def build_workflow():
with af.global_config_file(project_path +
'/resources/workflow_config.yaml'):
with af.config('job_1'):
op_1 = af.user_define_operation(
af.PythonObjectExecutor(StreamPrintHelloExecutor('job_1')))
with af.config('job_2'):
op_2 = af.user_define_operation(
af.PythonObjectExecutor(
SendEventExecutor(key='key_1', value='value_1')))
with af.config('job_3'):
op_3 = af.user_define_operation(
af.PythonObjectExecutor(
SendEventExecutor(key='key_2', value='value_2')))
af.user_define_control_dependency(op_1,
op_2,
event_key='key_1',
event_type='UNDEFINED',
event_value="value_1")
af.user_define_control_dependency(op_1,
op_3,
event_key='key_2',
event_type='UNDEFINED',
event_value="value_2")
def run_task_function(client: NotificationClient):
with af.global_config_file(workflow_config_file()):
with af.config('task_2'):
executor_1 = af.user_define_operation(
af.PythonObjectExecutor(SimpleExecutor()))
with af.config('task_5'):
executor_2 = af.user_define_operation(
af.PythonObjectExecutor(SimpleExecutor()))
af.user_define_control_dependency(src=executor_2,
dependency=executor_1,
namespace='test',
event_key='key_1',
event_value='value_1',
sender='*')
workflow_info = af.workflow_operation.submit_workflow(
workflow_name)
af.workflow_operation.start_new_workflow_execution(workflow_name)
flag = True
while True:
with create_session() as session:
tes = session.query(TaskExecution).filter(
TaskExecution.dag_id == 'test_project.test_workflow',
TaskExecution.task_id == 'task_2').all()
if 1 == len(tes) and flag:
client.send_event(
BaseEvent(key='key_1', value='value_1'))
flag = False
dag_run = session.query(DagRun).filter(
DagRun.dag_id == 'test_project.test_workflow').first()
if dag_run is not None and dag_run.state in State.finished:
break
else:
time.sleep(1)
def run_task_function(client: NotificationClient):
with af.global_config_file(workflow_config_file()):
with af.config('task_2'):
executor_1 = af.user_define_operation(
af.PythonObjectExecutor(
SendExecutor(sender='task_2',
key='key_1',
value='value_1',
event_type='UNDEFINED',
port=server_port())))
with af.config('task_5'):
executor_2 = af.user_define_operation(
af.PythonObjectExecutor(SimpleExecutor()))
af.user_define_control_dependency(src=executor_2,
dependency=executor_1,
event_key='key_1',
event_value='value_1')
workflow_info = af.workflow_operation.submit_workflow(
workflow_name)
af.workflow_operation.start_new_workflow_execution(workflow_name)
while True:
with create_session() as session:
dag_run = session.query(DagRun).filter(
DagRun.dag_id == 'test_project.test_workflow').first()
if dag_run is not None and dag_run.state in State.finished:
break
else:
time.sleep(1)
def build_workflow():
with af.global_config_file(project_path + '/resources/workflow_config.yaml'):
with af.config('job_1'):
af.user_define_operation(af.PythonObjectExecutor(StreamPrintEventExecutor('job_1')))
with af.config('job_2'):
af.user_define_operation(af.PythonObjectExecutor(
SendEventExecutor(key='key_1', value='value_1', num=5, post_time=5)))
def build_workflow(workflow_config_path):
with ai_flow.global_config_file(workflow_config_path):
with ai_flow.config('job_1'):
op_1 = ai_flow.user_define_operation(
ai_flow.PythonObjectExecutor(PrintHelloExecutor('job_1')))
with ai_flow.config('job_2'):
op_2 = ai_flow.user_define_operation(
ai_flow.PythonObjectExecutor(PrintHelloExecutor('job_2')))
ai_flow.stop_before_control_dependency(op_2, op_1)
def run_project(project_root_path):
af.set_project_config_file(project_root_path + '/project.yaml')
# Config command line job, we set platform to local and engine to cmd_line here
cmd_job_config = af.BaseJobConfig(platform=LocalPlatform.platform(), engine=CMDEngine().engine())
with af.config(cmd_job_config):
# Command line job executor
cmd_job = af.user_define_operation(executor=CmdExecutor(cmd_line="echo Start AI flow"))
# Config python job, we set platform to local and engine to python here
python_job_config = af.BaseJobConfig(platform=LocalPlatform.platform(), engine=PythonEngine.engine())
# Set execution mode of this python job to BATCH,
# which indicates jobs with this config is running in the form of batch.
python_job_config.exec_mode = af.ExecutionMode.BATCH
with af.config(python_job_config):
# Path of Source data(under '..../simple_transform_airflow' dir)
source_path = os.path.dirname(os.path.abspath(__file__)) + '/source_data.csv'
# Path of Sink data
sink_path = os.path.dirname(os.path.abspath(__file__)) + '/sink_data.csv'
# To make the project replaceable, we register the example in metadata service
read_example_meta = af.register_example(name='read_example', support_type=ExampleSupportType.EXAMPLE_BATCH,
data_format='csv', data_type='pandas', batch_uri=source_path)
# Read training example using af.read_example()
# example_info is the meta information of the example
read_example_channel = af.read_example(example_info=read_example_meta, exec_args=ExecuteArgs(
batch_properties=Args(header=None, names=["a", "b", "c"])))
# Transform examples using af.transform()
transform_channel = af.transform(input_data_list=[read_example_channel],
executor=PythonObjectExecutor(python_object=SimpleTransform()))
write_example_meta = af.register_example(name='write_example', support_type=ExampleSupportType.EXAMPLE_BATCH,
data_format='csv', data_type='pandas', batch_uri=sink_path)
# Write example to specific path
write = af.write_example(input_data=transform_channel, example_info=write_example_meta,
exec_args=ExecuteArgs(batch_properties=Args(sep=',', header=False, index=False)))
# Add control dependency, which means read_example job will start right after command line job finishes.
af.stop_before_control_dependency(read_example_channel, cmd_job)
transform_dag = 'simple_transform'
af.deploy_to_airflow(project_root_path, dag_id=transform_dag)
context = af.run(project_path=project_root_path,
dag_id=transform_dag,
scheduler_type=SchedulerType.AIRFLOW)
def test_context_with_yaml_file(self):
config_file = path_util.get_file_dir(__file__) + "/workflow.yaml"
with af.global_config_file(config_path=config_file) as g_config:
with af.config('task_1') as config_1:
self.assertEqual('task_1', config_1.job_name)
self.assertEqual('cmd_line', config_1.engine)
self.assertEqual('interval',
config_1.periodic_config.periodic_type)
self.assertEqual(20, config_1.periodic_config.args['seconds'])
with af.config('task_2') as config_2:
self.assertEqual('task_2', config_2.job_name)
self.assertEqual('cmd_line', config_2.engine)
self.assertEqual('cron',
config_2.periodic_config.periodic_type)
self.assertEqual('* * * * *', config_2.periodic_config.args)
def test_batch_train_component_with_an_output(self):
input_example_meta = af.register_example(
name='batch_train_example',
support_type=ExampleSupportType.EXAMPLE_BATCH)
model_meta = af.register_model(model_name='mnist_model',
model_type=ModelType.SAVED_MODEL)
example_meta = af.register_example(
name='output_example',
support_type=ExampleSupportType.EXAMPLE_BATCH,
data_type='numpy',
data_format='npz',
batch_uri=os.path.abspath(
os.path.dirname(__file__) + '/numpy_output.npz'))
with af.config(
af.BaseJobConfig(platform='local',
engine='python',
job_name='batch_train')):
input_example = af.read_example(
example_info=input_example_meta,
executor=PythonObjectExecutor(
python_object=ReadBatchExample()))
train_channel = af.train(
input_data_list=[input_example],
executor=PythonObjectExecutor(
python_object=TrainBatchMnistModelWithOutput()),
model_info=model_meta,
output_num=1)
af.write_example(input_data=train_channel,
example_info=example_meta)
workflow_id = af.run(test_util.get_project_path())
res = af.wait_workflow_execution_finished(workflow_id)
self.assertEqual(0, res)
def run_flink_python_job():
with af.global_config_file(test_util.get_job_config_file()):
with af.config('vvp_python_job'):
faf.vvp_job()
workflow_id = af.run(test_util.get_project_path(),
dag_id='wordcount_vvp_python',
scheduler_type=af.SchedulerType.AIRFLOW)
def run_flink_job():
with af.global_config_file(test_util.get_job_config_file()):
with af.config('vvp_job'):
faf.vvp_job()
workflow_id = af.run(test_util.get_project_path())
res = af.wait_workflow_execution_finished(workflow_id)
print(res)
def test_stream_transform_component(self):
file = get_file_dir(__file__) + '/test1.csv'
input_example_meta = af.register_example(
name='test_example',
support_type=ExampleSupportType.EXAMPLE_BOTH,
stream_uri=file)
output_file = get_file_dir(
__file__) + "/output_transform_stream_test1.csv"
output_example_meta = af.register_example(
name='test_example_output',
support_type=ExampleSupportType.EXAMPLE_BOTH,
stream_uri=output_file)
with af.config(
af.BaseJobConfig(platform='local',
engine='python',
job_name='stream_transform')):
input_example = af.read_example(
example_info=input_example_meta,
executor=PythonObjectExecutor(
python_object=ReadStreamExample()))
transform_example = af.transform(
input_data_list=[input_example],
executor=PythonObjectExecutor(
python_object=TransformStreamData()))
af.write_example(input_data=transform_example,
example_info=output_example_meta.name,
executor=PythonObjectExecutor(
python_object=WriteStreamExample()))
workflow_id = af.run(test_util.get_project_path())
res = af.wait_workflow_execution_finished(workflow_id)
self.assertEqual(0, res)
def run_task_function(client: NotificationClient):
with af.global_config_file(workflow_config_file()):
with af.config('task_1'):
cmd_executor = af.user_define_operation(
output_num=0,
executor=CmdExecutor(
cmd_line='echo "hello world" && sleep 30'.format(
1)))
workflow_info = af.workflow_operation.submit_workflow(
workflow_name)
we = af.workflow_operation.start_new_workflow_execution(
workflow_name)
while True:
with create_session() as session:
ti = session.query(TaskInstance) \
.filter(TaskInstance.dag_id == 'test_project.test_workflow').first()
if ti is not None and ti.state == State.RUNNING:
af.workflow_operation.stop_job('task_1',
we.execution_id)
elif ti.state == State.KILLED:
break
else:
time.sleep(1)
job_info = af.workflow_operation.get_job('task_1', we.execution_id)
self.assertEqual('task_1', job_info.job_name)
self.assertEqual(
project_name,
job_info.workflow_execution.workflow_info.namespace)
self.assertEqual(
workflow_name,
job_info.workflow_execution.workflow_info.workflow_name)
job_info_list = af.workflow_operation.list_jobs(we.execution_id)
self.assertEqual(1, len(job_info_list))
def run_task_function(client: NotificationClient):
with af.global_config_file(workflow_config_file()):
with af.config(task_config):
input_example = af.read_example(
example_info=example_1,
executor=faf.flink_executor.FlinkPythonExecutor(
python_object=Source()))
processed = af.transform(
input_data_list=[input_example],
executor=faf.flink_executor.FlinkPythonExecutor(
python_object=Transformer()))
af.write_example(
input_data=processed,
example_info=example_2,
executor=faf.flink_executor.FlinkPythonExecutor(
python_object=Sink()))
workflow_info = af.workflow_operation.submit_workflow(
workflow_name)
af.workflow_operation.start_new_workflow_execution(workflow_name)
while True:
with create_session() as session:
dag_run = session.query(DagRun).filter(
DagRun.dag_id == 'test_project.test_workflow').first()
if dag_run is not None and dag_run.state in State.finished:
break
else:
time.sleep(1)
def test_read_example_with_numpy_npy(self):
npy_name = 'test.npy'
np.save(file=npy_name, arr=np.arange(10))
input_example_meta = af.register_example(
name='input_numpy_example',
data_type='numpy',
data_format='npy',
support_type=ExampleSupportType.EXAMPLE_BATCH,
batch_uri=os.path.abspath(
os.path.dirname(__file__) + "/" + npy_name))
output_example_meta = af.register_example(
name='ouput_numpy_example',
data_type='numpy',
data_format='npy',
support_type=ExampleSupportType.EXAMPLE_BATCH,
batch_uri=os.path.abspath(
os.path.dirname(__file__) + '/numpy_output.npy'))
with af.config(
af.BaseJobConfig(platform='local',
engine='python',
job_name='test_npy')):
example_channel = af.read_example(example_info=input_example_meta)
af.write_example(input_data=example_channel,
example_info=output_example_meta)
workflow_id = af.run(test_util.get_project_path())
res = af.wait_workflow_execution_finished(workflow_id)
self.assertEqual(0, res)
def test_read_example_with_pandas(self):
input_example_meta = af.register_example(
name='input_pandas_example',
data_type='pandas',
data_format='csv',
support_type=ExampleSupportType.EXAMPLE_BATCH,
batch_uri=os.path.abspath(
os.path.dirname(__file__) + '/test1.csv'))
output_example_meta = af.register_example(
name='ouput_pandas_example',
data_type='pandas',
data_format='csv',
support_type=ExampleSupportType.EXAMPLE_BATCH,
batch_uri=os.path.abspath(
os.path.dirname(__file__) + '/pandas_output.csv'))
with af.config(
af.BaseJobConfig(platform='local',
engine='python',
job_name='test_csv')):
example_channel = af.read_example(example_info=input_example_meta)
af.write_example(input_data=example_channel,
example_info=output_example_meta)
workflow_id = af.run(test_util.get_project_path())
res = af.wait_workflow_execution_finished(workflow_id)
self.assertEqual(0, res)
def test_user_define_control_dependency(self):
print(sys._getframe().f_code.co_name)
trigger = af.external_trigger(name='stream_trigger')
job_config = af.BaseJobConfig('local', 'cmd_line')
job_config.job_name = 'test_cmd'
with af.config(job_config):
cmd_executor = af.user_define_operation(
output_num=0,
executor=CmdExecutor(
cmd_line="echo 'hello world' && sleep {}".format(1)))
af.user_define_control_dependency(
src=cmd_executor,
dependency=trigger,
event_key='key',
event_value='value',
event_type='name',
condition=MetCondition.NECESSARY,
action=TaskAction.START,
life=EventLife.ONCE,
value_condition=MetValueCondition.UPDATE)
workflow_id = af.submit_ai_flow()
af.get_ai_flow_client().publish_event('key', 'value1', 'name')
time.sleep(5)
af.get_ai_flow_client().publish_event('key', 'value2', 'name')
time.sleep(10)
af.stop_execution_by_id(workflow_id)
res = af.get_ai_flow_client().list_job(5, 0)
self.assertEqual(3, len(res))
def test_stream_train_component(self):
batch_input_example_meta = af.register_example(
name='stream_train_example',
support_type=ExampleSupportType.EXAMPLE_BOTH)
model_meta = af.register_model(model_name='mnist_model',
model_type=ModelType.SAVED_MODEL)
stream_input_example_meta = af.register_example(
name='stream_train_example',
support_type=ExampleSupportType.EXAMPLE_BOTH)
with af.config(
af.BaseJobConfig(platform='local',
engine='python',
job_name='stream_train')):
batch_input_example = af.read_example(
example_info=batch_input_example_meta,
executor=PythonObjectExecutor(
python_object=ReadBatchExample()))
batch_train = af.train(input_data_list=[batch_input_example],
executor=PythonObjectExecutor(
python_object=TrainBatchMnistModel()),
model_info=model_meta)
stream_input_example = af.read_example(
example_info=stream_input_example_meta,
executor=PythonObjectExecutor(
python_object=ReadStreamExample()))
stream_train = af.train(input_data_list=[stream_input_example],
executor=PythonObjectExecutor(
python_object=TrainStreamMnistModel()),
model_info=model_meta)
af.stop_before_control_dependency(stream_train, batch_train)
workflow_id = af.run(test_util.get_project_path())
res = af.wait_workflow_execution_finished(workflow_id)
self.assertEqual(0, res)
def run_flink_job():
input_file = "/test1.csv"
output_file ="/output_test1.csv"
if os.path.exists(output_file):
os.remove(output_file)
example_1 = af.create_example(name="example_1",
support_type=af.ExampleSupportType.EXAMPLE_BOTH,
batch_uri=input_file,
stream_uri=input_file,
data_format="csv")
example_2 = af.create_example(name="example_2",
support_type=af.ExampleSupportType.EXAMPLE_BOTH,
batch_uri=output_file,
stream_uri=output_file,
data_format="csv")
flink_config = faf.LocalFlinkJobConfig()
flink_config.flink_home = "/Users/chenwuchao/soft/apache/flink-1.10.0"
with af.config(flink_config):
batch_args_1: Properties = {}
ddl = """CREATE TABLE input_table (a STRING, b STRING, c STRING) WITH ('connector' = 'filesystem',
'path' = 'INPUT',
'format' = 'csv'
)"""
table_name = "input_table"
batch_args_1['ddl'] = ddl
batch_args_1['table_name'] = table_name
stream_args_1 = batch_args_1
batch_args_2: Properties = {}
ddl = """CREATE TABLE output_table (aa STRING, bb STRING) WITH ('connector' = 'filesystem',
'path' = 'OUTPUT',
'format' = 'csv'
)"""
table_name = "output_table"
batch_args_2['ddl'] = ddl
batch_args_2['table_name'] = table_name
stream_args_2 = batch_args_2
input_example = af.read_example(example_info=example_1,
exec_args=ExecuteProperties(
batch_properties=batch_args_1,
stream_properties=stream_args_1)
)
processed = af.transform(input_data_list=[input_example],
executor=faf.FlinkJavaExecutor(
java_class="com.apache.flink.ai.flow.TestTransformer"))
af.write_example(input_data=processed,
example_info=example_2,
exec_args=ExecuteProperties(
batch_properties=batch_args_2,
stream_properties=stream_args_2)
)
workflow = af.compile_workflow(test_util.get_project_path())
print(json_utils.dumps(list(workflow.jobs.values())[0]))
def build_workflow():
with af.global_config_file(project_path +
'/resources/workflow_config.yaml'):
with af.config('job_1'):
op_1 = af.user_define_operation(
af.PythonObjectExecutor(PrintHelloExecutor('job_1')))
with af.config('job_2'):
op_2 = af.user_define_operation(
af.PythonObjectExecutor(PrintHelloExecutor('job_2')))
with af.config('job_3'):
op_3 = af.user_define_operation(
af.PythonObjectExecutor(PrintHelloExecutor('job_3')))
af.stop_before_control_dependency(op_3, op_1)
af.stop_before_control_dependency(op_3, op_2)
def run_flink_spec_job():
with af.global_config_file(test_util.get_job_config_file()):
with af.config('vvp_spec_job'):
faf.vvp_job()
workflow_id = af.run(test_util.get_project_path(),
dag_id='wordcount_vvp_python',
scheduler_type=af.SchedulerType.AIFLOW)
res = af.wait_workflow_execution_finished(workflow_id)
print(res)
def test_deploy_airflow(self):
airflow_path = af.project_config().get_airflow_deploy_path()
if not os.path.exists(airflow_path):
os.makedirs(airflow_path)
with af.config(LocalPythonJobConfig(job_name="simple")):
op = af.user_define_operation(
af.PythonObjectExecutor(SimpleExecutor()))
res = af.run(test_util.get_project_path())
af.wait_workflow_execution_finished(res)
def build_and_submit_ai_flow():
with af.global_config_file(workflow_config_file()):
with af.config('task_1'):
cmd_executor = af.user_define_operation(
output_num=0,
executor=CmdExecutor(
cmd_line='echo "hello world"'.format(1)))
workflow_info = af.workflow_operation.submit_workflow(
'test_workflow')
return workflow_info.workflow_name
def test_k8s_cmd(self):
print(sys._getframe().f_code.co_name)
project_path = os.path.dirname(__file__) + '/../'
job_config = af.KubernetesCMDJobConfig()
job_config.job_name = 'test_cmd'
with af.config(job_config):
cmd_executor = af.user_define_operation(
output_num=0,
executor=CmdExecutor(
cmd_line="echo 'hello world' && sleep {}".format(1)))
code_text = af.generate_airflow_file_text(project_path, "hh")
print(code_text)
def build_workflow():
with af.global_config_file(project_path +
'/resources/workflow_config.yaml'):
with af.config('job_1'):
op_1 = af.user_define_operation(
af.PythonObjectExecutor(PrintHelloExecutor('job_1')))
with af.config('job_2'):
op_2 = af.user_define_operation(
af.PythonObjectExecutor(
SendEventExecutor(key='key_1',
value='value_1',
num=3,
post_time=20)))
af.user_define_control_dependency(op_1,
op_2,
event_key='key_1',
event_type='UNDEFINED',
event_value="value_1",
action=TaskAction.RESTART)
def test_periodic_job(self):
print(sys._getframe().f_code.co_name)
periodic_config = PeriodicConfig(periodic_type='interval',
args={'seconds': 5})
job_config = af.BaseJobConfig(platform='local', engine='cmd_line')
job_config.job_name = 'test_periodic'
job_config.periodic_config = periodic_config
with af.config(job_config):
af.user_define_operation(executor=af.CmdExecutor(
cmd_line="echo 'hello world!'"))
workflow_id = af.submit_ai_flow()
time.sleep(10)
af.stop_execution_by_id(workflow_id)
def run_task_function(client: NotificationClient):
with af.global_config_file(workflow_config_file()):
with af.config('task_2'):
executor_1 = af.user_define_operation(
af.PythonObjectExecutor(SimpleExecutor()))
with af.config('task_3'):
executor_2 = af.user_define_operation(
af.PythonObjectExecutor(SimpleExecutor()))
af.model_version_control_dependency(
src=executor_2,
dependency=executor_1,
model_name='model_1',
model_version_event_type=ModelVersionEventType.
MODEL_GENERATED)
workflow_info = af.workflow_operation.submit_workflow(
workflow_name)
af.workflow_operation.start_new_workflow_execution(workflow_name)
r_flag = True
while True:
with create_session() as session:
tes2 = session.query(TaskExecution).filter(
TaskExecution.dag_id == 'test_project.test_workflow',
TaskExecution.task_id == 'task_2').all()
if len(tes2) == 1 and r_flag:
af.register_model_version(
model='model_1',
model_path='/tmp/model/v1',
current_stage=af.ModelVersionStage.GENERATED)
r_flag = False
dag_run = session.query(DagRun).filter(
DagRun.dag_id == 'test_project.test_workflow').first()
if dag_run is not None and dag_run.state in State.finished:
break
else:
time.sleep(1)
def test_stream_evaluate_component(self):
input_example_meta = af.register_example(
name='batch_train_example',
support_type=ExampleSupportType.EXAMPLE_BATCH)
model_meta = af.register_model(model_name='mnist_model',
model_type=ModelType.SAVED_MODEL)
stream_evaluate_example_meta = af.register_example(
name='stream_evaluate_example',
support_type=ExampleSupportType.EXAMPLE_STREAM)
stream_output_file = get_file_dir(__file__) + '/stream_evaluate'
evaluate_output = af.register_artifact(name='stream_evaluate',
stream_uri=stream_output_file)
stream_evaluate_result_example_meta = af.register_example(
name='stream_evaluate_result_example',
support_type=ExampleSupportType.EXAMPLE_STREAM,
stream_uri=stream_output_file)
if os.path.exists(stream_output_file):
os.remove(stream_output_file)
with af.config(
af.BaseJobConfig(platform='local',
engine='python',
job_name='stream_evaluate')):
input_example = af.read_example(
example_info=input_example_meta,
executor=PythonObjectExecutor(
python_object=ReadBatchExample()))
batch_train = af.train(input_data_list=[input_example],
executor=PythonObjectExecutor(
python_object=TrainBatchMnistModel()),
model_info=model_meta)
stream_evaluate_example = af.read_example(
example_info=stream_evaluate_example_meta,
executor=PythonObjectExecutor(
python_object=ReadStreamExample()))
stream_evaluate = af.evaluate(
input_data_list=[stream_evaluate_example],
model_info=model_meta,
executor=PythonObjectExecutor(
python_object=EvaluateStreamMnistModel()),
output_num=1)
af.write_example(input_data=stream_evaluate,
example_info=stream_evaluate_result_example_meta,
executor=PythonObjectExecutor(
python_object=WriteStreamExample()))
af.stop_before_control_dependency(stream_evaluate, batch_train)
workflow_id = af.run(test_util.get_project_path())
res = af.wait_workflow_execution_finished(workflow_id)
self.assertEqual(0, res)
def run_task_function(client: NotificationClient):
with af.global_config_file(workflow_config_file()):
with af.config('task_1'):
cmd_executor = af.user_define_operation(
output_num=0,
executor=CmdExecutor(
cmd_line='echo "hello world" && sleep 30'.format(
1)))
workflow_info = af.workflow_operation.submit_workflow(
workflow_name)
self.assertFalse(is_paused())
af.workflow_operation.pause_workflow_scheduling(workflow_name)
self.assertTrue(is_paused())
af.workflow_operation.resume_workflow_scheduling(workflow_name)
self.assertFalse(is_paused())
def test_context(self):
global_config = af.BaseJobConfig(platform='a',
engine='b',
properties={'c': 'c'})
job_config = af.BaseJobConfig(platform='aa',
engine='bb',
properties={'cc': 'cc'})
with af.global_config(global_config):
with af.config(job_config):
af.user_define_operation(executor=None)
node_list = list(_default_ai_graph.nodes.values())
self.assertEqual('bb', node_list[0].properties[ENGINE_NAME])
self.assertEqual('cc', node_list[0].config.properties["cc"])
self.assertEqual('c', node_list[0].config.properties["c"])
self.assertEqual('bb', node_list[0].config.engine)
self.assertEqual('aa', node_list[0].config.platform)
def test_stream_with_external_trigger_with_model_control(self):
print(sys._getframe().f_code.co_name)
model_name = 'test_create_model_version'
model_desc = 'test create model version'
response = af.register_model(model_name=model_name,
model_type=af.ModelType.CHECKPOINT,
model_desc=model_desc)
trigger = af.external_trigger(name='stream_trigger')
job_config = af.BaseJobConfig('local', 'cmd_line')
job_config.job_name = 'test_cmd'
with af.config(job_config):
cmd_executor = af.user_define_operation(
output_num=0,
executor=CmdExecutor(
cmd_line="echo 'hello world' && sleep {}".format(1)))
af.model_version_control_dependency(
src=cmd_executor,
dependency=trigger,
model_name=model_name,
model_version_event_type='MODEL_DEPLOYED')
workflow_id = af.submit_ai_flow()
model_path1 = 'fs://source1.pkl'
model_metric1 = 'http://metric1'
model_flavor1 = '{"flavor.version":1}'
version_desc1 = 'test create model version1'
time.sleep(1)
response = af.register_model_version(
model=model_name,
model_path=model_path1,
model_metric=model_metric1,
model_flavor=model_flavor1,
version_desc=version_desc1,
current_stage=af.ModelVersionStage.DEPLOYED)
time.sleep(5)
response = af.register_model_version(
model=model_name,
model_path=model_path1,
model_metric=model_metric1,
model_flavor=model_flavor1,
version_desc=version_desc1,
current_stage=af.ModelVersionStage.DEPLOYED)
time.sleep(10)
af.stop_execution_by_id(workflow_id)
res = af.get_ai_flow_client().list_job(5, 0)
self.assertEqual(3, len(res))