def __init__(self, gpu_id, model_path):
self.logger = get_logger()
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph)
with self.graph.as_default():
self.x = tf.ones(shape=(3, 3), dtype=tf.float32)
self.y = tf.ones(shape=(3, 3), dtype=tf.float32)
def __init__(self, port, host='0.0.0.0', timeout_ms=1000, debug=False, asynchronous=False):
self.host = host
self.port = port
self.transport = None
self.client = None
self.asynchronous = asynchronous
self.results = {}
self.logger = get_logger(colored_worker_name('CLIENT'),
mode='debug' if debug else 'info')
from funicorn.client import ClientRPC
from funicorn.logger import get_logger
from funicorn.utils import colored_network_name
from concurrent.futures import ThreadPoolExecutor
import time
import numpy as np
logger = get_logger(colored_network_name('CLIENT'))
def sequential_test():
print('=' * 100)
N = 10000
print('Sequential Requests with {}\n'.format(N))
rpc = ClientRPC(port=8001)
t = time.time()
for i in range(N):
rpc.ping()
logger.info('Latency: {}'.format((time.time() - t) / N))
def parallel_test():
print('=' * 100)
num_clients = 30
num_requests_per_client = 1000
print(
'Parallel Requests with num_clients: {} - num_requests_per_client: {}\n'
.format(num_clients, num_requests_per_client))
def ping(num_requests_per_client):
def __init__(self, gpu_id, model_path):
self.logger = get_logger()