TensorRT

model_navigator.api.config.TensorRTPrecision

Bases: Enum

Precisions supported during TensorRT conversions.

model_navigator.api.config.TensorRTPrecisionMode

Bases: Enum

Precision modes for TensorRT conversions.

model_navigator.api.config.TensorRTConfig dataclass

Bases: CustomConfigForFormat

TensorRT custom config used for TensorRT conversion.

Parameters:

Name	Type	Description	Default
precision	Union[Union[str, TensorRTPrecision], Tuple[Union[str, TensorRTPrecision], ...]]	TensorRT precision.	DEFAULT_TENSORRT_PRECISION
max_workspace_size	Optional[int]	Max workspace size used by converter.	DEFAULT_MAX_WORKSPACE_SIZE
trt_profile	Optional[TensorRTProfile]	TensorRT profile.	None

format: Format property

Format represented by CustomConfig.

Returns:

Type	Description
Format	TensorRTConfig format

__post_init__()

Parse dataclass enums.

Source code in model_navigator/api/config.py

def __post_init__(self) -> None:
    """Parse dataclass enums."""
    self.precision_mode = TensorRTPrecisionMode(self.precision_mode)
    precision = (self.precision,) if not isinstance(self.precision, (list, tuple)) else self.precision
    self.precision = tuple(TensorRTPrecision(p) for p in precision)

defaults()

Update parameters to defaults.

Source code in model_navigator/api/config.py

def defaults(self) -> None:
    """Update parameters to defaults."""
    self.precision = tuple(TensorRTPrecision(p) for p in DEFAULT_TENSORRT_PRECISION)
    self.precision_mode = DEFAULT_TENSORRT_PRECISION_MODE
    self.trt_profile = None
    self.max_workspace_size = DEFAULT_MAX_WORKSPACE_SIZE

from_dict(config_dict) classmethod

Instantiate TensorRTConfig from adictionary.

Source code in model_navigator/api/config.py

@classmethod
def from_dict(cls, config_dict: Dict[str, Any]) -> "TensorRTConfig":
    """Instantiate TensorRTConfig from  adictionary."""
    if "trt_profile" in config_dict and not isinstance(config_dict["trt_profile"], TensorRTProfile):
        config_dict["trt_profile"] = TensorRTProfile.from_dict(config_dict["trt_profile"])
    return cls(**config_dict)

name() classmethod

Name of the config.

Source code in model_navigator/api/config.py

@classmethod
def name(cls) -> str:
    """Name of the config."""
    return "TensorRT"