def test_build_docker(iris_data, sk_model):
with mlflow.start_run() as active_run:
mlflow.sklearn.log_model(sk_model, "model")
model_uri = "runs:/{run_id}/model".format(run_id=active_run.info.run_id)
x, _ = iris_data
df = pd.DataFrame(x)
image_name = pyfunc_build_image(model_uri, extra_args=["--install-mlflow"])
host_port = get_safe_port()
scoring_proc = pyfunc_serve_from_docker_image(image_name, host_port)
_validate_with_rest_endpoint(scoring_proc, host_port, df, x, sk_model)
def test_build_docker_virtualenv(iris_data, sk_model):
with mlflow.start_run():
model_info = mlflow.sklearn.log_model(sk_model, "model")
x, _ = iris_data
df = pd.DataFrame(iris_data[0])
extra_args = ["--install-mlflow", "--env-manager", "virtualenv"]
image_name = pyfunc_build_image(model_info.model_uri,
extra_args=extra_args)
host_port = get_safe_port()
scoring_proc = pyfunc_serve_from_docker_image(image_name, host_port)
_validate_with_rest_endpoint(scoring_proc, host_port, df, x, sk_model)
def test_build_docker(iris_data, sk_model, enable_mlserver):
with mlflow.start_run() as active_run:
if enable_mlserver:
# MLServer requires Python 3.7, so we'll force that Python version
with mock.patch("mlflow.utils.environment.PYTHON_VERSION", "3.7"):
mlflow.sklearn.log_model(sk_model, "model")
else:
mlflow.sklearn.log_model(sk_model, "model")
model_uri = "runs:/{run_id}/model".format(run_id=active_run.info.run_id)
x, _ = iris_data
df = pd.DataFrame(x)
extra_args = ["--install-mlflow"]
if enable_mlserver:
extra_args.append("--enable-mlserver")
image_name = pyfunc_build_image(model_uri, extra_args=extra_args)
host_port = get_safe_port()
scoring_proc = pyfunc_serve_from_docker_image(image_name, host_port)
_validate_with_rest_endpoint(scoring_proc, host_port, df, x, sk_model, enable_mlserver)
def test_build_docker(iris_data, sk_model):
with mlflow.start_run() as active_run:
mlflow.sklearn.log_model(sk_model, "model")
model_uri = "runs:/{run_id}/model".format(
run_id=active_run.info.run_id)
x, _ = iris_data
df = pd.DataFrame(x)
image_name = pyfunc_build_image(model_uri, extra_args=["--install-mlflow"])
host_port = get_safe_port()
scoring_proc = pyfunc_serve_from_docker_image(image_name, host_port)
with RestEndpoint(proc=scoring_proc, port=host_port) as endpoint:
for content_type in [
CONTENT_TYPE_JSON_SPLIT_ORIENTED, CONTENT_TYPE_CSV,
CONTENT_TYPE_JSON
]:
scoring_response = endpoint.invoke(df, content_type)
assert scoring_response.status_code == 200, "Failed to serve prediction, got " \
"response %s" % scoring_response.text
np.testing.assert_array_equal(
np.array(json.loads(scoring_response.text)),
sk_model.predict(x))
# Try examples of bad input, verify we get a non-200 status code
for content_type in [
CONTENT_TYPE_JSON_SPLIT_ORIENTED, CONTENT_TYPE_CSV,
CONTENT_TYPE_JSON
]:
scoring_response = endpoint.invoke(data="",
content_type=content_type)
assert scoring_response.status_code == 500, \
"Expected server failure with error code 500, got response with status code %s " \
"and body %s" % (scoring_response.status_code, scoring_response.text)
scoring_response_dict = json.loads(scoring_response.content)
assert "error_code" in scoring_response_dict
assert scoring_response_dict["error_code"] == ErrorCode.Name(
MALFORMED_REQUEST)
assert "message" in scoring_response_dict
assert "stack_trace" in scoring_response_dict
