def on_start(self):
"""
get token
:return:
"""
print("on start")
self.oauth_enabled = getEnviron('OAUTH_ENABLED', "false")
self.oauth_key = getEnviron('OAUTH_KEY', "key")
self.oauth_secret = getEnviron('OAUTH_SECRET', "secret")
self.data_size = int(getEnviron('DATA_SIZE', "1"))
self.send_feedback = int(getEnviron('SEND_FEEDBACK', "1"))
self.oauth_endpoint = getEnviron('OAUTH_ENDPOINT',
"http://127.0.0.1:30015")
self.endpoint = getEnviron('API_ENDPOINT', "external")
#self.grpc_endpoint = getEnviron('GRPC_ENDPOINT',"127.0.0.1:30017")
if self.oauth_enabled == "true":
self.get_token()
else:
self.access_token = "NONE"
channel = grpc.insecure_channel(HOST)
if self.endpoint == "external":
self.stub = prediction_pb2_grpc.SeldonStub(channel)
else:
self.stub = prediction_pb2_grpc.ModelStub(channel)
self.rewardProbas = [0.5, 0.2, 0.9, 0.3, 0.7]
self.routeRewards = {}
self.routesSeen = []
def run(args):
contract = json.load(open(args.contract, 'r'))
contract = unfold_contract(contract)
feature_names = [feature["name"] for feature in contract["features"]]
REST_url = "http://" + args.host + ":" + str(args.port) + "/predict"
for i in range(args.n_requests):
batch = generate_batch(contract, args.batch_size)
if args.prnt:
print('-' * 40)
print("SENDING NEW REQUEST:")
if not args.grpc:
headers = {}
REST_request = gen_REST_request(batch,
features=feature_names,
tensor=args.tensor)
if args.prnt:
print(REST_request)
if args.oauth_key:
token = get_token(args)
headers = {'Authorization': 'Bearer ' + token}
response = requests.post("http://" + args.host + ":" +
str(args.port) +
"/api/v0.1/predictions",
json=REST_request,
headers=headers)
else:
response = requests.post(
"http://" + args.host + ":" + str(args.port) +
args.ambassador_path + "/api/v0.1/predictions",
json=REST_request,
headers=headers)
jresp = response.json()
if args.prnt:
print("RECEIVED RESPONSE:")
print(jresp)
print()
else:
GRPC_request = gen_GRPC_request(batch,
features=feature_names,
tensor=args.tensor)
if args.prnt:
print(GRPC_request)
channel = grpc.insecure_channel('{}:{}'.format(
args.host, args.port))
stub = prediction_pb2_grpc.ModelStub(channel)
response = stub.Predict(GRPC_request)
if args.prnt:
print("RECEIVED RESPONSE:")
print(response)
print()
def run(args):
contract = json.load(open(args.contract, 'r'))
contract = unfold_contract(contract)
feature_names = [feature["name"] for feature in contract["features"]]
REST_url = "http://" + args.host + ":" + str(args.port) + "/predict"
for i in range(args.n_requests):
batch = generate_batch(contract, args.batch_size)
if args.prnt:
print('-' * 40)
print("SENDING NEW REQUEST:")
if not args.grpc:
REST_request = gen_REST_request(batch,
features=feature_names,
tensor=args.tensor)
if args.prnt:
print(REST_request)
t1 = time()
response = requests.post(REST_url,
data={
"json": json.dumps(REST_request),
"isDefault": True
})
t2 = time()
jresp = response.json()
if args.prnt:
print("RECEIVED RESPONSE:")
print(jresp)
print()
print("Time " + str(t2 - t1))
else:
GRPC_request = gen_GRPC_request(batch,
features=feature_names,
tensor=args.tensor)
if args.prnt:
print(GRPC_request)
channel = grpc.insecure_channel('{}:{}'.format(
args.host, args.port))
stub = prediction_pb2_grpc.ModelStub(channel)
response = stub.Predict(GRPC_request)
if args.prnt:
print("RECEIVED RESPONSE:")
print(response)
print()
def on_start(self):
print("on_start")
self.oauth_enabled = getEnviron('OAUTH_ENABLED', "false")
self.oauth_key = getEnviron('OAUTH_KEY', "key")
self.oauth_secret = getEnviron('OAUTH_SECRET', "secret")
self.data_size = int(getEnviron('DATA_SIZE', "1"))
self.send_feedback = int(getEnviron('SEND_FEEDBACK', "0"))
self.endpoint = getEnviron('API_ENDPOINT', "external")
if self.oauth_enabled == "true":
self.get_token()
else:
self.access_token = "NONE"
channel = grpc.insecure_channel(HOST)
if self.endpoint == "external":
self.stub = prediction_pb2_grpc.SeldonStub(channel)
else:
self.stub = prediction_pb2_grpc.ModelStub(channel)
self.mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
def run_predict(args):
contract = json.load(open(args.contract, 'r'))
contract = unfold_contract(contract)
feature_names = [feature["name"] for feature in contract["features"]]
REST_url = "http://" + args.host + ":" + str(args.port) + "/predict"
for i in range(args.n_requests):
batch = generate_batch(contract, args.batch_size, 'features')
if args.prnt:
print('-' * 40)
print("SENDING NEW REQUEST:")
if not args.grpc and not args.fbs:
REST_request = gen_REST_request(batch,
features=feature_names,
tensor=args.tensor)
if args.prnt:
print(REST_request)
t1 = time()
response = requests.post(REST_url,
data={
"json": json.dumps(REST_request),
"isDefault": True
})
t2 = time()
jresp = response.json()
if args.prnt:
print("RECEIVED RESPONSE:")
print(jresp)
print()
print("Time " + str(t2 - t1))
elif args.grpc:
GRPC_request = gen_GRPC_request(batch,
features=feature_names,
tensor=args.tensor)
if args.prnt:
print(GRPC_request)
channel = grpc.insecure_channel('{}:{}'.format(
args.host, args.port))
stub = prediction_pb2_grpc.ModelStub(channel)
response = stub.Predict(GRPC_request)
if args.prnt:
print("RECEIVED RESPONSE:")
print(response)
print()
elif args.fbs:
import socket
import struct
from tester_flatbuffers import NumpyArrayToSeldonRPC, SeldonRPCToNumpyArray
data = NumpyArrayToSeldonRPC(batch, feature_names)
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((args.host, args.port))
totalsent = 0
MSGLEN = len(data)
print("Will send", MSGLEN, "bytes")
while totalsent < MSGLEN:
sent = s.send(data[totalsent:])
if sent == 0:
raise RuntimeError("socket connection broken")
totalsent = totalsent + sent
data = s.recv(4)
obj = struct.unpack('<i', data)
len_msg = obj[0]
data = s.recv(len_msg)
arr = SeldonRPCToNumpyArray(data)
print(arr)
def run_send_feedback(args):
contract = json.load(open(args.contract, 'r'))
contract = unfold_contract(contract)
feature_names = [feature["name"] for feature in contract["features"]]
response_names = [feature["name"] for feature in contract["targets"]]
REST_url = "http://" + args.host + ":" + str(args.port) + "/send-feedback"
for i in range(args.n_requests):
batch = generate_batch(contract, args.batch_size, 'features')
response = generate_batch(contract, args.batch_size, 'targets')
if args.prnt:
print('-' * 40)
print("SENDING NEW REQUEST:")
if not args.grpc and not args.fbs:
REST_request = gen_REST_request(batch,
features=feature_names,
tensor=args.tensor)
REST_response = gen_REST_request(response,
features=response_names,
tensor=args.tensor)
reward = 1.0
REST_feedback = {
"request": REST_request,
"response": REST_response,
"reward": reward
}
if args.prnt:
print(REST_feedback)
t1 = time()
response = requests.post(REST_url,
data={"json": json.dumps(REST_feedback)})
t2 = time()
if args.prnt:
print("Time " + str(t2 - t1))
print(response)
elif args.grpc:
GRPC_request = gen_GRPC_request(batch,
features=feature_names,
tensor=args.tensor)
GRPC_response = gen_GRPC_request(response,
features=response_names,
tensor=args.tensor)
reward = 1.0
GRPC_feedback = prediction_pb2.Feedback(request=GRPC_request,
response=GRPC_response,
reward=reward)
if args.prnt:
print(GRPC_feedback)
channel = grpc.insecure_channel('{}:{}'.format(
args.host, args.port))
stub = prediction_pb2_grpc.ModelStub(channel)
response = stub.SendFeedback(GRPC_feedback)
if args.prnt:
print("RECEIVED RESPONSE:")
print()