def run_reload_one_config():
"""
测试gRPC的reloadconfig接口
:return:
"""
channel = grpc.insecure_channel('yourip:port')
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest(
) ##message ReloadConfigRequest
model_server_config = model_server_config_pb2.ModelServerConfig()
config_list = model_server_config_pb2.ModelConfigList(
) ##message ModelConfigList
####try to add one config
one_config = config_list.config.add() #
one_config.name = "test1"
one_config.base_path = "/home/model/model1"
one_config.model_platform = "tensorflow"
model_server_config.model_config_list.CopyFrom(config_list) #one of
request.config.CopyFrom(model_server_config)
print(request.IsInitialized())
print(request.ListFields())
responese = stub.HandleReloadConfigRequest(request, 10)
if responese.status.error_code == 0:
print("reload sucessfully")
else:
print("reload error!")
print(responese.status.error_code)
print(responese.status.error_message)
def run():
channel = grpc.insecure_channel(sys.argv[1])
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest() ##message ReloadConfigRequest
model_server_config = model_server_config_pb2.ModelServerConfig()
config_list = model_server_config_pb2.ModelConfigList()##message ModelConfigList
one_config = config_list.config.add() #####try to add one model config
one_config.name= "svm_cls"
one_config.base_path = "/models/svm_cls"
one_config.model_platform="tensorflow"
#one_config.model_version_policy.specific.versions.append(4)
model_server_config.model_config_list.CopyFrom(config_list)
request.config.CopyFrom(model_server_config)
#print(request.IsInitialized())
#print(request.ListFields())
responese = stub.HandleReloadConfigRequest(request,10)
if responese.status.error_code == 0:
print("Reload sucessfully")
else:
print("Reload failed!")
print(responese.status.error_code)
print(responese.status.error_message)
def parse_config_file():
"""
测试读取config_file并回写
:return:
"""
with open("model_config.ini", "r") as f:
config_ini = f.read()
print(config_ini)
channel = grpc.insecure_channel('yourip:port')
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest(
) ##message ReloadConfigRequest
model_server_config = model_server_config_pb2.ModelServerConfig()
x = text_format.Parse(text=config_ini,
message=model_server_config) # 非官方认证的方法
x = text_format.MessageToString(model_server_config)
with open("x.txt", "w+") as f:
f.write(x)
print(x)
print(model_server_config.IsInitialized())
request.config.CopyFrom(model_server_config)
# print(request.ListFields())
responese = stub.HandleReloadConfigRequest(request, 10)
if responese.status.error_code == 0:
print("reload sucessfully")
else:
print("reload error!")
print(responese.status.error_code)
print(responese.status.error_message)
def updateConfigurations():
channel = grpc.insecure_channel(const.host + ":" + const.port)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest()
model_server_config = model_server_config_pb2.ModelServerConfig()
# Create a config to add to the list of served models
configurations = open("models.conf", "r").read()
config_list = model_server_config_pb2.ModelConfigList()
model_server_config = text_format.Parse(text=configurations,
message=model_server_config)
request.config.CopyFrom(model_server_config)
print(request.IsInitialized())
print(request.ListFields())
response = stub.HandleReloadConfigRequest(request, 10)
if response.status.error_code == 0:
return {"status": 200, "message": "Reload sucessfully"}
else:
return {
"status": response.status.error_code,
"message": response.status.error_message
}
def __init__(self, endpoint: Text, model_name: Text): # Note that the channel instance is automatically closed (unsubscribed) on # deletion, so we don't have to manually close this on __del__. self._channel = grpc.insecure_channel(endpoint) self._model_name = model_name self._model_service = model_service_pb2_grpc.ModelServiceStub(self._channel) self._prediction_service = prediction_service_pb2_grpc.PredictionServiceStub(self._channel) # pylint: disable=line-too-long
def read_add_rewrite_config():
"""
测试读取一个config文件，新加一个配置信息，并且reload过后回写
test:read a config_file and add a new model, then reload and rewrite to the config_file
:return:
"""
with open("model_config.ini", "r") as f:
config_ini = f.read()
#print(config_ini)
model_server_config = model_server_config_pb2.ModelServerConfig()
model_server_config = text_format.Parse(text=config_ini,
message=model_server_config)
one_config = model_server_config.model_config_list.config.add(
) # add one more config
one_config.name = "test2"
one_config.base_path = "/home/sparkingarthur/tools/tf-serving-custom/serving/my_models/test2"
one_config.model_platform = "tensorflow"
print(model_server_config)
channel = grpc.insecure_channel('10.200.24.101:8009')
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest()
request.config.CopyFrom(model_server_config)
responese = stub.HandleReloadConfigRequest(request, 10)
if responese.status.error_code == 0:
print("reload sucessfully")
else:
print("reload error!")
print(responese.status.error_code)
print(responese.status.error_message)
new_config = text_format.MessageToString(model_server_config)
with open("model_config.ini", "w+") as f:
f.write(new_config)
def __init__(self, address: str):
"""
TensorFlow Serving API for loading/unloading/reloading TF models and for running predictions.
Extra arguments passed to the tensorflow/serving container:
* --max_num_load_retries=0
* --load_retry_interval_micros=30000000 # 30 seconds
* --grpc_channel_arguments="grpc.max_concurrent_streams=<processes-per-api-replica>*<threads-per-process>" when inf == 0, otherwise
* --grpc_channel_arguments="grpc.max_concurrent_streams=<threads-per-process>" when inf > 0.
Args:
address: An address with the "host:port" format.
"""
if not tensorflow_dependencies_installed:
raise NameError("tensorflow_serving_api and tensorflow packages not installed")
self.address = address
self.models = (
{}
) # maps the model ID to the model metadata (signature def, signature key and so on)
self.channel = grpc.insecure_channel(self.address)
self._service = model_service_pb2_grpc.ModelServiceStub(self.channel)
self._pred = prediction_service_pb2_grpc.PredictionServiceStub(self.channel)
def update():
config_file = "models/model.config"
host_port = "localhost:8500"
channel = grpc.insecure_channel(host_port)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest()
# read config file
config_content = open(config_file, "r").read()
model_server_config = model_server_config_pb2.ModelServerConfig()
model_server_config = text_format.Parse(text=config_content, message=model_server_config)
#print model_server_config.model_config_list.config
#print(request.IsInitialized())
#print(request.ListFields())
request.config.CopyFrom(model_server_config)
request_response = stub.HandleReloadConfigRequest(request, 10)
if request_response.status.error_code == 0:
print("TF Serving config file updated.")
else:
print("Failed to update config file.")
print(request_response.status.error_code)
print(request_response.status.error_message)
def main(_):
if MODE.STATUS == FLAGS.mode:
request = get_model_status_pb2.GetModelStatusRequest()
request.model_spec.name = 'detection'
request.model_spec.signature_name = 'serving_default'
elif MODE.CONFIG == FLAGS.mode:
request = model_management_pb2.ReloadConfigRequest()
config = request.config.model_config_list.config.add()
config.name = 'detection'
config.base_path = '/models/detection/detection'
config.model_platform = 'tensorflow'
config.model_version_policy.specific.versions.append(5)
config.model_version_policy.specific.versions.append(7)
config2 = request.config.model_config_list.config.add()
config2.name = 'pascal'
config2.base_path = '/models/detection/pascal'
config2.model_platform = 'tensorflow'
elif MODE.ZOOKEEPER == FLAGS.mode:
zk = KazooClient(hosts="10.10.67.225:2181")
zk.start()
zk.ensure_path('/serving/cunan')
zk.set(
'/serving/cunan',
get_config('detection', 5, 224, 'serving_default',
','.join(get_classes('model_data/cci.names')),
"10.12.102.32:8000"))
return
for address in FLAGS.addresses:
channel = grpc.insecure_channel(address)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
if MODE.STATUS == FLAGS.mode:
result = stub.GetModelStatus(request)
elif MODE.CONFIG == FLAGS.mode:
result = stub.HandleReloadConfigRequest(request)
print(result)
def main(config_path, host):
models = load_config(config_path)
channel = grpc.insecure_channel(host)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest()
model_server_config = model_server_config_pb2.ModelServerConfig()
config_list = model_server_config_pb2.ModelConfigList()
for model in models:
image_config = config_list.config.add()
image_config.name = model['config']['name']
image_config.base_path = model['config']['base_path']
image_config.model_platform = model['config']['model_platform']
model_server_config.model_config_list.CopyFrom(config_list)
request.config.CopyFrom(model_server_config)
print(request.ListFields())
print('Sending request')
response = stub.HandleReloadConfigRequest(request, 30)
if response.status.error_code == 0:
print('Reload successful')
else:
print('Reload failed!')
print(response.status.error_code)
print(response.status.error_message)
def _create_channel(address: str, service: int):
channel = grpc.insecure_channel(address)
if service == MODEL_SERVICE:
return model_service_pb2_grpc.ModelServiceStub(channel)
elif service == PREDICTION_SERVICE:
return prediction_service_pb2_grpc.PredictionServiceStub(channel)
return None
def __initialize(self):
setproctitle.setproctitle('tf-serving')
tf_serving_config_file = './config/tf_serving.json'
with open(tf_serving_config_file, 'r') as fp:
serving_config = json.load(fp)
grpc_port = serving_config['gprc_port']
use_batch = serving_config['use_batch']
options = [('grpc.max_send_message_length', 1024 * 1024 * 1024), ('grpc.max_receive_message_length', 1024 * 1024 * 1024)]
model_config_file = './config/tf_serving_model.conf'
cmd = 'tensorflow_model_server --port={} --rest_api_port={} --model_config_file={}'.format(grpc_port-1, grpc_port, model_config_file)
if use_batch:
batch_parameter_file = './config/batching.conf'
cmd = cmd + ' --enable_batching=true --batching_parameters_file={}'.format(batch_parameter_file)
self.serving_cmds = []
self.serving_cmds.append(cmd)
print(self.serving_cmds)
system_env = os.environ.copy()
self.process = subprocess.Popen(self.serving_cmds, env=system_env, shell=True)
## start grpc
self.grpc_channel = grpc.insecure_channel('127.0.0.1:{}'.format(grpc_port-1), options=options)
self.grpc_stub = model_service_pb2_grpc.ModelServiceStub(self.grpc_channel)
# wait for start tf serving
time.sleep(3)
# reload model
model_server_config = model_server_config_pb2.ModelServerConfig()
config_list = model_server_config_pb2.ModelConfigList()
model_config_json_file = './config/model.json'
with open(model_config_json_file, 'r') as fp:
model_configs = json.load(fp)
model_dir = model_configs['model_dir']
model_list = model_configs['models']
for model_info in model_list:
print(model_info)
model_config = model_server_config_pb2.ModelConfig()
model_config.name = model_info['name']
model_config.base_path = os.path.abspath(os.path.join(model_dir, model_info['path']))
model_config.model_platform = 'tensorflow'
config_list.config.append(model_config)
model_server_config.model_config_list.CopyFrom(config_list)
request = model_management_pb2.ReloadConfigRequest()
request.config.CopyFrom(model_server_config)
grpc_response = self.grpc_stub.HandleReloadConfigRequest(request, 30)
print(grpc_port)
return
def run():
channel = grpc.insecure_channel('192.168.199.198:8500')
stub = model_service_pb2_grpc.ModelServiceStub(channel)
# message ReloadConfigRequest
request = model_management_pb2.ReloadConfigRequest()
model_server_config = model_server_config_pb2.ModelServerConfig()
# message ModelConfigList
config_list = model_server_config_pb2.ModelConfigList()
print(config_list)
####try to add
one_config = config_list.config.add()
one_config.name = "saved_model_half1"
one_config.base_path = "/models/saved_model_half_plus_two_cpu"
one_config.model_platform = "tensorflow"
one_config = config_list.config.add()
one_config.name = "saved_model_half2"
one_config.base_path = "/models/saved_model_half_plus_two_cpu"
one_config.model_platform = "tensorflow"
one_config = config_list.config.add()
one_config.name = "saved_model_half3"
one_config.base_path = "/models/saved_model_half_plus_two_cpu"
one_config.model_platform = "tensorflow"
one_config = config_list.config.add()
one_config.name = "saved_model_half4"
one_config.base_path = "/models/saved_model_half_plus_two_cpu"
one_config.model_platform = "tensorflow"
# one_config = config_list.config.add()
# one_config.name = "saved_model_half5"
# one_config.base_path = "/models/saved_model_half_plus_two_cpu_bak"
# one_config.model_platform = "tensorflow"
model_server_config.model_config_list.CopyFrom(config_list) # one of
request.config.CopyFrom(model_server_config)
print(request.IsInitialized())
print(request.ListFields())
response = stub.HandleReloadConfigRequest(request, 10)
if response.status.error_code == 0:
print("reload sucessfully")
else:
print("reload error!")
print(response.status.error_code)
print(response.status.error_message)
def main(args):
# apply logging config
logging.basicConfig(format='[%(asctime)s] %(levelname)s %(message)s',
level=args.log_level)
# create channel and stub
channel = grpc.insecure_channel('{}:{}'.format(args.tf_host, args.tf_port))
stub = model_service_pb2_grpc.ModelServiceStub(channel)
# register tf_serving exporter
REGISTRY.register(TFServingExporter(stub, args.model_name, args.timeout))
start_http_server(args.port)
logging.info('Server started on port:{}'.format(args.port))
while True:
time.sleep(1)
def main(_):
channel = grpc.insecure_channel(':8500')
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest()
model_server_config = model_server_config_pb2.ModelServerConfig()
_, _, _, _, _, model_configs_text = build_from_config(os.getcwd())
text_format.Parse(model_configs_text, model_server_config)
request.config.CopyFrom(model_server_config)
responese = stub.HandleReloadConfigRequest(request, 10)
if responese.status.error_code == 0:
print("successful update model")
else:
print("fail")
print(responese.status.error_code)
print(responese.status.error_message)
def health_check(self, name, signature_name, version=None):
"""
"""
request = get_model_status_pb2.GetModelStatusRequest()
request.model_spec.name = name
request.model_spec.signature_name = signature_name
if version:
request.model_spec.version.value = version
stub = model_service_pb2_grpc.ModelServiceStub(self.channel)
try:
response = stub.GetModelStatus(request, 10)
if len(response.model_version_status) > 0:
return True
except Exception as err:
logging.exception(err)
return False
def testGetModelStatus(self):
"""Test ModelService.GetModelStatus implementation."""
model_path = self._GetSavedModelBundlePath()
model_server_address = TensorflowModelServerTest.RunServer(
'default', model_path)[1]
print('Sending GetModelStatus request...')
# Send request
request = get_model_status_pb2.GetModelStatusRequest()
request.model_spec.name = 'default'
channel = grpc.insecure_channel(model_server_address)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
result = stub.GetModelStatus(request, RPC_TIMEOUT) # 5 secs timeout
# Verify response
self.assertEqual(1, len(result.model_version_status))
self.assertEqual(123, result.model_version_status[0].version)
# OK error code (0) indicates no error occurred
self.assertEqual(0, result.model_version_status[0].status.error_code)
def main(_):
channel = grpc.insecure_channel(FLAGS.address)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
if MODE.STATUS == FLAGS.mode:
request = get_model_status_pb2.GetModelStatusRequest()
request.model_spec.name = 'pascal'
request.model_spec.signature_name = 'serving_default'
result = stub.GetModelStatus(request)
elif MODE.CONFIG == FLAGS.mode:
request = model_management_pb2.ReloadConfigRequest()
config = request.config.model_config_list.config.add()
config.name = 'detection'
config.base_path = '/models/detection/detection'
config.model_platform = 'tensorflow'
config2 = request.config.model_config_list.config.add()
config2.name = 'pascal'
config2.base_path = '/models/detection/pascal'
config2.model_platform = 'tensorflow'
result = stub.HandleReloadConfigRequest(request)
print(result)
def add_model_config(host, name):
channel = grpc.insecure_channel(host)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest()
model_server_config = model_server_config_pb2.ModelServerConfig()
config_list = model_server_config_pb2.ModelConfigList()
one_config = config_list.config.add()
one_config.name = name
one_config.base_path = os.path.join('/models', name)
one_config.model_platform = 'tensorflow'
model_server_config.model_config_list.CopyFrom(config_list)
request.config.CopyFrom(model_server_config)
response = stub.HandleReloadConfigRequest(request, 10)
if response.status.error_code == 0:
print('Reload successful')
else:
print('Reload failed: {}: {}'.format(response.status.error_code,
response.status.error_message))
def prepare_stub_and_request(address,
model_name,
model_version=None,
creds=None,
opts=None,
request_type=INFERENCE_REQUEST):
if opts is not None:
opts = (('grpc.ssl_target_name_override', opts), )
if creds is not None:
channel = grpc.secure_channel(address, creds, options=opts)
else:
channel = grpc.insecure_channel(address, options=opts)
request = None
stub = None
if request_type == MODEL_STATUS_REQUEST:
request = get_model_status_pb2.GetModelStatusRequest()
stub = model_service_pb2_grpc.ModelServiceStub(channel)
elif request_type == INFERENCE_REQUEST:
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = model_name
if model_version is not None:
request.model_spec.version.value = model_version
return stub, request
def service_channel(cls):
return model_service_pb2_grpc.ModelServiceStub(cls.channel)
def __init__(self, port, host='0.0.0.0'):
self.channel = grpc.insecure_channel('{}:{}'.format(host, port))
self.stub = model_service_pb2_grpc.ModelServiceStub(self.channel)
required=False,
default=9000,
help='Specify port to grpc service. default: 9000')
parser.add_argument('--model_name',
default='resnet',
help='Model name to query. default: resnet',
dest='model_name')
parser.add_argument(
'--model_version',
type=int,
help='Model version to query. Lists all versions if omitted',
dest='model_version')
args = vars(parser.parse_args())
channel = grpc.insecure_channel("{}:{}".format(args['grpc_address'],
args['grpc_port']))
stub = model_service_pb2_grpc.ModelServiceStub(channel)
print('Getting model status for model:', args.get('model_name'))
request = get_model_status_pb2.GetModelStatusRequest()
request.model_spec.name = args.get('model_name')
if args.get('model_version') is not None:
request.model_spec.version.value = args.get('model_version')
result = stub.GetModelStatus(
request, 10.0) # result includes a dictionary with all model outputs
print_status_response(response=result)
def __init__(self, address):
self.address = address
self.model_platform = "tensorflow"
self.channel = grpc.insecure_channel(self.address)
self.stub = model_service_pb2_grpc.ModelServiceStub(self.channel)
self.timeout = 600 # gRPC timeout in seconds
def __init__(self, server):
super().__init__(server)
self.stub = model_service_pb2_grpc.ModelServiceStub(self.channel)
def getConfigurations():
channel = grpc.insecure_channel(const.host + ":" + const.port)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
model_server_config = model_server_config_pb2.ModelServerConfig()
return model_server_config
def create_channel_for_model_ver_pol_server_status():
channel = grpc.insecure_channel('localhost:9006')
stub = model_service_pb2_grpc.ModelServiceStub(channel)
return stub
def create_channel_for_update_flow_specific_status():
channel = grpc.insecure_channel('localhost:9008')
stub = model_service_pb2_grpc.ModelServiceStub(channel)
return stub
