Sequence Batcher

model_navigator.api.triton.SequenceBatcher dataclass

Sequence batching configuration.

Read more in Triton Inference server model configuration

Parameters:

• strategy (Optional[Union[SequenceBatcherStrategyDirect, SequenceBatcherStrategyOldest]], default: None ) –

  The strategy used by the sequence batcher.

• max_sequence_idle_microseconds (Optional[int], default: None ) –

  The maximum time, in microseconds, that a sequence is allowed to be idle before it is aborted.

• control_inputs (List[SequenceBatcherControlInput], default: dataclasses.field(default_factory=lambda : []) ) –

  The model input(s) that the server should use to communicate sequence start, stop, ready and similar control values to the model.

• states (List[SequenceBatcherState], default: dataclasses.field(default_factory=lambda : []) ) –

  The optional state that can be stored in Triton for performing inference requests on a sequence.

model_navigator.api.triton.SequenceBatcherControl dataclass

Sequence Batching control configuration.

Read more in Triton Inference server model configuration

Parameters:

• kind (SequenceBatcherControlKind) –

  The kind of this control.

• dtype (Optional[Union[dtype, Type[numpy.dtype]]], default: None ) –

  The control's datatype.

• int32_false_true (List[int], default: dataclasses.field(default_factory=lambda : []) ) –

  The control's true and false setting is indicated by setting a value in an int32 tensor.

• fp32_false_true (List[float], default: dataclasses.field(default_factory=lambda : []) ) –

  The control's true and false setting is indicated by setting a value in a fp32 tensor.

• bool_false_true (List[bool], default: dataclasses.field(default_factory=lambda : []) ) –

  The control's true and false setting is indicated by setting a value in a bool tensor.

model_navigator.api.triton.SequenceBatcherControlInput dataclass

Sequence Batching control input configuration.

Read more in Triton Inference server model configuration

Parameters:

• input_name (str) –

  The name of the model input.

• controls (List[SequenceBatcherControl]) –

  List of control value(s) that should be communicated to the model using this model input.

model_navigator.api.triton.SequenceBatcherControlKind

Bases: Enum

Sequence Batching control options.

Read more in Triton Inference server model configuration

Parameters:

• CONTROL_SEQUENCE_START –

  "CONTROL_SEQUENCE_START"

• CONTROL_SEQUENCE_READY –

  "CONTROL_SEQUENCE_READY"

• CONTROL_SEQUENCE_END –

  "CONTROL_SEQUENCE_END"

• CONTROL_SEQUENCE_CORRID –

  "CONTROL_SEQUENCE_CORRID"

model_navigator.api.triton.SequenceBatcherInitialState dataclass

Sequence Batching initial state configuration.

Read more in Triton Inference server model configuration

Parameters:

• name (str) –
• shape (Tuple[int, ...]) –

  The shape of the state tensor, not including the batch dimension.

• dtype (Optional[Union[dtype, Type[numpy.dtype]]], default: None ) –

  The data-type of the state.

• zero_data (Optional[bool], default: None ) –

  The identifier for using zeros as initial state data.

• data_file (Optional[pathlib.Path], default: None ) –

  The file whose content will be used as the initial data for the state in row-major order.

model_navigator.api.triton.SequenceBatcherState dataclass

Sequence Batching state configuration.

Read more in Triton Inference server model configuration

Parameters:

• input_name (str) –

  The name of the model state input.

• output_name (str) –

  The name of the model state output.

• dtype (Union[dtype, Type[numpy.dtype]]) –

  The data-type of the state.

• shape (Tuple[int, ...]) –

  The shape of the state tensor.

• initial_states (List[SequenceBatcherInitialState], default: dataclasses.field(default_factory=lambda : []) ) –

  The optional field to specify the list of initial states for the model.

model_navigator.api.triton.SequenceBatcherStrategyDirect dataclass

Sequence Batching strategy direct configuration.

Read more in Triton Inference server model configuration

Parameters:

• max_queue_delay_microseconds (int, default: 0 ) –

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

• minimum_slot_utilization (float, default: 0.0 ) –

  The minimum slot utilization that must be satisfied to execute the batch before 'max_queue_delay_microseconds' expires.

model_navigator.api.triton.SequenceBatcherStrategyOldest dataclass

Sequence Batching strategy oldest configuration.

Read more in Triton Inference server model configuration

Parameters:

• max_candidate_sequences (int) –

  Maximum number of candidate sequences that the batcher maintains.

• preferred_batch_size (List[int], default: dataclasses.field(default_factory=lambda : []) ) –

  Preferred batch sizes for dynamic batching of candidate sequences.

• max_queue_delay_microseconds (int, default: 0 ) –

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