def register(stub, model_name, mar_set_str):
mar_set = set()
if mar_set_str:
mar_set = set(mar_set_str.split(','))
marfile = f"{model_name}.mar"
print(f"## Check {marfile} in mar_set :", mar_set)
if marfile not in mar_set:
marfile = "https://torchserve.s3.amazonaws.com/mar_files/{}.mar".format(
model_name)
print(f"## Register marfile:{marfile}\n")
params = {
'url': marfile,
'initial_workers': 1,
'synchronous': True,
'model_name': model_name
}
try:
response = stub.RegisterModel(
management_pb2.RegisterModelRequest(**params))
print(f"Model {model_name} registered successfully")
except grpc.RpcError as e:
print(f"Failed to register model {model_name}.")
print(str(e.details()))
exit(1)
def test_inference_apis():
with open(os.path.dirname(__file__) + inference_data_json, 'rb') as f:
test_data = json.loads(f.read())
for item in test_data:
if item['url'].startswith('{{mar_path_'):
path = test_utils.mar_file_table[item['url'][2:-2]]
else:
path = item['url']
managment_stub = test_gRPC_utils.get_management_stub()
response = managment_stub.RegisterModel(
management_pb2.RegisterModelRequest(url=path,
initial_workers=item['worker'],
synchronous=bool(
item['synchronous']),
model_name=item['model_name']))
print(response.msg)
model_input = os.path.dirname(__file__) + "/../" + item['file']
prediction = __infer(test_gRPC_utils.get_inference_stub(),
item['model_name'], model_input)
print("Prediction is : ", str(prediction))
if 'expected' in item:
try:
prediction = literal_eval(prediction)
except SyntaxError:
pass
if isinstance(prediction, list) and 'tolerance' in item:
assert len(prediction) == len(item['expected'])
for i in range(len(prediction)):
assert __get_change(
prediction[i], item['expected'][i]) < item['tolerance']
elif isinstance(prediction, dict) and 'tolerance' in item:
assert len(prediction) == len(item['expected'])
for key in prediction:
assert __get_change(
prediction[key],
item['expected'][key]) < item['tolerance']
else:
assert str(prediction) == str(item['expected'])
response = managment_stub.UnregisterModel(
management_pb2.UnregisterModelRequest(
model_name=item['model_name'], ))
print(response.msg)
def register(stub, model_name, local=False):
if local:
url = "{}".format(model_name)
else:
url = "https://torchserve.s3.amazonaws.com/mar_files/{}.mar".format(
model_name)
params = {
'url': url,
'initial_workers': 1,
'synchronous': True,
'model_name': model_name.replace(".mar", "")
}
response = stub.RegisterModel(
management_pb2.RegisterModelRequest(**params))
return response
def register(stub, model_name, url):
print("Registering ", model_name)
params = {
'url': url,
'initial_workers': 1,
'synchronous': True,
'model_name': model_name
}
try:
response = stub.RegisterModel(
management_pb2.RegisterModelRequest(**params))
print(f"Model {model_name} registered successfully")
except grpc.RpcError as e:
print(f"Failed to register model {model_name}.")
print(str(e.details()))
exit(1)
def register(stub, model_name):
params = {
'url':
"https://torchserve.s3.amazonaws.com/mar_files/{}.mar".format(
model_name),
'initial_workers':
1,
'synchronous':
True,
'model_name':
model_name
}
try:
response = stub.RegisterModel(
management_pb2.RegisterModelRequest(**params))
print(f"Model {model_name} registered successfully")
except grpc.RpcError as e:
print(f"Failed to register model {model_name}.")
print(str(e.details()))
exit(1)
