通过API构图并执行

import dataflow as df
import numpy as np

# 系统初始化
options = {
    "ge.exec.deviceId":"0",
    "ge.experiment.data_flow_deploy_info_path":"./data_flow_deploy_info.json",
    "ge.socVersion": "AscendXXX"  # 根据环境修改version
}
df.init(options)

# 定义FlowData
data0 = df.FlowData()
data1 = df.FlowData()

# 定义FuncProcessPoint实现Add功能并添加到FlowNode中
pp0 = df.FuncProcessPoint(compile_config_path='config/add_func.json')
add_node = df.FlowNode(input_num=2, output_num=1)
add_node.add_process_point(pp0)

# 构建FlowData和FlowNode的连边关系
add_out = add_node(data0, data1)

# 通过FlowOut构建FlowGraph
dag = df.FlowGraph([add_out])

# 调用FlowGraph.feed_data填充输入
flow_info = df.FlowInfo()
dag.feed_data({data0:np.array([[1, 2]], dtype=np.int32), data1:np.array([[2, 3]], dtype=np.int32)}, flow_info)

# 调用FlowGraph.fetch_data获取输出
print("dataflow fetch result:", dag.fetch_data())

# 释放系统资源 
df.finalize()