelType.XGBOOST)
model_dir = "test/xgboost-model"
BST_FILE = "model.bst"
envs = merlin.list_environment()
assert len(envs) >= 1
default_env = merlin.get_default_environment()
assert default_env is not None
undeploy_all_version()
with merlin.new_model_version() as v:
iris = load_iris()
y = iris['target']
X = iris['data']
dtrain = xgb.DMatrix(X, label=y)
param = {
'max_depth': 6,
'eta': 0.1,
'silent': 1,
'nthread': 4,
'num_class': 10,
'objective': 'multi:softmax'
}
xgb_model = xgb.train(params=param, dtrain=dtrain)
model_file = os.path.join(model_dir, BST_FILE)
xgb_model.save_model(model_file)
# Upload the serialized model to MLP
merlin.log_model(model_dir=model_dir)
resource_request = ResourceRequest(1, 1, "100m", "200Mi")
endpoint = merlin.deploy(v,
environment_name=default_env.name,
resource_request=resource_request)
sleep(5)
resp = requests.post(f"{endpoint.url}", json=request_json)
assert resp.status_code == 200
assert resp.json() is not None
assert len(resp.json()['predictions']) == len(request_json['instances'])
merlin.undeploy(v)
sleep(5)
resp = requests.post(f"{endpoint.url}", json=request_json)
assert resp.status_code == 404