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Deployment Config

model_navigator.api.pytriton.ModelConfig dataclass 

ModelConfig(batching=True, max_batch_size=4, batcher=DynamicBatcher(), response_cache=False, decoupled=False)

Additional model configuration for running model through Triton Inference Server.

Parameters:

  • batching (bool, default: True ) –

    Flag to enable/disable batching for model.

  • max_batch_size (int, default: 4 ) –

    The maximal batch size that would be handled by model.

  • batcher (DynamicBatcher, default: DynamicBatcher() ) –

    Configuration of Dynamic Batching for the model.

  • response_cache (bool, default: False ) –

    Flag to enable/disable response cache for the model

  • decoupled (bool, default: False ) –

    Flag to enable/disable decoupled transaction policy

model_navigator.api.pytriton.DynamicBatcher dataclass 

DynamicBatcher(max_queue_delay_microseconds=0, preferred_batch_size=None, preserve_ordering=False, priority_levels=0, default_priority_level=0, default_queue_policy=None, priority_queue_policy=None)

Dynamic batcher configuration.

More in Triton Inference Server documentation

Parameters:

  • max_queue_delay_microseconds (int, default: 0 ) –

    The maximum time, in microseconds, a request will be delayed in the scheduling queue to wait for additional requests for batching.

  • preferred_batch_size (Optional[list], default: None ) –

    Preferred batch sizes for dynamic batching.

  • preserve_ordering (bool, default: False ) –

    Should the dynamic batcher preserve the ordering of responses to match the order of requests received by the scheduler.

  • priority_levels (int, default: 0 ) –

    The number of priority levels to be enabled for the model.

  • default_priority_level (int, default: 0 ) –

    The priority level used for requests that don't specify their priority.

  • default_queue_policy (Optional[QueuePolicy], default: None ) –

    The default queue policy used for requests.

  • priority_queue_policy (Optional[Dict[int, QueuePolicy]], default: None ) –

    Specify the queue policy for the priority level.

model_navigator.api.pytriton.Tensor dataclass 

Tensor(shape, dtype, name=None, optional=False)

Model input and output definition for Triton deployment.

Parameters:

  • shape (tuple) –

    Shape of the input/output tensor.

  • dtype (Union[dtype, Type[dtype], Type[object]]) –

    Data type of the input/output tensor.

  • name (Optional[str], default: None ) –

    Name of the input/output of model.

  • optional (Optional[bool], default: False ) –

    Flag to mark if input is optional.

model_navigator.api.pytriton.QueuePolicy dataclass 

QueuePolicy(timeout_action=TimeoutAction.REJECT, default_timeout_microseconds=0, allow_timeout_override=False, max_queue_size=0)

Model queue policy configuration.

More in Triton Inference Server documentation

Parameters:

  • timeout_action (TimeoutAction, default: REJECT ) –

    The action applied to timed-out request.

  • default_timeout_microseconds (int, default: 0 ) –

    The default timeout for every request, in microseconds.

  • allow_timeout_override (bool, default: False ) –

    Whether individual request can override the default timeout value.

  • max_queue_size (int, default: 0 ) –

    The maximum queue size for holding requests.

model_navigator.api.pytriton.TimeoutAction

Bases: Enum

Timeout action definition for timeout_action QueuePolicy field.

Parameters:

  • REJECT (str) –

    Reject the request and return error message accordingly.

  • DELAY (str) –

    Delay the request until all other requests at the same (or higher) priority levels that have not reached their timeouts are processed.