def grpc_request_ambassador(deploymentName,
namespace,
endpoint="localhost:8004",
data_size=5,
rows=1,
data=None,
headers=None):
if data is None:
shape, arr = create_random_data(data_size, rows)
else:
shape = data.shape
arr = data.flatten()
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=shape, values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
channel = grpc.insecure_channel(endpoint)
stub = prediction_pb2_grpc.SeldonStub(channel)
if namespace is None:
metadata = [('seldon', deploymentName)]
else:
metadata = [('seldon', deploymentName), ('namespace', namespace)]
if not headers is None:
for k in headers:
metadata.append((k, headers[k]))
response = stub.Predict(request=request, metadata=metadata)
return response
def grpc_request_ambassador(
deployment_name,
namespace,
endpoint="localhost:8004",
data_size=5,
rows=1,
data=None,
):
if data is None:
shape, arr = create_random_data(data_size, rows)
else:
shape = data.shape
arr = data.flatten()
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=shape, values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
channel = grpc.insecure_channel(endpoint)
stub = prediction_pb2_grpc.SeldonStub(channel)
if namespace is None:
metadata = [("seldon", deployment_name)]
else:
metadata = [("seldon", deployment_name), ("namespace", namespace)]
try:
response = stub.Predict(request=request, metadata=metadata)
channel.close()
return response
except Exception as e:
channel.close()
raise e
def grpc_request_ambassador_bindata(deploymentName,namespace,endpoint="localhost:8080",data=None):
request = prediction_pb2.SeldonMessage(binData = data)
channel = grpc.insecure_channel(endpoint)
stub = prediction_pb2_grpc.SeldonStub(channel)
if namespace is None:
metadata = [('seldon',deploymentName)]
else:
metadata = [('seldon',deploymentName),('namespace',namespace)]
response = stub.Predict(request=request,metadata=metadata)
return response
def grpc_request_api_gateway(oauth_key,oauth_secret,namespace,rest_endpoint="localhost:8002",grpc_endpoint="localhost:8003",data_size=5,rows=1,data=None):
token = get_token(oauth_key,oauth_secret,namespace,rest_endpoint)
if data is None:
shape, arr = create_random_data(data_size,rows)
else:
shape = data.shape
arr = data.flatten()
datadef = prediction_pb2.DefaultData(
tensor = prediction_pb2.Tensor(
shape = shape,
values = arr
)
)
request = prediction_pb2.SeldonMessage(data = datadef)
channel = grpc.insecure_channel(grpc_endpoint)
stub = prediction_pb2_grpc.SeldonStub(channel)
metadata = [('oauth_token', token)]
response = stub.Predict(request=request,metadata=metadata)
return response
def grpc_request_ambassador_metadata(
deployment_name, namespace, endpoint=API_AMBASSADOR, model_name=None
):
if model_name is None:
request = empty_pb2.Empty()
else:
request = prediction_pb2.SeldonModelMetadataRequest(name=model_name)
channel = grpc.insecure_channel(endpoint)
stub = prediction_pb2_grpc.SeldonStub(channel)
if namespace is None:
metadata = [("seldon", deployment_name)]
else:
metadata = [("seldon", deployment_name), ("namespace", namespace)]
try:
if model_name is None:
response = stub.GraphMetadata(request=request, metadata=metadata)
else:
response = stub.ModelMetadata(request=request, metadata=metadata)
channel.close()
return response
except Exception as e:
channel.close()
raise e
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:
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.SeldonStub(channel)
if args.oauth_key:
token = get_token(args)
metadata = [('oauth_token', token)]
response = stub.Predict(request=GRPC_request,
metadata=metadata)
else:
response = stub.Predict(request=GRPC_request)
if args.prnt:
print("RECEIVED RESPONSE:")
print(response)
print()
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:
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)
if args.oauth_key:
token = get_token(args)
headers = {'Authorization': 'Bearer ' + token}
response = requests.post("http://" + args.host + ":" +
str(args.port) + "/api/v0.1/feedback",
json=REST_feedback,
headers=headers)
else:
response = requests.post(
"http://" + args.host + ":" + str(args.port) +
args.ambassador_path + "/api/v0.1/feedback",
json=REST_feedback,
headers=headers)
if args.prnt:
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.SeldonStub(channel)
if args.oauth_key:
token = get_token(args)
metadata = [('oauth_token', token)]
response = stub.SendFeedback(request=GRPC_feedback,
metadata=metadata)
else:
response = stub.SendFeedback(request=GRPC_feedback)
if args.prnt:
print("RECEIVED RESPONSE:")
print()