def test_mlflow_model_logger_logging_deactivation_is_bool():
mlflow_model_logger_dataset = MlflowModelLoggerDataSet(
flavor="mlflow.sklearn")
with pytest.raises(ValueError,
match="_logging_activated must be a boolean"):
mlflow_model_logger_dataset._logging_activated = "hello"
def test_flavor_does_not_exists():
with pytest.raises(DataSetError, match="mlflow.whoops module not found"):
MlflowModelLoggerDataSet.from_config(
name="whoops",
config={
"type": "kedro_mlflow.io.models.MlflowModelLoggerDataSet",
"flavor": "mlflow.whoops",
},
)
def test_save_and_load_sklearn_flavor_without_run_id(tracking_uri,
mlflow_client,
linreg_model,
initial_active_run):
mlflow.set_tracking_uri(tracking_uri)
# close all opened mlflow runs to avoid interference between tests
while mlflow.active_run():
mlflow.end_run()
artifact_path = "my_linreg"
model_config = {
"name": "linreg",
"config": {
"type": "kedro_mlflow.io.models.MlflowModelLoggerDataSet",
"run_id": None,
"artifact_path": artifact_path,
"flavor": "mlflow.sklearn",
},
}
mlflow_model_ds = MlflowModelLoggerDataSet.from_config(**model_config)
# if no initial active run, "_save" triggers the run opening
if initial_active_run:
mlflow.start_run()
mlflow_model_ds.save(linreg_model)
current_run_id = mlflow.active_run().info.run_id
mlflow_client.list_artifacts(run_id=current_run_id)[0]
artifact = mlflow_client.list_artifacts(run_id=current_run_id)[0]
assert artifact.path == artifact_path
# the run_id is still opened
mlflow_model_ds = MlflowModelLoggerDataSet.from_config(**model_config)
linreg_model_loaded = mlflow_model_ds.load()
assert isinstance(linreg_model_loaded, LinearRegression)
assert pytest.approx(linreg_model_loaded.predict([[1, 2]])[0],
abs=10**(-14)) == 5
# load a second time after closing the active_run
mlflow.end_run()
model_config2 = model_config.copy()
model_config2["config"]["run_id"] = current_run_id
mlflow_model_ds2 = MlflowModelLoggerDataSet.from_config(**model_config2)
linreg_model_loaded2 = mlflow_model_ds2.load()
assert isinstance(linreg_model_loaded2, LinearRegression)
assert (pytest.approx(linreg_model_loaded2.predict([[1, 2]])[0],
abs=10**(-14)) == 5)
def test_save_sklearn_flavor_with_run_id_and_already_active_run(tracking_uri):
"""This test checks that saving a mlflow dataset must fail
if a run_id is specified but is different from the
mlflow.active_run()
"""
mlflow.set_tracking_uri(tracking_uri)
# close all opened mlflow runs to avoid interference between tests
while mlflow.active_run():
mlflow.end_run()
mlflow.start_run()
existing_run_id = mlflow.active_run().info.run_id
mlflow.end_run()
artifact_path = "my_linreg"
model_config = {
"name": "linreg",
"config": {
"type": "kedro_mlflow.io.models.MlflowModelLoggerDataSet",
"run_id": existing_run_id,
"artifact_path": artifact_path,
"flavor": "mlflow.sklearn",
},
}
mlflow_model_ds = MlflowModelLoggerDataSet.from_config(**model_config)
# if a run is active, it is impossible to log in another run
with mlflow.start_run():
with pytest.raises(
DataSetError,
match=
"'run_id' cannot be specified if there is an mlflow active run.",
):
mlflow_model_ds.save(linreg_model)
def test_pyfunc_flavor_python_model_save_and_load(
tmp_folder,
tracking_uri,
pipeline,
dummy_catalog,
):
kedro_pipeline_model = KedroPipelineModel(
pipeline=pipeline,
catalog=dummy_catalog,
input_name="raw_data",
)
artifacts = kedro_pipeline_model.extract_pipeline_artifacts(tmp_folder)
model_config = {
"name": "kedro_pipeline_model",
"config": {
"type": "kedro_mlflow.io.models.MlflowModelLoggerDataSet",
"flavor": "mlflow.pyfunc",
"pyfunc_workflow": "python_model",
"artifact_path": "test_model",
"save_args": {
"artifacts": artifacts,
"conda_env": {
"python": "3.7.0",
"dependencies": ["kedro==0.16.5"]
},
},
},
}
mlflow.set_tracking_uri(tracking_uri)
mlflow_model_ds = MlflowModelLoggerDataSet.from_config(**model_config)
mlflow_model_ds.save(kedro_pipeline_model)
current_run_id = mlflow.active_run().info.run_id
# close the run, create another dataset and reload
# (emulate a new "kedro run" with the launch of the )
mlflow.end_run()
model_config2 = model_config.copy()
model_config2["config"]["run_id"] = current_run_id
mlflow_model_ds2 = MlflowModelLoggerDataSet.from_config(**model_config2)
loaded_model = mlflow_model_ds2.load()
loaded_model.predict(pd.DataFrame(
data=[1], columns=["a"])) == pd.DataFrame(data=[2], columns=["a"])
def test_pyfunc_flavor_wrong_pyfunc_workflow(tracking_uri):
model_config = {
"name": "kedro_pipeline_model",
"config": {
"type": "kedro_mlflow.io.models.MlflowModelLoggerDataSet",
"flavor": "mlflow.pyfunc",
"pyfunc_workflow": "wrong_workflow",
"artifact_path": "test_model",
},
}
with pytest.raises(
DataSetError,
match=
r"PyFunc models require specifying `pyfunc_workflow` \(set to either `python_model` or `loader_module`\)",
):
MlflowModelLoggerDataSet.from_config(**model_config)
def test_save_and_load_sklearn_flavor_with_run_id(tracking_uri, mlflow_client,
linreg_model,
active_run_when_loading):
mlflow.set_tracking_uri(tracking_uri)
# close all opened mlflow runs to avoid interference between tests
while mlflow.active_run():
mlflow.end_run()
mlflow.start_run()
existing_run_id = mlflow.active_run().info.run_id
mlflow.end_run()
artifact_path = "my_linreg"
model_config = {
"name": "linreg",
"config": {
"type": "kedro_mlflow.io.models.MlflowModelLoggerDataSet",
"run_id": existing_run_id,
"artifact_path": artifact_path,
"flavor": "mlflow.sklearn",
},
}
mlflow_model_ds = MlflowModelLoggerDataSet.from_config(**model_config)
# "_save" opens, log and close the specified run
mlflow_model_ds.save(linreg_model)
mlflow_client.list_artifacts(run_id=existing_run_id)[0]
artifact = mlflow_client.list_artifacts(run_id=existing_run_id)[0]
assert artifact.path == artifact_path
if not active_run_when_loading:
mlflow.end_run()
mlflow_model_ds = MlflowModelLoggerDataSet.from_config(**model_config)
linreg_model_loaded = mlflow_model_ds.load()
assert isinstance(linreg_model_loaded, LinearRegression)
assert pytest.approx(linreg_model_loaded.predict([[1, 2]])[0],
abs=10**(-14)) == 5
def test_mlflow_model_logger_logging_deactivation(tracking_uri, linreg_model):
mlflow_model_logger_dataset = MlflowModelLoggerDataSet(
flavor="mlflow.sklearn")
mlflow.set_tracking_uri(tracking_uri)
mlflow_client = MlflowClient(tracking_uri=tracking_uri)
mlflow_model_logger_dataset._logging_activated = False
all_runs_id_beginning = set([
run.run_id for k in range(len(mlflow_client.list_experiments()))
for run in mlflow_client.list_run_infos(experiment_id=f"{k}")
])
mlflow_model_logger_dataset.save(linreg_model)
all_runs_id_end = set([
run.run_id for k in range(len(mlflow_client.list_experiments()))
for run in mlflow_client.list_run_infos(experiment_id=f"{k}")
])
assert all_runs_id_beginning == all_runs_id_end
def test_load_without_run_id_nor_active_run():
mlflow.set_tracking_uri(tracking_uri)
# close all opened mlflow runs to avoid interference between tests
while mlflow.active_run():
mlflow.end_run()
artifact_path = "my_linreg"
model_config = {
"name": "linreg",
"config": {
"type": "kedro_mlflow.io.models.MlflowModelLoggerDataSet",
"run_id": None,
"artifact_path": artifact_path,
"flavor": "mlflow.sklearn",
},
}
mlflow_model_ds = MlflowModelLoggerDataSet.from_config(**model_config)
with pytest.raises(
DataSetError,
match="To access the model_uri, you must either",
):
mlflow_model_ds.load()