Model Server Parameters

Model Configuration Options

Option	Value format	Description
"model_name"/"name"	string	Model name exposed over gRPC and REST API.(use model_name in command line, name in json config)
"model_path"/"base_path"	string	If using a Google Cloud Storage, Azure Storage or S3 path, see cloud storage guide. The path may look as follows: "/opt/ml/models/model" "gs://bucket/models/model" "s3://bucket/models/model" "azure://bucket/models/model" The path can be also relative to the config.json location (use model_path in command line, base_path in json config)
"shape"	tuple/json/"auto"	shape is optional and takes precedence over batch_size. The shape argument changes the model that is enabled in the model server to fit the parameters. shape accepts three forms of the values: * auto - The model server reloads the model with the shape that matches the input data matrix. * a tuple, such as (1,3,224,224) - The tuple defines the shape to use for all incoming requests for models with a single input. * A dictionary of shapes, such as {"input1":"(1,3,224,224)","input2":"(1,3,50,50)", "input3":"auto"} - This option defines the shape of every included input in the model.Some models don’t support the reshape operation.If the model can’t be reshaped, it remains in the original parameters and all requests with incompatible input format result in an error. See the logs for more information about specific errors.Learn more about supported model graph layers including all limitations at Shape Inference Document.
"batch_size"	integer/"auto"	Optional. By default, the batch size is derived from the model, defined through the OpenVINO Model Optimizer. batch_size is useful for sequential inference requests of the same batch size.Some models, such as object detection, don’t work correctly with the batch_size parameter. With these models, the output’s first dimension doesn’t represent the batch size. You can set the batch size for these models by using network reshaping and setting the shape parameter appropriately.The default option of using the Model Optimizer to determine the batch size uses the size of the first dimension in the first input for the size. For example, if the input shape is (1, 3, 225, 225), the batch size is set to 1. If you set batch_size to a numerical value, the model batch size is changed when the service starts.batch_size also accepts a value of auto. If you use auto, then the served model batch size is set according to the incoming data at run time. The model is reloaded each time the input data changes the batch size. You might see a delayed response upon the first request.
"layout"	json/string	layout is optional argument which allows to define or change the layout of model input and output tensors. To change the layout (add the transposition step), specify <target layout>:<source layout>. Example: NHWC:NCHW means that user will send input data in NHWC layout while the model is in NCHW layout. When specified without colon separator, it doesn’t add a transposition but can determine the batch dimension. E.g. --layout CN makes prediction service treat second dimension as batch size. When the model has multiple inputs or the output layout has to be changed, use a json format. Set the mapping, such as: {"input1":"NHWC:NCHW","input2":"HWN:NHW","output1":"CN:NC"}. If not specified, layout is inherited from model. Read more
"model_version_policy"	json/string	Optional. The model version policy lets you decide which versions of a model that the OpenVINO Model Server is to serve. By default, the server serves the latest version. One reason to use this argument is to control the server memory consumption.The accepted format is in json or string. Examples: {"latest": { "num_versions":2 } {"specific": { "versions":[1, 3] } } {"all": {} }
"plugin_config"	json/string	List of device plugin parameters. For full list refer to OpenVINO documentation and performance tuning guide. Example: {"PERFORMANCE_HINT": "LATENCY"}
"nireq"	integer	The size of internal request queue. When set to 0 or no value is set value is calculated automatically based on available resources.
"target_device"	string	Device name to be used to execute inference operations. Accepted values are: "CPU"/"GPU"/"MULTI"/"HETERO"
"stateful"	bool	If set to true, model is loaded as stateful.
"idle_sequence_cleanup"	bool	If set to true, model will be subject to periodic sequence cleaner scans. See idle sequence cleanup.
"max_sequence_number"	uint32	Determines how many sequences can be handled concurrently by a model instance.
"low_latency_transformation"	bool	If set to true, model server will apply low latency transformation on model load.
"metrics_enable"	bool	Flag enabling metrics endpoint on rest_port.
"metrics_list"	string	Comma separated list of metrics. If unset, only default metrics will be enabled.

Note : Specifying config_path is mutually exclusive with putting model parameters in the CLI (serving multiple models).

Option	Value format	Description
config_path	string	Absolute path to json configuration file

Server configuration options

Configuration options for the server are defined only via command-line options and determine configuration common for all served models.

Option	Value format	Description
port	integer	Number of the port used by gRPC sever.
rest_port	integer	Number of the port used by HTTP server (if not provided or set to 0, HTTP server will not be launched).
grpc_bind_address	string	Network interface address or a hostname, to which gRPC server will bind to. Default: all interfaces: 0.0.0.0
rest_bind_address	string	Network interface address or a hostname, to which REST server will bind to. Default: all interfaces: 0.0.0.0
grpc_workers	integer	Number of the gRPC server instances (must be from 1 to CPU core count). Default value is 1 and it’s optimal for most use cases. Consider setting higher value while expecting heavy load.
rest_workers	integer	Number of HTTP server threads. Effective when rest_port > 0. Default value is set based on the number of CPUs.
file_system_poll_wait_seconds	integer	Time interval between config and model versions changes detection in seconds. Default value is 1. Zero value disables changes monitoring.
sequence_cleaner_poll_wait_minutes	integer	Time interval (in minutes) between next sequence cleaner scans. Sequences of the models that are subjects to idle sequence cleanup that have been inactive since the last scan are removed. Zero value disables sequence cleaner. See idle sequence cleanup. It also sets the schedule for releasing free memory from the heap.
custom_node_resources_cleaner_interval_seconds	integer	Time interval (in seconds) between two consecutive resources cleanup scans. Default is 1. Must be greater than 0. See custom node development.
cpu_extension	string	Optional path to a library with custom layers implementation.
log_level	"DEBUG"/"INFO"/"ERROR"	Serving logging level
log_path	string	Optional path to the log file.
cache_dir	string	Path to the model cache storage. Caching will be enabled if this parameter is defined or the default path /opt/cache exists
grpc_channel_arguments	string	A comma separated list of arguments to be passed to the grpc server. (e.g. grpc.max_connection_age_ms=2000)
grpc_max_threads	string	Maximum number of threads which can be used by the grpc server. Default value depends on number of CPUs.
grpc_memory_quota	string	GRPC server buffer memory quota. Default value set to 2147483648 (2GB).
help	NA	Shows help message and exit
version	NA	Shows binary version