def tearDown(self) -> None:
print('tearDown')
store = SqlAlchemyStore(_SQLITE_DB_URI)
base.metadata.drop_all(store.db_engine)
base.metadata.create_all(store.db_engine)
af.default_graph().clear_graph()
res = client.list_job(page_size=10, offset=0)
self.assertIsNone(res)
def setUp(self):
master._clear_db()
db_utils.clear_db_jobs()
db_utils.clear_db_dags()
db_utils.clear_db_serialized_dags()
db_utils.clear_db_runs()
db_utils.clear_db_task_execution()
db_utils.clear_db_message()
db_utils.clear_db_jobs()
af.default_graph().clear_graph()
def build_ai_graph() -> AIGraph:
with af.engine('cmd_line'):
p_list = []
for i in range(3):
p = af.user_define_operation(
executor=CmdExecutor(cmd_line="echo 'hello_{}' && sleep 3".format(i)))
p_list.append(p)
af.stop_before_control_dependency(p_list[0], p_list[1])
af.stop_before_control_dependency(p_list[0], p_list[2])
return af.default_graph()
def setUp(self):
TestProject.master._clear_db()
af.default_graph().clear_graph()
def run_flink_predict_job():
input_file = "/test1.csv"
output_file = "/output_test2.csv"
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 = ''
with af.config(flink_config):
batch_args_1: Properties = {}
ddl = """CREATE TABLE input_table (a INT, b INT, c INT) 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 INT, cc INT) 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=ExecuteArgs(
batch_properties=batch_args_1,
stream_properties=stream_args_1))
model_meta = af.ModelMeta(name="test", model_type="saved_model")
model_version = af.ModelVersionMeta(version="11111",
model_path="./tmp/saved_model/",
model_metric="./tmp/saved_model/",
model_id=0)
processed = af.predict(
input_data_list=[input_example],
model_info=model_meta,
model_version_info=model_version,
executor=faf.flink_executor.FlinkJavaExecutor(
java_class="com.apache.flink.ai.flow.TestPredict"))
af.write_example(input_data=processed,
example_info=example_2,
exec_args=ExecuteArgs(
batch_properties=batch_args_2,
stream_properties=stream_args_2))
g = af.default_graph()
workflow = af.compile_workflow(project_path=test_util.get_project_path())
print(dumps(list(workflow.jobs.values())[0]))