benchmark.py

import sys
import os
import yaml
import requests
import time
import json
import cv2
import base64
try:
    from paddle_serving_server_gpu.pipeline import PipelineClient
except ImportError:
    from paddle_serving_server.pipeline import PipelineClient
import numpy as np
from paddle_serving_client.utils import MultiThreadRunner
from paddle_serving_client.utils import benchmark_args, show_latency

def cv2_to_base64(image):
    return base64.b64encode(image).decode('utf8')

def parse_benchmark(filein, fileout):
    with open(filein, "r") as fin:
        res = yaml.load(fin)
        del_list = []
        for key in res["DAG"].keys():
            if "call" in key:
                del_list.append(key)
        for key in del_list:
            del res["DAG"][key]
    with open(fileout, "w") as fout:
        yaml.dump(res, fout, default_flow_style=False)

def gen_yml(device, gpu_id):
    fin = open("config.yml", "r")
    config = yaml.load(fin)
    fin.close()
    config["dag"]["tracer"] = {"interval_s": 30}
    if device == "gpu":
        config["op"]["ppyolo_mbv3"]["local_service_conf"]["device_type"] = 1
        config["op"]["ppyolo_mbv3"]["local_service_conf"]["devices"] = gpu_id
    with open("config2.yml", "w") as fout: 
        yaml.dump(config, fout, default_flow_style=False)

def run_http(idx, batch_size):
    print("start thread ({})".format(idx))
    url = "http://127.0.0.1:18082/ppyolo_mbv3/prediction"
    with open(os.path.join(".", "000000570688.jpg"), 'rb') as file:
        image_data1 = file.read()
    image = cv2_to_base64(image_data1)
    latency_list = []
    start = time.time()
    total_num = 0
    while True:
        l_start = time.time()
        data = {"key": [], "value": []}
        for j in range(batch_size):
            data["key"].append("image_" + str(j))
            data["value"].append(image)
        r = requests.post(url=url, data=json.dumps(data))
        l_end = time.time()
        total_num += 1
        end = time.time()
        latency_list.append(l_end * 1000 - l_start * 1000)
        if end - start > 70:
            #print("70s end")
            break
    return [[end - start], latency_list, [total_num]]

def multithread_http(thread, batch_size):
    multi_thread_runner = MultiThreadRunner()
    start = time.time()
    result = multi_thread_runner.run(run_http, thread, batch_size)
    end = time.time()
    total_cost = end - start
    avg_cost = 0
    total_number = 0
    for i in range(thread):
        avg_cost += result[0][i]
        total_number += result[2][i]
    avg_cost = avg_cost / thread
    print("Total cost: {}s".format(total_cost))
    print("Each thread cost: {}s. ".format(avg_cost))
    print("Total count: {}. ".format(total_number))
    print("AVG QPS: {} samples/s".format(batch_size * total_number /
                                         total_cost))
    show_latency(result[1])

def run_rpc(thread, batch_size):
    pass

def multithread_rpc(thraed, batch_size):
    multi_thread_runner = MultiThreadRunner()
    result = multi_thread_runner.run(run_rpc , thread, batch_size)

if __name__ == "__main__":
    if sys.argv[1] == "yaml":
        mode = sys.argv[2] # brpc/  local predictor
        thread = int(sys.argv[3])
        device = sys.argv[4]
        gpu_id = sys.argv[5]
        gen_yml(device, gpu_id)
    elif sys.argv[1] == "run":
        mode = sys.argv[2] # http/ rpc
        thread = int(sys.argv[3])
        batch_size = int(sys.argv[4])
        if mode == "http":
            multithread_http(thread, batch_size)
        elif mode == "rpc":
            multithread_rpc(thread, batch_size)
    elif sys.argv[1] == "dump":
        filein = sys.argv[2]
        fileout = sys.argv[3]
        parse_benchmark(filein, fileout)