def table_with_input():
prep_data()
testing_server = start_zk_server()
stream_env = StreamExecutionEnvironment.get_execution_environment()
table_env = TableEnvironment.get_table_environment(stream_env)
stream_env.set_parallelism(2)
train_data_path = "file://" + get_root_path(
) + "/examples/target/data/train/"
paths = [train_data_path + "0.tfrecords", train_data_path + "1.tfrecords"]
out_row_type = RowType([StringType(), IntegerType()],
["image_raw", "label"])
table_src = TFRTableSource(
paths=paths,
epochs=1,
out_row_type=out_row_type,
converters=[ScalarConverter.FIRST, ScalarConverter.ONE_HOT])
input_table = table_src.register_table(table_env=table_env)
tensorflow_on_flink_table.train(num_worker=1,
num_ps=1,
func=mnist_dist_with_input.map_fun,
properties=build_props(),
stream_env=stream_env,
table_env=table_env,
input_table=input_table)
table_env.generate_stream_graph()
stream_env.execute()
testing_server.stop()
def table_no_input():
prep_data()
testing_server = start_zk_server()
tensorflow_on_flink_table.train(num_worker=1,
num_ps=1,
func=mnist_dist.map_fun,
properties=build_props(),
zk_conn="localhost:2181")
testing_server.stop()
def generate_model():
if not os.path.exists(export_path):
tensorflow_on_flink_table.train(num_worker=1,
num_ps=1,
func=mnist_dist.map_fun,
properties=build_props('0'))