code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def test_create_logged_models_with_params(
monkeypatch, params_count, expected_call_count, create_batch_size, log_batch_size
):
"""Test creating logged models with parameters."""
# Set environment variables using monkeypatch
monkeypatch.setenv(_MLFLOW_CREATE_LOGGED_MODEL_PARAMS_BATCH_SIZE.name, create_b... | Test creating logged models with parameters. | test_create_logged_models_with_params | python | mlflow/mlflow | tests/store/tracking/test_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/tracking/test_rest_store.py | Apache-2.0 |
def test_log_metric_concurrent_logging_succeeds(store: SqlAlchemyStore):
"""
Verifies that concurrent logging succeeds without deadlock, which has been an issue
in previous MLflow releases
"""
experiment_id = _create_experiments(store, "concurrency_exp")
run_config = _get_run_configs(experiment_... |
Verifies that concurrent logging succeeds without deadlock, which has been an issue
in previous MLflow releases
| test_log_metric_concurrent_logging_succeeds | python | mlflow/mlflow | tests/store/tracking/test_sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/tracking/test_sqlalchemy_store.py | Apache-2.0 |
def test_log_batch_with_unchanged_and_new_params(store: SqlAlchemyStore):
"""
Test case to ensure the following code works:
---------------------------------------------
mlflow.log_params({"a": 0, "b": 1})
mlflow.log_params({"a": 0, "c": 2})
---------------------------------------------
"""
... |
Test case to ensure the following code works:
---------------------------------------------
mlflow.log_params({"a": 0, "b": 1})
mlflow.log_params({"a": 0, "c": 2})
---------------------------------------------
| test_log_batch_with_unchanged_and_new_params | python | mlflow/mlflow | tests/store/tracking/test_sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/tracking/test_sqlalchemy_store.py | Apache-2.0 |
def test_search_runs_returns_expected_results_with_large_experiment(
store: SqlAlchemyStore,
):
"""
This case tests the SQLAlchemyStore implementation of the SearchRuns API to ensure
that search queries over an experiment containing many runs, each with a large number
of metrics, parameters, and tag... |
This case tests the SQLAlchemyStore implementation of the SearchRuns API to ensure
that search queries over an experiment containing many runs, each with a large number
of metrics, parameters, and tags, are performant and return the expected results.
| test_search_runs_returns_expected_results_with_large_experiment | python | mlflow/mlflow | tests/store/tracking/test_sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/tracking/test_sqlalchemy_store.py | Apache-2.0 |
def _create_trace(
store: SqlAlchemyStore,
request_id: str,
experiment_id=0,
timestamp_ms=0,
execution_time_ms=0,
status=TraceStatus.OK,
request_metadata=None,
tags=None,
) -> TraceInfoV2:
"""Helper function to create a test trace in the database."""
if not store.get_experiment(e... | Helper function to create a test trace in the database. | _create_trace | python | mlflow/mlflow | tests/store/tracking/test_sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/tracking/test_sqlalchemy_store.py | Apache-2.0 |
def test_running_migrations_generates_expected_schema(tmp_path, expected_schema_file, db_url):
"""Test that migrating an existing database generates the desired schema."""
engine = sqlalchemy.create_engine(db_url)
InitialBase.metadata.create_all(engine)
invoke_cli_runner(mlflow.db.commands, ["upgrade", ... | Test that migrating an existing database generates the desired schema. | test_running_migrations_generates_expected_schema | python | mlflow/mlflow | tests/store/tracking/test_sqlalchemy_store_schema.py | https://github.com/mlflow/mlflow/blob/master/tests/store/tracking/test_sqlalchemy_store_schema.py | Apache-2.0 |
def configure_client_for_uc(mock_databricks_uc_host_creds):
"""
Configure MLflow client to register models to UC
"""
with mock.patch("mlflow.utils.databricks_utils.get_databricks_host_creds"):
orig_registry_uri = mlflow.get_registry_uri()
mlflow.set_registry_uri("databricks-uc")
... |
Configure MLflow client to register models to UC
| configure_client_for_uc | python | mlflow/mlflow | tests/store/_unity_catalog/conftest.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/conftest.py | Apache-2.0 |
def test_create_registered_model_three_level_name_hint(store):
"""Test that creating a registered model with invalid name provides legacy registry hint."""
# Mock the _call_endpoint method to raise a RestException with
# "specify all three levels" message
original_error_message = "Model name must specif... | Test that creating a registered model with invalid name provides legacy registry hint. | test_create_registered_model_three_level_name_hint | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def test_create_registered_model_three_level_name_hint_with_period(store):
"""Test the hint works correctly when original error message ends with a period."""
original_error_message = "Model name must specify all three levels."
rest_exception = RestException(
{"error_code": "INVALID_PARAMETER_VALUE"... | Test the hint works correctly when original error message ends with a period. | test_create_registered_model_three_level_name_hint_with_period | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def test_create_registered_model_metastore_does_not_exist_hint(store):
"""
Test that creating a registered model when metastore doesn't exist
provides legacy registry hint.
"""
# Mock the _call_endpoint method to raise a RestException with
# "METASTORE_DOES_NOT_EXIST" message
original_error_... |
Test that creating a registered model when metastore doesn't exist
provides legacy registry hint.
| test_create_registered_model_metastore_does_not_exist_hint | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def test_create_registered_model_other_rest_exceptions_not_modified(store):
"""
Test that RestExceptions unrelated to bad UC model names are not modified
and are re-raised as-is.
"""
original_error_message = "Some other error"
rest_exception = RestException(
{"error_code": "INTERNAL_ERRO... |
Test that RestExceptions unrelated to bad UC model names are not modified
and are re-raised as-is.
| test_create_registered_model_other_rest_exceptions_not_modified | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def _assert_create_model_version_endpoints_called(
request_mock,
name,
source,
version,
run_id=None,
description=None,
extra_headers=None,
tags=None,
model_version_dependencies=None,
):
"""
Asserts that endpoints related to the model version creation flow were called on the p... |
Asserts that endpoints related to the model version creation flow were called on the provided
`request_mock`
| _assert_create_model_version_endpoints_called | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def test_link_prompt_version_to_model_success(store):
"""Test successful Unity Catalog linking with API call."""
with (
mock.patch.object(store, "_edit_endpoint_and_call") as mock_edit_call,
mock.patch.object(store, "_get_endpoint_from_method") as mock_get_endpoint,
mock.patch(
... | Test successful Unity Catalog linking with API call. | test_link_prompt_version_to_model_success | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def test_link_prompt_version_to_model_sets_tag(mock_get_tracking_store, store):
"""Test that linking a prompt version to a model sets the appropriate tag."""
# Setup mocks
mock_tracking_store = mock.Mock()
mock_get_tracking_store.return_value = mock_tracking_store
# Mock the prompt version
moc... | Test that linking a prompt version to a model sets the appropriate tag. | test_link_prompt_version_to_model_sets_tag | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def test_link_prompts_to_trace_success(store):
"""Test successful Unity Catalog linking prompts to a trace with API call."""
with (
mock.patch.object(store, "_edit_endpoint_and_call") as mock_edit_call,
mock.patch.object(store, "_get_endpoint_from_method") as mock_get_endpoint,
mock.pat... | Test successful Unity Catalog linking prompts to a trace with API call. | test_link_prompts_to_trace_success | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def test_link_prompt_version_to_run_success(store):
"""Test successful Unity Catalog linking prompt version to run with API call."""
with (
mock.patch.object(store, "_edit_endpoint_and_call") as mock_edit_call,
mock.patch.object(store, "_get_endpoint_from_method") as mock_get_endpoint,
... | Test successful Unity Catalog linking prompt version to run with API call. | test_link_prompt_version_to_run_success | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def test_link_prompt_version_to_run_sets_tag(mock_get_tracking_store, store):
"""Test that linking a prompt version to a run sets the appropriate tag."""
# Setup mocks
mock_tracking_store = mock.Mock()
mock_get_tracking_store.return_value = mock_tracking_store
# Mock the prompt version
mock_pr... | Test that linking a prompt version to a run sets the appropriate tag. | test_link_prompt_version_to_run_sets_tag | python | mlflow/mlflow | tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py | Apache-2.0 |
def test_proto_to_mlflow_prompt():
"""Test that proto_to_mlflow_prompt correctly handles the decoupled tag architecture."""
# Test with version tags - the key behavior we care about
proto_version = ProtoPromptVersion()
proto_version.name = "test_prompt"
proto_version.version = "1"
proto_version... | Test that proto_to_mlflow_prompt correctly handles the decoupled tag architecture. | test_proto_to_mlflow_prompt | python | mlflow/mlflow | tests/store/_unity_catalog/registry/test_uc_prompt_utils.py | https://github.com/mlflow/mlflow/blob/master/tests/store/_unity_catalog/registry/test_uc_prompt_utils.py | Apache-2.0 |
def eval_input_fn(features, labels, batch_size):
"""An input function for evaluation or prediction"""
features = dict(features)
# Use only features when labels are null.
inputs = features if labels is None else (features, labels)
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.fro... | An input function for evaluation or prediction | eval_input_fn | python | mlflow/mlflow | tests/tensorflow/iris_data_utils.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/iris_data_utils.py | Apache-2.0 |
def test_model_load_succeeds_with_missing_data_key_when_data_exists_at_default_path(
tf_keras_model, model_path, data
):
"""
This is a backwards compatibility test to ensure that models saved in MLflow version <= 0.8.0
can be loaded successfully. These models are missing the `data` flavor configuration ... |
This is a backwards compatibility test to ensure that models saved in MLflow version <= 0.8.0
can be loaded successfully. These models are missing the `data` flavor configuration key.
| test_model_load_succeeds_with_missing_data_key_when_data_exists_at_default_path | python | mlflow/mlflow | tests/tensorflow/test_keras_model_export.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_keras_model_export.py | Apache-2.0 |
def test_save_model_with_tf_save_format(model_path):
"""Ensures that Keras models can be saved with SavedModel format.
Using SavedModel format (save_format="tf") requires that the file extension
is _not_ "h5".
"""
keras_model = mock.Mock(spec=tf.keras.Model)
mlflow.tensorflow.save_model(
... | Ensures that Keras models can be saved with SavedModel format.
Using SavedModel format (save_format="tf") requires that the file extension
is _not_ "h5".
| test_save_model_with_tf_save_format | python | mlflow/mlflow | tests/tensorflow/test_keras_model_export.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_keras_model_export.py | Apache-2.0 |
def test_save_and_load_model_with_tf_save_format(tf_keras_model, model_path, data):
"""Ensures that keras models saved with save_format="tf" can be loaded."""
mlflow.tensorflow.save_model(
tf_keras_model, path=model_path, keras_model_kwargs={"save_format": "tf"}
)
model_conf_path = os.path.join(... | Ensures that keras models saved with save_format="tf" can be loaded. | test_save_and_load_model_with_tf_save_format | python | mlflow/mlflow | tests/tensorflow/test_keras_model_export.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_keras_model_export.py | Apache-2.0 |
def test_load_without_save_format(tf_keras_model, model_path, data):
"""Ensures that keras models without save_format can still be loaded."""
mlflow.tensorflow.save_model(
tf_keras_model, path=model_path, keras_model_kwargs={"save_format": "h5"}
)
model_conf_path = os.path.join(model_path, "MLmo... | Ensures that keras models without save_format can still be loaded. | test_load_without_save_format | python | mlflow/mlflow | tests/tensorflow/test_keras_model_export.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_keras_model_export.py | Apache-2.0 |
def single_multidim_tensor_input_model(data):
"""
This is a model that requires a single input of shape (-1, 4, 3)
"""
x, y = data
model = Sequential()
# This decorator injects the decorated class or function into the Keras custom
# object dictionary, so that it can be serialized and deseri... |
This is a model that requires a single input of shape (-1, 4, 3)
| single_multidim_tensor_input_model | python | mlflow/mlflow | tests/tensorflow/test_keras_pyfunc_model_works_with_all_input_types.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_keras_pyfunc_model_works_with_all_input_types.py | Apache-2.0 |
def multi_multidim_tensor_input_model(data):
"""
This is a model that requires 2 inputs: 'a' and 'b',
input 'a' must be shape of (-1, 2, 3),
input 'b' must be shape of (-1, 2, 5),
"""
x, y = data
input_a = Input(shape=(2, 3), name="a")
input_b = Input(shape=(2, 5), name="b")
@regist... |
This is a model that requires 2 inputs: 'a' and 'b',
input 'a' must be shape of (-1, 2, 3),
input 'b' must be shape of (-1, 2, 5),
| multi_multidim_tensor_input_model | python | mlflow/mlflow | tests/tensorflow/test_keras_pyfunc_model_works_with_all_input_types.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_keras_pyfunc_model_works_with_all_input_types.py | Apache-2.0 |
def test_tf_keras_autolog_logs_metrics_for_single_epoch_training(
random_train_data, random_one_hot_labels
):
"""
tf.Keras exhibits inconsistent epoch indexing behavior in comparison with other
TF2 APIs (e.g., tf.Estimator). tf.Keras uses zero-indexing for epochs,
while other APIs use one-indexing. ... |
tf.Keras exhibits inconsistent epoch indexing behavior in comparison with other
TF2 APIs (e.g., tf.Estimator). tf.Keras uses zero-indexing for epochs,
while other APIs use one-indexing. Accordingly, this test verifies that metrics are
produced in the boundary case where a model is trained for a single ... | test_tf_keras_autolog_logs_metrics_for_single_epoch_training | python | mlflow/mlflow | tests/tensorflow/test_tensorflow2_autolog.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_tensorflow2_autolog.py | Apache-2.0 |
def test_tf_keras_autolog_does_not_mutate_original_callbacks_list(
tmp_path, random_train_data, random_one_hot_labels, positional
):
"""
TensorFlow autologging passes new callbacks to the `fit()` / `fit_generator()` function. If
preexisting user-defined callbacks already exist, these new callbacks are a... |
TensorFlow autologging passes new callbacks to the `fit()` / `fit_generator()` function. If
preexisting user-defined callbacks already exist, these new callbacks are added to the
user-specified ones. This test verifies that the new callbacks are added to the without
permanently mutating the original li... | test_tf_keras_autolog_does_not_mutate_original_callbacks_list | python | mlflow/mlflow | tests/tensorflow/test_tensorflow2_autolog.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_tensorflow2_autolog.py | Apache-2.0 |
def test_autolog_text_vec_model(tmp_path):
"""
Verifies autolog successfully saves a model that can't be saved in the H5 format
"""
mlflow.tensorflow.autolog()
train_samples = tf.convert_to_tensor(["this is an example", "another example"])
train_labels = np.array([0.4, 0.2])
model = get_tex... |
Verifies autolog successfully saves a model that can't be saved in the H5 format
| test_autolog_text_vec_model | python | mlflow/mlflow | tests/tensorflow/test_tensorflow2_autolog.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_tensorflow2_autolog.py | Apache-2.0 |
def test_fluent_autolog_with_tf_keras_logs_expected_content(
random_train_data, random_one_hot_labels
):
"""
Guards against previously-exhibited issues where using the fluent `mlflow.autolog()` API with
`tf.keras` Models did not work due to conflicting patches set by both the
`mlflow.tensorflow.auto... |
Guards against previously-exhibited issues where using the fluent `mlflow.autolog()` API with
`tf.keras` Models did not work due to conflicting patches set by both the
`mlflow.tensorflow.autolog()` and the `mlflow.keras.autolog()` APIs.
| test_fluent_autolog_with_tf_keras_logs_expected_content | python | mlflow/mlflow | tests/tensorflow/test_tensorflow2_autolog.py | https://github.com/mlflow/mlflow/blob/master/tests/tensorflow/test_tensorflow2_autolog.py | Apache-2.0 |
def otel_collector():
"""Start an OpenTelemetry collector in a Docker container."""
subprocess.run(["docker", "pull", "otel/opentelemetry-collector-contrib"], check=True)
with (
tempfile.NamedTemporaryFile() as output_file,
subprocess.Popen(
[
"docker",
... | Start an OpenTelemetry collector in a Docker container. | otel_collector | python | mlflow/mlflow | tests/tracing/conftest.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/conftest.py | Apache-2.0 |
def create_mock_otel_span(
trace_id: int,
span_id: int,
name: str = "test_span",
parent_id: Optional[int] = None,
start_time: Optional[int] = None,
end_time: Optional[int] = None,
):
"""
Create a mock OpenTelemetry span for testing purposes.
OpenTelemetry doesn't allow creating a sp... |
Create a mock OpenTelemetry span for testing purposes.
OpenTelemetry doesn't allow creating a span outside of a tracer. So here we create a mock span
that extends ReadableSpan (data object) and exposes the necessary attributes for testing.
| create_mock_otel_span | python | mlflow/mlflow | tests/tracing/helper.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/helper.py | Apache-2.0 |
def get_tracer_tracking_uri() -> Optional[str]:
"""Get current tracking URI configured as the trace export destination."""
from opentelemetry import trace
tracer = _get_tracer(__name__)
if isinstance(tracer, trace.ProxyTracer):
tracer = tracer._tracer
span_processor = tracer.span_processor.... | Get current tracking URI configured as the trace export destination. | get_tracer_tracking_uri | python | mlflow/mlflow | tests/tracing/helper.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/helper.py | Apache-2.0 |
def reset_autolog_state():
"""Reset autologging state to avoid interference between tests"""
yield
for flavor in FLAVOR_TO_MODULE_NAME:
# 1. Remove post-import hooks (registered by global mlflow.autolog() function)
mlflow.utils.import_hooks._post_import_hooks.pop(flavor, None)
for flav... | Reset autologging state to avoid interference between tests | reset_autolog_state | python | mlflow/mlflow | tests/tracing/helper.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/helper.py | Apache-2.0 |
def score_in_model_serving(model_uri: str, model_input: dict):
"""
A helper function to emulate model prediction inside a Databricks model serving environment.
This is highly simplified version, but captures important aspects for testing tracing:
1. Setting env vars that users set for enable tracing ... |
A helper function to emulate model prediction inside a Databricks model serving environment.
This is highly simplified version, but captures important aspects for testing tracing:
1. Setting env vars that users set for enable tracing in model serving
2. Load the model in a background thread
| score_in_model_serving | python | mlflow/mlflow | tests/tracing/helper.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/helper.py | Apache-2.0 |
def skip_module_when_testing_trace_sdk():
"""Skip the entire module if only mlflow-tracing package is installed"""
if IS_TRACING_SDK_ONLY:
pytest.skip(
"Skipping test because it requires mlflow or mlflow-skinny to be installed.",
allow_module_level=True,
) | Skip the entire module if only mlflow-tracing package is installed | skip_module_when_testing_trace_sdk | python | mlflow/mlflow | tests/tracing/helper.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/helper.py | Apache-2.0 |
def test_log_feedback_with_exception_object(store, tracking_uri, legacy_api):
"""Test that log_feedback correctly accepts Exception objects."""
test_exception = ValueError("Test exception message")
if legacy_api:
mlflow.log_feedback(
trace_id="1234",
name="faithfulness",
... | Test that log_feedback correctly accepts Exception objects. | test_log_feedback_with_exception_object | python | mlflow/mlflow | tests/tracing/test_assessment.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_assessment.py | Apache-2.0 |
def test_update_current_trace_client_request_id_overwrites():
"""Test that client_request_id can be overwritten by subsequent calls."""
from mlflow.tracing.trace_manager import InMemoryTraceManager
with mlflow.start_span("overwrite_test") as span:
# First set
mlflow.update_current_trace(cli... | Test that client_request_id can be overwritten by subsequent calls. | test_update_current_trace_client_request_id_overwrites | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_update_current_trace_client_request_id_stringification():
"""Test that client_request_id is stringified when it's not a string."""
from mlflow.tracing.trace_manager import InMemoryTraceManager
test_cases = [
(123, "123"),
(45.67, "45.67"),
(True, "True"),
(False, "F... | Test that client_request_id is stringified when it's not a string. | test_update_current_trace_client_request_id_stringification | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_update_current_trace_with_metadata():
"""Test that update_current_trace correctly handles metadata parameter."""
@mlflow.trace
def f():
mlflow.update_current_trace(
metadata={
"mlflow.source.name": "inference.py",
"mlflow.source.git.commit": "123... | Test that update_current_trace correctly handles metadata parameter. | test_update_current_trace_with_metadata | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_update_current_trace_should_not_raise_during_model_logging():
"""
Tracing is disabled while model logging. When the model includes
`update_current_trace` call, it should be no-op.
"""
class MyModel(mlflow.pyfunc.PythonModel):
@mlflow.trace
def predict(self, model_inputs):
... |
Tracing is disabled while model logging. When the model includes
`update_current_trace` call, it should be no-op.
| test_update_current_trace_should_not_raise_during_model_logging | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_update_current_trace_state_none():
"""Test that state=None doesn't change trace state."""
from mlflow.tracing.trace_manager import InMemoryTraceManager
with mlflow.start_span("test_span") as span:
# First set state to OK
mlflow.update_current_trace(state="OK")
# Then call ... | Test that state=None doesn't change trace state. | test_update_current_trace_state_none | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_update_current_trace_state_validation():
"""Test that state validation only allows OK or ERROR."""
with mlflow.start_span("test_span"):
# Valid states should work
mlflow.update_current_trace(state="OK")
mlflow.update_current_trace(state="ERROR")
mlflow.update_current_tra... | Test that state validation only allows OK or ERROR. | test_update_current_trace_state_validation | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_span_record_exception_invalid_type():
"""Test record_exception method with invalid parameter type."""
with mlflow.start_span("test_span") as span:
with pytest.raises(
MlflowException,
match="The `exception` parameter must be an Exception instance or a string",
):... | Test record_exception method with invalid parameter type. | test_span_record_exception_invalid_type | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_combined_state_and_record_exception():
"""Test using both status update and record_exception together."""
@mlflow.trace
def test_function():
# Get current span and record exception
span = mlflow.get_current_active_span()
span.record_exception("Processing failed")
#... | Test using both status update and record_exception together. | test_combined_state_and_record_exception | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_span_record_exception_no_op_span():
"""Test that record_exception works gracefully with NoOpSpan."""
# This should not raise an exception
from mlflow.entities.span import NoOpSpan
no_op_span = NoOpSpan()
no_op_span.record_exception("This should be ignored")
# Should not create any tra... | Test that record_exception works gracefully with NoOpSpan. | test_span_record_exception_no_op_span | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_update_current_trace_state_isolation():
"""Test that state update doesn't affect span status."""
with mlflow.start_span("test_span") as span:
# Set span status to OK explicitly
span.set_status("OK")
# Update trace state to ERROR
mlflow.update_current_trace(state="ERROR"... | Test that state update doesn't affect span status. | test_update_current_trace_state_isolation | python | mlflow/mlflow | tests/tracing/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/test_fluent.py | Apache-2.0 |
def test_size_bytes_in_trace_sent_to_mlflow_backend(monkeypatch):
"""Test that SIZE_BYTES is correctly set in the trace sent to MLflow backend via dual write."""
# Enable dual write
monkeypatch.setenv("MLFLOW_ENABLE_TRACE_DUAL_WRITE_IN_MODEL_SERVING", "True")
# Create spans and trace
otel_span = cr... | Test that SIZE_BYTES is correctly set in the trace sent to MLflow backend via dual write. | test_size_bytes_in_trace_sent_to_mlflow_backend | python | mlflow/mlflow | tests/tracing/export/test_inference_table_exporter.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/export/test_inference_table_exporter.py | Apache-2.0 |
def test_prompt_linking_with_dual_write(monkeypatch):
"""Test that prompts are correctly linked when dual write to MLflow backend is enabled."""
# Enable dual write
monkeypatch.setenv("MLFLOW_ENABLE_TRACE_DUAL_WRITE_IN_MODEL_SERVING", "True")
# Create span and trace
otel_span = create_mock_otel_spa... | Test that prompts are correctly linked when dual write to MLflow backend is enabled. | test_prompt_linking_with_dual_write | python | mlflow/mlflow | tests/tracing/export/test_inference_table_exporter.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/export/test_inference_table_exporter.py | Apache-2.0 |
def test_prompt_linking_disabled_without_dual_write(monkeypatch):
"""Test that prompt linking is not attempted when dual write is disabled."""
# Disable dual write
monkeypatch.setenv("MLFLOW_ENABLE_TRACE_DUAL_WRITE_IN_MODEL_SERVING", "False")
# Create span and trace
otel_span = create_mock_otel_spa... | Test that prompt linking is not attempted when dual write is disabled. | test_prompt_linking_disabled_without_dual_write | python | mlflow/mlflow | tests/tracing/export/test_inference_table_exporter.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/export/test_inference_table_exporter.py | Apache-2.0 |
def test_prompt_linking_with_empty_prompts(monkeypatch):
"""Test that empty prompts list doesn't cause issues with dual write."""
# Enable dual write
monkeypatch.setenv("MLFLOW_ENABLE_TRACE_DUAL_WRITE_IN_MODEL_SERVING", "True")
# Create span and trace
otel_span = create_mock_otel_span(
name... | Test that empty prompts list doesn't cause issues with dual write. | test_prompt_linking_with_empty_prompts | python | mlflow/mlflow | tests/tracing/export/test_inference_table_exporter.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/export/test_inference_table_exporter.py | Apache-2.0 |
def test_prompt_linking_error_handling_with_dual_write(monkeypatch):
"""Test that prompt linking errors are handled gracefully with dual write enabled."""
# Enable dual write
monkeypatch.setenv("MLFLOW_ENABLE_TRACE_DUAL_WRITE_IN_MODEL_SERVING", "True")
# Create span and trace
otel_span = create_moc... | Test that prompt linking errors are handled gracefully with dual write enabled. | test_prompt_linking_error_handling_with_dual_write | python | mlflow/mlflow | tests/tracing/export/test_inference_table_exporter.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/export/test_inference_table_exporter.py | Apache-2.0 |
def join_thread_by_name_prefix(prefix: str, timeout: float = 5.0):
"""Join thread by name prefix to avoid time.sleep in tests."""
for thread in threading.enumerate():
if thread != threading.main_thread() and thread.name.startswith(prefix):
thread.join(timeout=timeout) | Join thread by name prefix to avoid time.sleep in tests. | join_thread_by_name_prefix | python | mlflow/mlflow | tests/tracing/export/test_mlflow_v3_exporter.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/export/test_mlflow_v3_exporter.py | Apache-2.0 |
def test_prompt_linking_in_mlflow_v3_exporter(is_async, monkeypatch):
"""Test that prompts are correctly linked when using MLflow v3 exporter."""
monkeypatch.setenv("DATABRICKS_HOST", "dummy-host")
monkeypatch.setenv("DATABRICKS_TOKEN", "dummy-token")
monkeypatch.setenv("MLFLOW_ENABLE_ASYNC_TRACE_LOGGIN... | Test that prompts are correctly linked when using MLflow v3 exporter. | test_prompt_linking_in_mlflow_v3_exporter | python | mlflow/mlflow | tests/tracing/export/test_mlflow_v3_exporter.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/export/test_mlflow_v3_exporter.py | Apache-2.0 |
def test_prompt_linking_with_empty_prompts_mlflow_v3(is_async, monkeypatch):
"""Test that empty prompts list doesn't cause issues with MLflow v3 exporter."""
monkeypatch.setenv("DATABRICKS_HOST", "dummy-host")
monkeypatch.setenv("DATABRICKS_TOKEN", "dummy-token")
monkeypatch.setenv("MLFLOW_ENABLE_ASYNC_... | Test that empty prompts list doesn't cause issues with MLflow v3 exporter. | test_prompt_linking_with_empty_prompts_mlflow_v3 | python | mlflow/mlflow | tests/tracing/export/test_mlflow_v3_exporter.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/export/test_mlflow_v3_exporter.py | Apache-2.0 |
def test_prompt_linking_error_handling_mlflow_v3(monkeypatch):
"""Test that MLflow v3 exporter handles prompt linking errors gracefully."""
monkeypatch.setenv("DATABRICKS_HOST", "dummy-host")
monkeypatch.setenv("DATABRICKS_TOKEN", "dummy-token")
monkeypatch.setenv("MLFLOW_ENABLE_ASYNC_TRACE_LOGGING", "F... | Test that MLflow v3 exporter handles prompt linking errors gracefully. | test_prompt_linking_error_handling_mlflow_v3 | python | mlflow/mlflow | tests/tracing/export/test_mlflow_v3_exporter.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/export/test_mlflow_v3_exporter.py | Apache-2.0 |
def test_on_end_preserves_user_set_trace_state():
"""Test that explicitly set trace state is preserved when span ends."""
otel_span = create_mock_otel_span(
name="foo",
trace_id=_OTEL_TRACE_ID,
span_id=1,
parent_id=None,
start_time=5_000_000,
end_time=9_000_000,
... | Test that explicitly set trace state is preserved when span ends. | test_on_end_preserves_user_set_trace_state | python | mlflow/mlflow | tests/tracing/processor/test_inference_table_processor.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/processor/test_inference_table_processor.py | Apache-2.0 |
def test_on_end_updates_trace_state_when_in_progress():
"""Test that trace state is updated from span when trace is still IN_PROGRESS."""
otel_span = create_mock_otel_span(
name="foo",
trace_id=_OTEL_TRACE_ID,
span_id=1,
parent_id=None,
start_time=5_000_000,
end_t... | Test that trace state is updated from span when trace is still IN_PROGRESS. | test_on_end_updates_trace_state_when_in_progress | python | mlflow/mlflow | tests/tracing/processor/test_inference_table_processor.py | https://github.com/mlflow/mlflow/blob/master/tests/tracing/processor/test_inference_table_processor.py | Apache-2.0 |
def _await_server_up_or_die(port, timeout=30):
"""Waits until the local flask server is listening on the given port."""
_logger.info(f"Awaiting server to be up on {LOCALHOST}:{port}")
start_time = time.time()
while time.time() - start_time < timeout:
with socket.socket(socket.AF_INET, socket.SOC... | Waits until the local flask server is listening on the given port. | _await_server_up_or_die | python | mlflow/mlflow | tests/tracking/integration_test_utils.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/integration_test_utils.py | Apache-2.0 |
def _init_server(backend_uri, root_artifact_uri, extra_env=None, app="mlflow.server:app"):
"""
Launch a new REST server using the tracking store specified by backend_uri and root artifact
directory specified by root_artifact_uri.
:returns A tuple (url, process) containing the string URL of the server an... |
Launch a new REST server using the tracking store specified by backend_uri and root artifact
directory specified by root_artifact_uri.
:returns A tuple (url, process) containing the string URL of the server and a handle to the
server process (a multiprocessing.Process object).
| _init_server | python | mlflow/mlflow | tests/tracking/integration_test_utils.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/integration_test_utils.py | Apache-2.0 |
def _send_rest_tracking_post_request(tracking_server_uri, api_path, json_payload, auth=None):
"""
Make a POST request to the specified MLflow Tracking API and retrieve the
corresponding `requests.Response` object
"""
import requests
url = tracking_server_uri + api_path
return requests.post(... |
Make a POST request to the specified MLflow Tracking API and retrieve the
corresponding `requests.Response` object
| _send_rest_tracking_post_request | python | mlflow/mlflow | tests/tracking/integration_test_utils.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/integration_test_utils.py | Apache-2.0 |
def tracking_uri(request, tmp_path):
"""Set an MLflow Tracking URI with different type of backend."""
if "MLFLOW_SKINNY" in os.environ and request.param == "sqlalchemy":
pytest.skip("SQLAlchemy store is not available in skinny.")
original_tracking_uri = mlflow.get_tracking_uri()
if request.par... | Set an MLflow Tracking URI with different type of backend. | tracking_uri | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_update_registered_model(mock_registry_store):
"""
Update registered model no longer supports name change.
"""
expected_return_value = "some expected return value."
mock_registry_store.rename_registered_model.return_value = expected_return_value
expected_return_value_2 = "other expected ... |
Update registered model no longer supports name change.
| test_update_registered_model | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_create_model_version(mock_registry_store):
"""
Basic test for create model version.
"""
mock_registry_store.create_model_version.return_value = _default_model_version()
res = MlflowClient(registry_uri="sqlite:///somedb.db").create_model_version(
"orig name", "source", "run-id", tags... |
Basic test for create model version.
| test_create_model_version | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_update_model_version(mock_registry_store):
"""
Update registered model no longer support state changes.
"""
mock_registry_store.update_model_version.return_value = _default_model_version()
res = MlflowClient(registry_uri="sqlite:///somedb.db").update_model_version(
name="orig name",... |
Update registered model no longer support state changes.
| test_update_model_version | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_client_can_be_serialized_with_pickle(tmp_path):
"""
Verifies that instances of `MlflowClient` can be serialized using pickle, even if the underlying
Tracking and Model Registry stores used by the client are not serializable using pickle
"""
class MockUnpickleableTrackingStore(SqlAlchemyTra... |
Verifies that instances of `MlflowClient` can be serialized using pickle, even if the underlying
Tracking and Model Registry stores used by the client are not serializable using pickle
| test_client_can_be_serialized_with_pickle | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def registry_uri(request, tmp_path):
"""Set an MLflow Model Registry URI with different type of backend."""
if "MLFLOW_SKINNY" in os.environ and request.param == "sqlalchemy":
pytest.skip("SQLAlchemy store is not available in skinny.")
original_registry_uri = mlflow.get_registry_uri()
if reque... | Set an MLflow Model Registry URI with different type of backend. | registry_uri | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_delete_prompt_version_no_auto_cleanup(tracking_uri):
"""Test that delete_prompt_version no longer automatically deletes prompts"""
client = MlflowClient(tracking_uri=tracking_uri)
# Create prompt and version
client.register_prompt(name="test_prompt", template="Hello {{name}}!")
# Verify p... | Test that delete_prompt_version no longer automatically deletes prompts | test_delete_prompt_version_no_auto_cleanup | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_delete_prompt_with_no_versions(tracking_uri):
"""Test that delete_prompt works when prompt has no versions"""
client = MlflowClient(tracking_uri=tracking_uri)
# Create prompt and version, then delete version
client.register_prompt(name="empty_prompt", template="Hello {{name}}!")
client.del... | Test that delete_prompt works when prompt has no versions | test_delete_prompt_with_no_versions | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_delete_prompt_complete_workflow(tracking_uri):
"""Test the complete workflow: create, add versions, delete versions, delete prompt"""
client = MlflowClient(tracking_uri=tracking_uri)
# Create prompt with multiple versions
client.register_prompt(name="workflow_prompt", template="Version 1: {{na... | Test the complete workflow: create, add versions, delete versions, delete prompt | test_delete_prompt_complete_workflow | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_delete_prompt_version_behavior_consistency(tracking_uri):
"""Test that delete_prompt_version behavior is consistent across registry types"""
client = MlflowClient(tracking_uri=tracking_uri)
# Create multiple prompts with versions
for i in range(3):
prompt_name = f"consistency_test_{i}"... | Test that delete_prompt_version behavior is consistent across registry types | test_delete_prompt_version_behavior_consistency | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_delete_prompt_with_versions_unity_catalog_error(registry_uri):
"""Test that Unity Catalog throws error when deleting prompt with existing versions"""
# Mock Unity Catalog behavior
client = MlflowClient(registry_uri=registry_uri)
# Mock the search_prompt_versions to return versions
mock_re... | Test that Unity Catalog throws error when deleting prompt with existing versions | test_delete_prompt_with_versions_unity_catalog_error | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_link_prompt_version_to_model_smoke_test(tracking_uri):
"""Smoke test for linking a prompt version to a model - just verify the method can be called."""
client = MlflowClient(tracking_uri=tracking_uri)
# Create an experiment and a run to have a proper context
experiment_id = client.create_exper... | Smoke test for linking a prompt version to a model - just verify the method can be called. | test_link_prompt_version_to_model_smoke_test | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_link_prompts_to_trace_smoke_test(tracking_uri):
"""Smoke test for linking prompt versions to a trace - just verify the method can be called."""
client = MlflowClient(tracking_uri=tracking_uri)
# Create an experiment and a run to have a proper context
experiment_id = client.create_experiment("t... | Smoke test for linking prompt versions to a trace - just verify the method can be called. | test_link_prompts_to_trace_smoke_test | python | mlflow/mlflow | tests/tracking/test_client.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_client.py | Apache-2.0 |
def test_duplicated_log_image_with_step():
"""
MLflow will save both files if there are multiple calls to log_image
with the same key and step.
"""
import numpy as np
image1 = np.random.randint(0, 256, size=(100, 100, 3), dtype=np.uint8)
image2 = np.random.randint(0, 256, size=(100, 100, 3)... |
MLflow will save both files if there are multiple calls to log_image
with the same key and step.
| test_duplicated_log_image_with_step | python | mlflow/mlflow | tests/tracking/test_log_image.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_log_image.py | Apache-2.0 |
def test_duplicated_log_image_with_timestamp():
"""
MLflow will save both files if there are multiple calls to log_image
with the same key, step, and timestamp.
"""
import numpy as np
image1 = np.random.randint(0, 256, size=(100, 100, 3), dtype=np.uint8)
image2 = np.random.randint(0, 256, s... |
MLflow will save both files if there are multiple calls to log_image
with the same key, step, and timestamp.
| test_duplicated_log_image_with_timestamp | python | mlflow/mlflow | tests/tracking/test_log_image.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_log_image.py | Apache-2.0 |
def mlflow_client(request, tmp_path):
"""Provides an MLflow Tracking API client pointed at the local tracking server."""
if request.param == "file":
backend_uri = tmp_path.joinpath("file").as_uri()
elif request.param == "sqlalchemy":
path = tmp_path.joinpath("sqlalchemy.db").as_uri()
... | Provides an MLflow Tracking API client pointed at the local tracking server. | mlflow_client | python | mlflow/mlflow | tests/tracking/test_rest_tracking.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_rest_tracking.py | Apache-2.0 |
def cli_env(mlflow_client):
"""Provides an environment for the MLflow CLI pointed at the local tracking server."""
return {
"LC_ALL": "en_US.UTF-8",
"LANG": "en_US.UTF-8",
"MLFLOW_TRACKING_URI": mlflow_client.tracking_uri,
} | Provides an environment for the MLflow CLI pointed at the local tracking server. | cli_env | python | mlflow/mlflow | tests/tracking/test_rest_tracking.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/test_rest_tracking.py | Apache-2.0 |
def create_test_runs_and_expected_data(experiment_id=None):
"""
Create a pair of runs and a corresponding data to expect when runs are searched
for the same experiment.
Returns:
A tuple of a list and a dictionary
"""
start_times = [get_current_time_millis(), get_current_time_millis()]
... |
Create a pair of runs and a corresponding data to expect when runs are searched
for the same experiment.
Returns:
A tuple of a list and a dictionary
| create_test_runs_and_expected_data | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def reset_experiment_id():
"""
This fixture resets the active experiment id *after* the execution of the test case in which
its included
"""
yield
mlflow.tracking.fluent._active_experiment_id = None |
This fixture resets the active experiment id *after* the execution of the test case in which
its included
| reset_experiment_id | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_search_runs_no_arguments(search_runs_output_format):
"""
When no experiment ID is specified, it should try to get the implicit one.
"""
mock_experiment_id = mock.Mock()
experiment_id_patch = mock.patch(
"mlflow.tracking.fluent._get_experiment_id", return_value=mock_experiment_id
... |
When no experiment ID is specified, it should try to get the implicit one.
| test_search_runs_no_arguments | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_search_runs_all_experiments(search_runs_output_format):
"""
When no experiment ID is specified but flag is passed, it should search all experiments.
"""
from mlflow.entities import Experiment
mock_experiment_id = mock.Mock()
mock_experiment = mock.Mock(Experiment)
experiment_id_pat... |
When no experiment ID is specified but flag is passed, it should search all experiments.
| test_search_runs_all_experiments | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_search_runs_by_non_existing_experiment_name():
"""When invalid experiment names are used (including None), it should return an empty
collection.
"""
for name in [None, f"Random {random.randint(1, 1e6)}"]:
assert search_runs(experiment_names=[name], output_format="list") == [] | When invalid experiment names are used (including None), it should return an empty
collection.
| test_search_runs_by_non_existing_experiment_name | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_search_runs_by_experiment_id_and_name():
"""When both experiment_ids and experiment_names are used, it should throw an exception"""
err_msg = "Only experiment_ids or experiment_names can be used, but not both"
with pytest.raises(MlflowException, match=err_msg):
search_runs(experiment_ids=["... | When both experiment_ids and experiment_names are used, it should throw an exception | test_search_runs_by_experiment_id_and_name | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_paginate_lt_maxresults_onepage():
"""
Number of runs is less than max_results and fits on one page,
so we only need to fetch one page.
"""
runs = [create_run() for _ in range(5)]
tokenized_runs = PagedList(runs, "")
max_results = 50
max_per_page = 10
mocked_lambda = mock.Moc... |
Number of runs is less than max_results and fits on one page,
so we only need to fetch one page.
| test_paginate_lt_maxresults_onepage | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_paginate_lt_maxresults_multipage():
"""
Number of runs is less than max_results, but multiple pages are necessary to get all runs
"""
tokenized_runs = PagedList([create_run() for _ in range(10)], "token")
no_token_runs = PagedList([create_run()], "")
max_results = 50
max_per_page = ... |
Number of runs is less than max_results, but multiple pages are necessary to get all runs
| test_paginate_lt_maxresults_multipage | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_paginate_lt_maxresults_onepage_nonetoken():
"""
Number of runs is less than max_results and fits on one page.
The token passed back on the last page is None, not the emptystring
"""
runs = [create_run() for _ in range(5)]
tokenized_runs = PagedList(runs, None)
max_results = 50
m... |
Number of runs is less than max_results and fits on one page.
The token passed back on the last page is None, not the emptystring
| test_paginate_lt_maxresults_onepage_nonetoken | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_paginate_eq_maxresults_blanktoken():
"""
Runs returned are equal to max_results which are equal to a full number of pages.
The server might send a token back, or they might not (depending on if they know if
more runs exist). In this example, no token is sent back.
Expected behavior is to NO... |
Runs returned are equal to max_results which are equal to a full number of pages.
The server might send a token back, or they might not (depending on if they know if
more runs exist). In this example, no token is sent back.
Expected behavior is to NOT query for more pages.
| test_paginate_eq_maxresults_blanktoken | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_paginate_eq_maxresults_token():
"""
Runs returned are equal to max_results which are equal to a full number of pages.
The server might send a token back, or they might not (depending on if they know if
more runs exist). In this example, a token IS sent back.
Expected behavior is to NOT quer... |
Runs returned are equal to max_results which are equal to a full number of pages.
The server might send a token back, or they might not (depending on if they know if
more runs exist). In this example, a token IS sent back.
Expected behavior is to NOT query for more pages.
| test_paginate_eq_maxresults_token | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_paginate_gt_maxresults_multipage():
"""
Number of runs that fit search criteria is greater than max_results. Multiple pages expected.
Expected to only get max_results number of results back.
"""
# should ask for and return the correct number of max_results
full_page_runs = PagedList([cr... |
Number of runs that fit search criteria is greater than max_results. Multiple pages expected.
Expected to only get max_results number of results back.
| test_paginate_gt_maxresults_multipage | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_paginate_gt_maxresults_onepage():
"""
Number of runs that fit search criteria is greater than max_results. Only one page expected.
Expected to only get max_results number of results back.
"""
runs = [create_run() for _ in range(10)]
tokenized_runs = PagedList(runs, "abc")
max_result... |
Number of runs that fit search criteria is greater than max_results. Only one page expected.
Expected to only get max_results number of results back.
| test_paginate_gt_maxresults_onepage | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_delete_tag():
"""
Confirm that fluent API delete tags actually works.
"""
mlflow.set_tag("a", "b")
run = MlflowClient().get_run(mlflow.active_run().info.run_id)
assert "a" in run.data.tags
mlflow.delete_tag("a")
run = MlflowClient().get_run(mlflow.active_run().info.run_id)
a... |
Confirm that fluent API delete tags actually works.
| test_delete_tag | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_set_active_model_public_env_var(monkeypatch, is_in_databricks_serving):
"""Test that MLFLOW_ACTIVE_MODEL_ID (public env var) works correctly."""
with mock.patch(
"mlflow.tracking.fluent.is_in_databricks_model_serving_environment",
return_value=is_in_databricks_serving,
) as mock_is_... | Test that MLFLOW_ACTIVE_MODEL_ID (public env var) works correctly. | test_set_active_model_public_env_var | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def test_clear_active_model_clears_env_vars(monkeypatch):
"""Test that clear_active_model() properly clears environment variables."""
# Set both environment variables
monkeypatch.setenv(_MLFLOW_ACTIVE_MODEL_ID.name, "legacy-model-id")
monkeypatch.setenv(MLFLOW_ACTIVE_MODEL_ID.name, "public-model-id")
... | Test that clear_active_model() properly clears environment variables. | test_clear_active_model_clears_env_vars | python | mlflow/mlflow | tests/tracking/fluent/test_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent.py | Apache-2.0 |
def reset_module_import():
"""
Temporarily reset the module import state to simulate the module being not imported.
"""
original_modules = {}
for module_name in FLAVOR_TO_MODULE_NAME.values():
original_modules[module_name] = sys.modules.get(module_name)
try:
yield
finally:
... |
Temporarily reset the module import state to simulate the module being not imported.
| reset_module_import | python | mlflow/mlflow | tests/tracking/fluent/test_fluent_autolog.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/fluent/test_fluent_autolog.py | Apache-2.0 |
def join_thread_by_name_prefix(prefix: str):
"""Join any thread whose name starts with the given prefix."""
for t in threading.enumerate():
if t.name.startswith(prefix):
t.join() | Join any thread whose name starts with the given prefix. | join_thread_by_name_prefix | python | mlflow/mlflow | tests/tracking/_model_registry/test_model_registry_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/_model_registry/test_model_registry_fluent.py | Apache-2.0 |
def mock_dbr_version():
"""Mock DatabricksRuntimeVersion to simulate a supported client image."""
with mock.patch(
"mlflow.utils.databricks_utils.DatabricksRuntimeVersion.parse",
return_value=DatabricksRuntimeVersion(
is_client_image=True,
major=2, # Supported version
... | Mock DatabricksRuntimeVersion to simulate a supported client image. | mock_dbr_version | python | mlflow/mlflow | tests/tracking/_model_registry/test_model_registry_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/_model_registry/test_model_registry_fluent.py | Apache-2.0 |
def test_register_model_with_env_pack(tmp_path, mock_dbr_version):
"""Test that register_model correctly integrates with environment packing functionality."""
# Mock download_artifacts to return a path
mock_artifacts_dir = tmp_path / "artifacts"
mock_artifacts_dir.mkdir()
(mock_artifacts_dir / "requ... | Test that register_model correctly integrates with environment packing functionality. | test_register_model_with_env_pack | python | mlflow/mlflow | tests/tracking/_model_registry/test_model_registry_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/_model_registry/test_model_registry_fluent.py | Apache-2.0 |
def test_register_model_with_env_pack_staging_failure(tmp_path, mock_dbr_version):
"""Test that register_model handles staging failure gracefully."""
# Mock download_artifacts to return a path
mock_artifacts_dir = tmp_path / "artifacts"
mock_artifacts_dir.mkdir()
(mock_artifacts_dir / "requirements.... | Test that register_model handles staging failure gracefully. | test_register_model_with_env_pack_staging_failure | python | mlflow/mlflow | tests/tracking/_model_registry/test_model_registry_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/_model_registry/test_model_registry_fluent.py | Apache-2.0 |
def test_load_prompt_with_link_to_model_disabled():
"""Test load_prompt with link_to_model=False does not attempt linking."""
# Register a prompt
mlflow.register_prompt(name="test_prompt", template="Hello, {{name}}!")
# Create a logged model and set it as active
with mlflow.start_run():
mo... | Test load_prompt with link_to_model=False does not attempt linking. | test_load_prompt_with_link_to_model_disabled | python | mlflow/mlflow | tests/tracking/_model_registry/test_model_registry_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/_model_registry/test_model_registry_fluent.py | Apache-2.0 |
def test_load_prompt_with_active_model_integration():
"""Test load_prompt with active model integration using get_active_model_id."""
# Register a prompt
mlflow.register_prompt(name="test_prompt", template="Hello, {{name}}!")
# Test loading prompt with active model context
with mlflow.start_run():... | Test load_prompt with active model integration using get_active_model_id. | test_load_prompt_with_active_model_integration | python | mlflow/mlflow | tests/tracking/_model_registry/test_model_registry_fluent.py | https://github.com/mlflow/mlflow/blob/master/tests/tracking/_model_registry/test_model_registry_fluent.py | Apache-2.0 |
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