def test_build_docker_with_env_override(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_with_env_override(
image_name, host_port, gunicorn_options)
_validate_with_rest_endpoint(scoring_proc, host_port, df, x, sk_model)
def test_build_docker_with_env_override(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_with_env_override(
image_name, host_port, gunicorn_options
)
_validate_with_rest_endpoint(scoring_proc, host_port, df, x, sk_model, enable_mlserver)