# 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" (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 an optional argument that allows changing the layout of model input and output tensors. Only NCHW and NHWC layouts are supported. When specified with a single string value - layout change is only applied to single model input. To change multiple model inputs or outputs, you can specify JSON object with mapping, such as: {"input1":"NHWC","input2":"NHWC","output1":"NHWC"} .If not specified, the layout is inherited from the model.

"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: {"CPU_THROUGHPUT_STREAMS": "CPU_THROUGHPUT_AUTO"}

"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"/"HDDL"/"GPU"/"MYRIAD"/"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.

### Batch Processing in OpenVINO Model Server¶

• batch_size parameter is optional. By default, the batch size is derived from the model. It is set by the Model Optimizer tool.

• When the parameter is set to a numerical value, it is changing the model batch size at service startup. It also accepts a value auto - this makes the served model set the batch size automatically based on the incoming data at runtime.

• Each time the input data change the batch size, the model is reloaded. It might have an extra response delay for the first request. This feature is useful for sequential inference requests of the same batch size.

NOTE : In case of frequent batch size changes in predict requests, consider using demultiplexing feature from Directed Acyclic Graph Scheduler which is more performant in such situations because it is not adding extra overhead with model reloading between requests like batch_size auto setting. Examplary usage of this feature can be found in dynamic_batch_size document.

• OpenVINO Model Server determines the batch size based on the size of the first dimension in the first input. For example with the input shape (1, 3, 225, 225), the batch size is set to 1. With input shape (8, 3, 225, 225) the batch size is set to 8.

Note: Some models like object detection do not work correctly with batch size changed with batch_size parameter. Typically those are the models, whose output’s first dimension is not representing the batch size like on the input side. Changing batch size in this kind of models can be done with network reshaping by setting shape parameter appropriately.

### Model reshaping in OpenVINO Model Server¶

• shape is an optional parameter that takes precedence over the batch_size parameter. When the shape is defined as an argument, it ignores the batch_size value.

• The shape argument can change the model enabled in the Model Server to fit the required parameters. It accepts 3 forms of the values:

• “auto” phrase - model server will be reloading the model with the shape matching the input data matrix.

• a tuple e.g. (1,3,224,224) - it defines the shape to be used for all incoming requests for models with a single input

• a dictionary of tuples e.g. {input1:(1,3,224,224),input2:(1,3,50,50)} - it defines a shape of every included input in the model

Note: Some models do not support reshape operation. Learn more about supported model graph layers including all limitations on Shape Inference Document. In case the model can’t be reshaped, it will remain in the original parameters and all requests with incompatible input formats will get an error. The model server will also report such problems in the logs.

### Changing model input/output layout¶

OpenVINO models which process image data are generated via the model optimizer with NCHW layout. Image transformation libraries like OpenCV or Pillow use NHWC layout. This makes it required to transpose the data in the client application before it can be sent to OVMS. Custom node example implementations internally also use NHWC format to perform image transformations. Transposition operations increase the overall processing latency. The layout parameter reduces the latency by changing the model in runtime to accept NHWC layout instead of NCHW. That way the whole processing cycle is more effective by avoiding unnecessary data transpositions. That is especially beneficial for models with high-resolution images, where data transposition could be more expensive in processing.

The layout parameter is optional. By default, layout is inherited from the OpenVINO™ model. You can specify layout during conversion to IR format via Model Optimizer. You can also use this parameter for ONNX models.

Layout change is only supported to NCHW or NHWC. You can specify 2 forms of values:

• string - either NCHW or NHWC; applicable only for models with a single input tensor

• dictionary of strings - e.g. {"input1":"NHWC", "input2":"NCHW", "output1":"NHWC"}; allows to specify layout for multiple inputs and outputs by name.

After the model layout is changed, the requests must match the new updated shape in order NHWC instead of NCHW. For NCHW inputs it should be: (batch, channels, height, width) but for NHWC this is: (batch, height, width, channels).

Changing layout is not supported for models with input names the same as output names. For the model included in DAG, layouts of subsequent nodes must match similarly to network shape and precision.

## 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 .

cpu_extension

string

Optional path to a library with custom layers implementation (preview feature in OVMS).

log_level

"DEBUG"/"INFO"/"ERROR"

Serving logging level

log_path

string

Optional path to the log file.