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SIENTIAPDE-1309: Update README with Helm instructions and refactor experiment status messages. Also, update values.yaml with new image and configurations.

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This commit is contained in:
Bruno Domingues
2025-11-06 15:34:03 -03:00
parent a625ef6c19
commit c7f44a2423
9 changed files with 133 additions and 95 deletions
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36
tests/utils/models/test_experiment_status.py
View File

@@ -5,11 +5,11 @@ from model_manager.utils.models.experiment_status import ExperimentStatus
def test_experiment_status_values():
"""Test that all expected status values exist."""
assert ExperimentStatus.MAGE_WAITING_PROC == 'MAGE_WAITING_PROC'
assert ExperimentStatus.ORCHESTRATOR_WAITING_PROC == 'ORCHESTRATOR_WAITING_PROC'
assert ExperimentStatus.TRAINING_SUCCESS == 'TRAINING_SUCCESS'
assert ExperimentStatus.TRAINING_ERROR == 'TRAINING_ERROR'
assert ExperimentStatus.MLFLOW_SENT == 'MLFLOW_SENT'
assert ExperimentStatus.MLFLOW_SEND_ERROR == 'MLFLOW_SEND_ERROR'
assert ExperimentStatus.TRACKING_SENT == 'TRACKING_SENT'
assert ExperimentStatus.TRACKING_SEND_ERROR == 'TRACKING_SEND_ERROR'
assert ExperimentStatus.FILE_DELETED == 'FILE_DELETED'
assert ExperimentStatus.FILE_DELETE_ERROR == 'FILE_DELETE_ERROR'
@@ -28,11 +28,11 @@ def test_experiment_status_is_string():
def test_experiment_status_membership():
"""Test membership checks for status values."""
assert 'MAGE_WAITING_PROC' in [s.value for s in ExperimentStatus]
assert 'ORCHESTRATOR_WAITING_PROC' in [s.value for s in ExperimentStatus]
assert 'TRAINING_SUCCESS' in [s.value for s in ExperimentStatus]
assert 'TRAINING_ERROR' in [s.value for s in ExperimentStatus]
assert 'MLFLOW_SENT' in [s.value for s in ExperimentStatus]
assert 'MLFLOW_SEND_ERROR' in [s.value for s in ExperimentStatus]
assert 'TRACKING_SENT' in [s.value for s in ExperimentStatus]
assert 'TRACKING_SEND_ERROR' in [s.value for s in ExperimentStatus]
assert 'FILE_DELETED' in [s.value for s in ExperimentStatus]
assert 'FILE_DELETE_ERROR' in [s.value for s in ExperimentStatus]
@@ -41,39 +41,43 @@ def test_experiment_status_iteration():
"""Test that enum can be iterated."""
statuses = list(ExperimentStatus)
assert len(statuses) == 8
assert ExperimentStatus.MAGE_WAITING_PROC in statuses
assert ExperimentStatus.ORCHESTRATOR_WAITING_PROC in statuses
assert ExperimentStatus.TRAINING_SUCCESS in statuses
assert ExperimentStatus.TRAINING_ERROR in statuses
assert ExperimentStatus.MLFLOW_SENT in statuses
assert ExperimentStatus.MLFLOW_SEND_ERROR in statuses
assert ExperimentStatus.TRACKING_SENT in statuses
assert ExperimentStatus.TRACKING_SEND_ERROR in statuses
assert ExperimentStatus.FILE_DELETED in statuses
assert ExperimentStatus.FILE_DELETE_ERROR in statuses
def test_experiment_status_comparison():
"""Test that enum values can be compared with strings."""
assert ExperimentStatus.MAGE_WAITING_PROC == 'MAGE_WAITING_PROC'
assert ExperimentStatus.ORCHESTRATOR_WAITING_PROC == 'ORCHESTRATOR_WAITING_PROC'
assert ExperimentStatus.TRAINING_SUCCESS == 'TRAINING_SUCCESS'
assert ExperimentStatus.TRAINING_ERROR != 'TRAINING_SUCCESS'
def test_experiment_status_access_by_name():
"""Test accessing enum members by name."""
assert ExperimentStatus['MAGE_WAITING_PROC'] == ExperimentStatus.MAGE_WAITING_PROC
assert (
ExperimentStatus['ORCHESTRATOR_WAITING_PROC'] == ExperimentStatus.ORCHESTRATOR_WAITING_PROC
)
assert ExperimentStatus['TRAINING_SUCCESS'] == ExperimentStatus.TRAINING_SUCCESS
assert ExperimentStatus['TRAINING_ERROR'] == ExperimentStatus.TRAINING_ERROR
assert ExperimentStatus['MLFLOW_SENT'] == ExperimentStatus.MLFLOW_SENT
assert ExperimentStatus['MLFLOW_SEND_ERROR'] == ExperimentStatus.MLFLOW_SEND_ERROR
assert ExperimentStatus['TRACKING_SENT'] == ExperimentStatus.TRACKING_SENT
assert ExperimentStatus['TRACKING_SEND_ERROR'] == ExperimentStatus.TRACKING_SEND_ERROR
assert ExperimentStatus['FILE_DELETED'] == ExperimentStatus.FILE_DELETED
assert ExperimentStatus['FILE_DELETE_ERROR'] == ExperimentStatus.FILE_DELETE_ERROR
def test_experiment_status_access_by_value():
"""Test accessing enum members by value."""
assert ExperimentStatus('MAGE_WAITING_PROC') == ExperimentStatus.MAGE_WAITING_PROC
assert (
ExperimentStatus('ORCHESTRATOR_WAITING_PROC') == ExperimentStatus.ORCHESTRATOR_WAITING_PROC
)
assert ExperimentStatus('TRAINING_SUCCESS') == ExperimentStatus.TRAINING_SUCCESS
assert ExperimentStatus('TRAINING_ERROR') == ExperimentStatus.TRAINING_ERROR
assert ExperimentStatus('MLFLOW_SENT') == ExperimentStatus.MLFLOW_SENT
assert ExperimentStatus('MLFLOW_SEND_ERROR') == ExperimentStatus.MLFLOW_SEND_ERROR
assert ExperimentStatus('TRACKING_SENT') == ExperimentStatus.TRACKING_SENT
assert ExperimentStatus('TRACKING_SEND_ERROR') == ExperimentStatus.TRACKING_SEND_ERROR
assert ExperimentStatus('FILE_DELETED') == ExperimentStatus.FILE_DELETED
assert ExperimentStatus('FILE_DELETE_ERROR') == ExperimentStatus.FILE_DELETE_ERROR

2
tests/utils/models/test_init.py
View File

@@ -10,7 +10,7 @@ from model_manager.utils.models import (
def test_experiment_status_import():
"""Test that ExperimentStatus can be imported from models package."""
assert ExperimentStatus is not None
assert hasattr(ExperimentStatus, 'MAGE_WAITING_PROC')
assert hasattr(ExperimentStatus, 'ORCHESTRATOR_WAITING_PROC')
assert hasattr(ExperimentStatus, 'TRAINING_SUCCESS')

20
tests/workflows/test_train_model.py
View File

@@ -286,9 +286,9 @@ async def test_train_model_mlflow_error(mock_workflow_module, mock_train_params)
with pytest.raises(ModelTrainingError):
await workflow_instance._train_model(mock_train_params, 123, metadata)
# Verify MLFLOW_SEND_ERROR status was set
# Verify TRACKING_SEND_ERROR status was set
call_args = mock_workflow_module.execute_activity_method.call_args_list[1]
assert call_args[0][1]['status'] == ExperimentStatus.MLFLOW_SEND_ERROR
assert call_args[0][1]['status'] == ExperimentStatus.TRACKING_SEND_ERROR
@pytest.mark.asyncio
@@ -359,14 +359,14 @@ async def test_update_experiment_run_status_only(mock_workflow_module):
metadata=metadata,
experiment_run_id=123,
update_type=UpdateType.STATUS,
status=ExperimentStatus.MAGE_WAITING_PROC,
status=ExperimentStatus.ORCHESTRATOR_WAITING_PROC,
)
# Verify activity was called with correct parameters
call_args = mock_workflow_module.execute_activity_method.call_args[0]
assert call_args[1]['experiment_run_id'] == 123
assert call_args[1]['update_type'] == UpdateType.STATUS
assert call_args[1]['status'] == ExperimentStatus.MAGE_WAITING_PROC
assert call_args[1]['status'] == ExperimentStatus.ORCHESTRATOR_WAITING_PROC
assert 'error_message' not in call_args[1] or call_args[1].get('error_message') is None
@@ -411,7 +411,7 @@ async def test_update_experiment_run_with_run_name(mock_workflow_module):
metadata=metadata,
experiment_run_id=123,
update_type=UpdateType.MODEL_SAVED,
status=ExperimentStatus.MLFLOW_SENT,
status=ExperimentStatus.TRACKING_SENT,
run_name='test-run-123',
)
@@ -438,9 +438,9 @@ async def test_run_complete_workflow_success(
mock_workflow_module.execute_activity_method = AsyncMock(
side_effect=[
mock_train_params, # validate_train_params
None, # update status (MAGE_WAITING_PROC)
None, # update status (ORCHESTRATOR_WAITING_PROC)
train_result, # train_model
None, # update status (MLFLOW_SENT)
None, # update status (TRACKING_SENT)
None, # cleanup_resources
None, # update status (FILE_DELETED)
]
@@ -490,7 +490,7 @@ async def test_run_workflow_training_error(
mock_workflow_module.execute_activity_method = AsyncMock(
side_effect=[
mock_train_params, # validate_train_params
None, # update status (MAGE_WAITING_PROC)
None, # update status (ORCHESTRATOR_WAITING_PROC)
RuntimeError('Training failed'), # train_model fails
None, # update status (TRAINING_ERROR)
]
@@ -521,9 +521,9 @@ async def test_run_workflow_cleanup_error(
mock_workflow_module.execute_activity_method = AsyncMock(
side_effect=[
mock_train_params, # validate_train_params
None, # update status (MAGE_WAITING_PROC)
None, # update status (ORCHESTRATOR_WAITING_PROC)
train_result, # train_model
None, # update status (MLFLOW_SENT)
None, # update status (TRACKING_SENT)
RuntimeError('Cleanup failed'), # cleanup_resources fails
None, # update status (FILE_DELETE_ERROR)
]