def test_run_databricks_validations(
tmpdir, cluster_spec_mock, # pylint: disable=unused-argument
tracking_uri_mock, dbfs_mocks, set_tag_mock): # pylint: disable=unused-argument
"""
Tests that running on Databricks fails before making any API requests if validations fail.
"""
with mock.patch.dict(os.environ, {'DATABRICKS_HOST': 'test-host', 'DATABRICKS_TOKEN': 'foo'}),\
mock.patch("mlflow.projects.databricks.DatabricksJobRunner._databricks_api_request")\
as db_api_req_mock:
# Test bad tracking URI
tracking_uri_mock.return_value = tmpdir.strpath
with pytest.raises(ExecutionException):
run_databricks_project(cluster_spec_mock, synchronous=True)
assert db_api_req_mock.call_count == 0
db_api_req_mock.reset_mock()
mlflow_service = mlflow.tracking.MlflowClient()
assert (len(mlflow_service.list_run_infos(experiment_id=FileStore.DEFAULT_EXPERIMENT_ID))
== 0)
tracking_uri_mock.return_value = "http://"
# Test misspecified parameters
with pytest.raises(ExecutionException):
mlflow.projects.run(
TEST_PROJECT_DIR, backend="databricks", entry_point="greeter",
backend_config=cluster_spec_mock)
assert db_api_req_mock.call_count == 0
db_api_req_mock.reset_mock()
# Test bad cluster spec
with pytest.raises(ExecutionException):
mlflow.projects.run(TEST_PROJECT_DIR, backend="databricks", synchronous=True,
backend_config=None)
assert db_api_req_mock.call_count == 0
db_api_req_mock.reset_mock()
# Test that validations pass with good tracking URIs
databricks.before_run_validations("http://", cluster_spec_mock)
databricks.before_run_validations("databricks", cluster_spec_mock)
def test_run_databricks_validations(
tmpdir,
cluster_spec_mock,
dbfs_mocks,
set_tag_mock,
): # pylint: disable=unused-argument
"""
Tests that running on Databricks fails before making any API requests if validations fail.
"""
with mock.patch.dict(os.environ, {
"DATABRICKS_HOST": "test-host",
"DATABRICKS_TOKEN": "foo"
}), mock.patch(
"mlflow.projects.databricks.DatabricksJobRunner._databricks_api_request"
) as db_api_req_mock:
# Test bad tracking URI
mlflow.set_tracking_uri(tmpdir.strpath)
with pytest.raises(ExecutionException,
match="MLflow tracking URI must be of"):
run_databricks_project(cluster_spec_mock, synchronous=True)
assert db_api_req_mock.call_count == 0
db_api_req_mock.reset_mock()
mlflow_service = mlflow.tracking.MlflowClient()
assert (len(
mlflow_service.list_run_infos(
experiment_id=FileStore.DEFAULT_EXPERIMENT_ID)) == 0)
mlflow.set_tracking_uri("databricks")
# Test misspecified parameters
with pytest.raises(
ExecutionException,
match="No value given for missing parameters: 'name'"):
mlflow.projects.run(
TEST_PROJECT_DIR,
backend="databricks",
entry_point="greeter",
backend_config=cluster_spec_mock,
)
assert db_api_req_mock.call_count == 0
db_api_req_mock.reset_mock()
# Test bad cluster spec
with pytest.raises(ExecutionException,
match="Backend spec must be provided"):
mlflow.projects.run(TEST_PROJECT_DIR,
backend="databricks",
synchronous=True,
backend_config=None)
assert db_api_req_mock.call_count == 0
db_api_req_mock.reset_mock()
# Test that validations pass with good tracking URIs
databricks.before_run_validations("http://", cluster_spec_mock)
databricks.before_run_validations("databricks", cluster_spec_mock)